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Neuro? Logical! Forum for all neuro-things => from neuron to brain...

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Old 03-10-2015, 11:31 AM   #1
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Default Controllability of structural brain networks

http://www.nature.com/ncomms/2015/15...comms9414.html

Abstract
Abstract• Introduction• Results• Discussion• Methods• Additional information• References• Acknowledgements• Author information• Supplementary information
Quote:
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.
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Old 27-01-2016, 09:56 PM   #2
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Default Human brain networks function in connectome-specific harmonic waves

http://www.nature.com/ncomms/2016/16...omms10340.html

Abstract
Abstract• Introduction• Results• Discussion• Methods• Additional information• References• Acknowledgements• Author information• Supplementary information
Quote:
A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call ‘connectome harmonics’, oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory–inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation–inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.
Quote:
A characteristic feature of cortical dynamics in mammals is the emergence of behaviour-dependent oscillatory networks spanning five orders of magnitude in the frequency domain4. Recently, strong temporal correlation within widely distributed cortical regions has also been discovered in spontaneous slow (<0.1 Hz) fluctuations of the blood oxygen level-dependent signal measured with functional magnetic resonance imaging (fMRI). This discovery revealed that spontaneous activity, in the absence of any external stimuli or task condition, also exhibits highly structured correlation patterns throughout the brain. Remarkably, the topography of these correlation patterns, termed the resting state networks (RSNs)5, 6, closely resembles the functional networks of the human brain identified by various sensory, motor and cognitive paradigms6, 7 and have been found to relate to electroencephalography microstates, global brain states occurring in discrete epochs of about 100 ms (refs 8, 9).
Quote:
Our description of the universal harmonics implied by the graph Laplacian in terms of diffusion rests on the undirected nature of the structural connectome (represented by a symmetric adjacency matrix). However, we know that reciprocal forward and backward connections show strong asymmetries in the human brain, rendering the conceptual link between the (directed) effective connectivity and diffusion not always valid. Having said this, there is no reason why one cannot pursue modelling and simulation using the eigenmodes of directed effective connectivity matrices28.
Quote:
In summary, in this work we introduce a new connectome-specific representation of cortical activity patterns and dynamics, which extends the Fourier basis to the structural connectivity of the thalamo-cortical system. Remarkably, when expressed in this new analytic language, RSNs of the human brain overlap with the connectome harmonic patterns of certain frequencies. We demonstrate the self-organization of these connectome-specific harmonics patterns from the interplay of neural excitation and inhibition in coupled dynamical systems as described by neural field models. Interestingly, due to the emergence of these harmonic patterns in various natural phenomena, ranging from acoustic vibrations, electromagnetic interactions and electron wave functions to morphogenesis, it is tempting to suppose that human brain activity might also be governed by the same underlying principles as other natural phenomena.

This is what lies beneath.

It references the work of Laplace, Schrodinger, Alan Turing's work on biological pattern formation, the Wilson-Cowan equations and Karl Friston's nodes and modes.
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Old 05-02-2016, 12:01 AM   #3
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Default MATHEMATICALLY MODELLING HOW THE BRAIN MAKES COMPLEX DECISIONS

http://neurosciencenews.com/decision...th-model-3578/

Quote:
“A goal-based decision is much more complicated from a neurological point of view, because there are so many more variables – it involves exploring a branching set of possible future situations,” said the paper’s first author Dr Johannes Friedrich of Columbia University, who conducted the work while a postdoctoral researcher in Cambridge’s Department of Engineering. “If you think about a detour on your daily commute, you need to make a separate decision each time you reach an intersection.”

Habit-based decisions have been thoroughly studied by neuroscientists and are fairly well-understood in terms of how they work at a neural level. The mechanisms behind goal-based decisions however, remain elusive.
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Old 18-02-2016, 10:21 PM   #4
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Default A role of phase-resetting in coordinating large scale neural oscillations during attention and goal-directed behavior

http://journal.frontiersin.org/artic...00018/abstract

Quote:
Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets (1) set a “neural context” in terms of narrow band frequencies that uniquely characterizes the activated circuits, (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances, (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations, and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior.
Keywords: oscillations, Phase reset, cross frequency coupling, coding, inter-areal coordination, theta, alpha, gamma




Time-compressed preplay of anticipated events in human primary visual cortex

https://www.nature.com/articles/ncomms15276

Abstract
Quote:
Perception is guided by the anticipation of future events. It has been hypothesized that this process may be implemented by pattern completion in early visual cortex, in which a stimulus sequence is recreated after only a subset of the visual input is provided. Here we test this hypothesis using ultra-fast functional magnetic resonance imaging to measure BOLD activity at precisely defined receptive field locations in visual cortex (V1) of human volunteers. We find that after familiarizing subjects with a spatial sequence, flashing only the starting point of the sequence triggers an activity wave in V1 that resembles the full stimulus sequence. This preplay activity is temporally compressed compared to the actual stimulus sequence and remains present even when attention is diverted from the stimulus sequence. Preplay might therefore constitute an automatic prediction mechanism for temporal sequences in V1.
Introduction
Quote:
The visual system is predictive in nature, anticipating relevant events to facilitate sensory processing and decision-making1. Prediction in perception has often been studied in static contexts, where a stimulus is expected because the base rate of occurrence is higher2 or because of statistical associations between stimuli3. These forms of prediction can be neurally implemented by pre-activating sensory representations of the expected events4,5,6.

However, real-world predictions are typically dynamic: for example, we predict the trajectory of a ball moving towards us or whether a car will hit us if we cross the road. Implementing this kind of dynamic prediction is more complex, as it requires an anticipatory wave of visual responses that is both spatially and temporally precise. Recently, such waves of preplay activity have been observed in the visual cortical system of rats7 and monkeys8, but the existence, function and potential source of preplay waves in humans however remain unknown.

Here we tested whether human primary visual cortex (V1) is engaged in dynamic prediction by preplaying anticipated visual events. We characterized neural activity in the primary visual cortex at both high spatial and temporal resolution, by combining ultra-fast functional magnetic resonance imaging (fMRI, using a volume acquisition time (TR) of 88 ms) and population receptive field (pRF) mapping9 to identify retinotopically specific responses with high temporal resolution.
Update 31/05/2017
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Old 18-02-2016, 10:31 PM   #5
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Default Stimuli Reduce the Dimensionality of Cortical Activity

http://journal.frontiersin.org/artic...016.00011/full

Quote:
The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.
Quote:
Understanding the dynamics of neural activity and how it is generated in cortical circuits is a fundamental question in Neuroscience.
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Old 12-03-2016, 10:55 PM   #6
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Default On the character of consciousness

http://journal.frontiersin.org/artic...00027/abstract

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The human brain is a particularly demanding system to infer its nature from observations. Thus, there is on one hand plenty of room for theorizing and on the other hand a pressing need for a rigorous theory. We apply statistical mechanics of open systems to describe the brain as a hierarchical system in consuming free energy in least time. This holistic tenet accounts for cellular metabolism, neuronal signaling, cognitive processes all together or any other process by a formal equation of motion that extends down to the ultimate precision of one quantum of action. According to this general thermodynamic theory cognitive processes are no different by their operational and organizational principle from other natural processes. Cognition too will emerge and evolve along path-dependent and non-determinate trajectories by consuming free energy in least time to attain thermodynamic balance within the nervous system itself and with its surrounding systems. Specifically, consciousness can be ascribed to a natural process that integrates various neural networks for coherent consumption of free energy, i.e., for meaningful deeds. The whole hierarchy of integrated systems can be formally summed up to thermodynamic entropy. The holistic tenet provides insight to the character of consciousness also by acknowledging awareness in other systems at other levels of nature’s hierarchy.


Who Am I: The Conscious and the Unconscious Self
http://journal.frontiersin.org/artic...017.00126/full

Quote:
Who am I? What is the self and where does it come from? This may be one of the oldest problems in philosophy. Beyond traditional philosophy, only very recently approaches from neuroscience (in particular imaging studies) have tried to address these questions, too. So what are neural substrates of our self? An increasing body of evidence has demonstrated that a set of structures labeled as cortical midline structures are fundamental components to generate a conscious self. Moreover, recent theories on embodied cognition propose that this conscious self might be supplemented by additional structures, for example, in the somatosensory cortices, which enable our brain to create an “embodied mind”. While the self based on cortical midline structures may be related to a conscious self, we here propose that the embodied facet of the self may be linked to something we call unconscious self. In this article we describe problems of this model of a conscious and unconscious self and discuss possible solutions from a theoretical point of view.
Who Am I?
Quote:
We know that even in prehistoric times humans tried to open the skulls of their sick conspecifics. Moreover, prehistoric men used human skulls, usually those of ancestors, for religious worship long after death. Thus, the head always seemed to be an object of interest for us. Perhaps the prehistoric men assumed that something inside our skull may be related to our feelings, thoughts and memories. But we had to wait until the French philosopher René Descartes, who was the first one who made the distinction between mind and body very explicit. His famous philosophical statement “Cogito ergo sum” can be translated as “I think, therefore I am”. Hence, he concludes that he can be certain that he exists because he thinks. For many researchers these thoughts mark the beginning of modern western philosophy. Descartes statement raised a lot of questions, in particular about the relationship between body and mind, which are still a matter of discussion today.

This is in particular true since modern neuroscience started to unravel the mystery of the brain. New imaging tools such as fMRI enable us to look at our brain while it is working. These new approaches have opened the door to answer the questions Descartes posed about the relationship between mind and body in a way he never would have imagined.

In this article we suggest the idea that the processing of self-referential stimuli in cortical midline structures may represent an important part of the conscious self, which may be supplemented by an unconscious part of the self that has been called an “embodied mind” (Varela et al., 1991), which relies on other brain structures.
Update 05/04/2017
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Old 13-03-2016, 01:10 AM   #7
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Default Multisensory Tracking of Objects in Darkness: Capture of Positive Afterimages by the Tactile and Proprioceptive Senses

http://journals.plos.org/plosone/art...l.pone.0150714

Abstract

Quote:
This paper reports on three experiments investigating the contribution of different sensory modalities to the tracking of objects moved in total darkness. Participants sitting in the dark were exposed to a brief, bright flash which reliably induced a positive visual afterimage of the scene so illuminated. If the participants subsequently move their hand in the darkness, the visual afterimage of that hand fades or disappears; this is presumably due to conflict between the illusory visual afterimage (of the hand in its original location) and other information (e.g., proprioceptive) from a general mechanism for tracking body parts. This afterimage disappearance effect also occurs for held objects which are moved in the dark, and some have argued that this represents a case of body schema extension, i.e. the rapid incorporation of held external objects into the body schema. We demonstrate that the phenomenon is not limited to held objects and occurs in conditions where incorporation into the body schema is unlikely. Instead, we propose that the disappearance of afterimages of objects moved in darkness comes from a general mechanism for object tracking which integrates input from multiple sensory systems. This mechanism need not be limited to tracking body parts, and thus we need not invoke body schema extension to explain the afterimage disappearance. In this series of experiments, we test whether auditory feedback of object movement can induce afterimage disappearance, demonstrate that the disappearance effect scales with the magnitude of proprioceptive feedback, and show that tactile feedback alone is sufficient for the effect. Together, these data demonstrate that the visual percept of a positive afterimage is constructed not just from visual input of the scene when light reaches the eyes, but in conjunction with input from multiple other senses.
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Old 30-03-2016, 02:43 PM   #8
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Default Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG

http://www.ncbi.nlm.nih.gov/pubmed/26921713

Abstract
Quote:
This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision - inferred by our behavioural DCM - correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia.
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Old 20-04-2016, 07:18 PM   #9
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Default Rapid Adaptation Induces Persistent Biases in Population Codes for Visual Motion

http://www.jneurosci.org/content/36/....abstract?etoc

Abstract

Quote:
Each visual experience changes the neural response to subsequent stimuli. If the brain is unable to incorporate these encoding changes, the decoding, or perception, of subsequent stimuli is biased. Although the phenomenon of adaptation pervades the nervous system, its effects have been studied mainly in isolation, based on neuronal encoding changes induced by an isolated, prolonged stimulus. To understand how adaptation-induced biases arise and persist under continuous, naturalistic stimulation, we simultaneously recorded the responses of up to 61 neurons in the marmoset (Callithrix jacchus) middle temporal area to a sequence of directions that changed every 500 ms. We found that direction-specific adaptation following only 0.5 s of stimulation strongly affected encoding for up to 2 s by reducing both the gain and the spike count correlations between pairs of neurons with preferred directions close to the adapting direction. In addition, smaller changes in bandwidth and preferred direction were observed in some animals. Decoding individual trials of adaptation-affected activity in simultaneously recorded neurons predicted repulsive biases that are consistent with the direction aftereffect. Surprisingly, removing spike count correlations by trial shuffling did not impact decoding performance or bias. When adaptation had the largest effect on encoding, the decoder made the most errors. This suggests that neural and perceptual repulsion is not a mechanism to enhance perceptual performance but is instead a necessary consequence of optimizing neural encoding for the identification of a wide range of stimulus properties in diverse temporal contexts.

SIGNIFICANCE STATEMENT Although perception depends upon decoding the pattern of activity across a neuronal population, the encoding properties of individual neurons are unreliable: a single neuron's response to repetitions of the same stimulus is variable, and depends on both its spatial and temporal context. In this manuscript, we describe the complete cascade of adaptation-induced effects in sensory encoding and show how they predict population decoding errors consistent with perceptual biases. We measure the time course of adaptation-induced changes to the response properties of neurons in isolation, and to the correlation structure across pairs of simultaneously recorded neurons. These results provide novel insight into how and for how long adaptation affects the neural code, particularly during continuous, naturalistic vision.
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Old 20-04-2016, 07:45 PM   #10
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Default The Medial Orbitofrontal Cortex Regulates Sensitivity to Outcome Value

http://www.jneurosci.org/content/36/....abstract?etoc

Abstract

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An essential component of goal-directed decision-making is the ability to maintain flexible responding based on the value of a given reward, or “reinforcer.” The medial orbitofrontal cortex (mOFC), a subregion of the ventromedial prefrontal cortex, is uniquely positioned to regulate this process. We trained mice to nose poke for food reinforcers and then stimulated this region using CaMKII-driven Gs-coupled designer receptors exclusively activated by designer drugs (DREADDs). In other mice, we silenced the neuroplasticity-associated neurotrophin brain-derived neurotrophic factor (BDNF). Activation of Gs-DREADDs increased behavioral sensitivity to reinforcer devaluation, whereas Bdnf knockdown blocked sensitivity. These changes were accompanied by modifications in breakpoint ratios in a progressive ratio task, and they were recapitulated in Bdnf+/− mice. Replacement of BDNF selectively in the mOFC in Bdnf+/− mice rescued behavioral deficiencies, as well as phosphorylation of extracellular-signal regulated kinase 1/2 (ERK1/2). Thus, BDNF expression in the mOFC is both necessary and sufficient for the expression of typical effort allocation relative to an anticipated reinforcer. Additional experiments indicated that expression of the immediate-early gene c-fos was aberrantly elevated in the Bdnf+/− dorsal striatum, and BDNF replacement in the mOFC normalized expression. Also, systemic administration of an MAP kinase kinase inhibitor increased breakpoint ratios, whereas the addition of discrete cues bridging the response–outcome contingency rescued breakpoints in Bdnf+/− mice. We argue that BDNF–ERK1/2 in the mOFC is a key regulator of “online” goal-directed action selection.

SIGNIFICANCE STATEMENT Goal-directed response selection often involves predicting the consequences of one's actions and the value of potential payoffs. Lesions or chemogenetic inactivation of the medial orbitofrontal cortex (mOFC) in rats induces failures in retrieving outcome identity memories (Bradfield et al., 2015), suggesting that the healthy mOFC serves to access outcome value information when it is not immediately observable and thereby guide goal-directed decision-making. Our findings suggest that the mOFC also bidirectionally regulates effort allocation for a given reward and that expression of the neurotrophin BDNF in the mOFC is both necessary and sufficient for mice to sustain stable representations of reinforcer value.
cue dorsal striatum neurotrophin operant orbital progressive ratio
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Old 09-05-2016, 03:09 PM   #11
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Default Dual-process theories and consciousness: the case for ‘Type Zero’ cognition

http://nc.oxfordjournals.org/content/2016/1/niw005

Abstract
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A step towards a theory of consciousness would be to characterize the effect of consciousness on information processing. One set of results suggests that the effect of consciousness is to interfere with computations that are optimally performed non-consciously. Another set of results suggests that conscious, system 2 processing is the home of norm-compliant computation. This is contrasted with system 1 processing, thought to be typically unconscious, which operates with useful but error-prone heuristics. These results can be reconciled by separating out two different distinctions: between conscious and non-conscious representations, on the one hand, and between automatic and deliberate processes, on the other. This pair of distinctions is used to illuminate some existing experimental results and to resolve the puzzle about whether consciousness helps or hinders accurate information processing. This way of resolving the puzzle shows the importance of another category, which we label ‘type 0 cognition’, characterized by automatic computational processes operating on non-conscious representations.
consciousness unconscious processing theories and models function of consciousness dual processing

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Old 29-06-2016, 11:13 AM   #12
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Default Opposing Effects of Neuronal Activity on Structural Plasticity

http://journal.frontiersin.org/artic...016.00075/full

Introduction
Quote:
Information from the environment leads to the activation of neural subnetworks in the brain. The connectivity of these neural subnetworks, i.e., the existence and strength of synapses between neurons, influences the neuronal activation and, thereby, determines the way environmental information is processed. Accordingly, the long-term storage of information is related to activity-dependent (long-lasting) changes in connectivity (Hebb, 1949; Morris et al., 1986; Rioult-Pedotti et al., 1998; Leuner et al., 2003; Pastalkova et al., 2006; Whitlock et al., 2006; reviewed, e.g., in Martin et al., 2000; Chklovskii et al., 2004; Dudai, 2004; Hübener and Bonhoeffer, 2010). Basically two types of activity-dependent mechanisms yield such changes: synaptic or functional plasticity and structural plasticity. Structural or architectural plasticity determines the formation and removal of synapses. On the other hand, synaptic or functional plasticity changes the electrochemical transmission efficacy of synapses by altering, for instance, the receptor configuration of the postsynaptic site. Note, as we will show, this functional synaptic plasticity is associated with structural changes at existing synapses (size, postsynaptic density, etc.) and these changes are sometimes summarized as structural plasticity (Lamprecht and LeDoux, 2004). However, here we restrict structural plasticity to changes of the number of synapses (and of axonal/dendritic trees) and refer the long-term functional changes at existing synapses as synaptic plasticity.

The alterations of the transmission efficacy by synaptic plasticity depend on the level of neuronal activation. However, the mapping between activity level and triggered synaptic changes is not unique. In general, they are categorized into two classes: Hebbian and homeostatic synaptic plasticity. Hebbian synaptic plasticity yields an increase in synaptic efficacy given high neuronal activities (long-term potentiation; LTP; Bliss and Lomo, 1973; Lynch et al., 1983; Bliss and Collingridge, 1993; see Feldman, 2009 for a review), while low levels of activity induce a decrease (long-term depression; LTD; Lynch et al., 1977; Dudek and Bear, 1992; Mulkey and Malenka, 1992; see Collingridge et al., 2010 for a review). Thus, Hebbian synaptic plasticity basically maps the neuronal activation onto the synaptic efficacies or rather connectivity (high activity → stronger connections; low activity → weaker connections; Hebb, 1949; Bliss and Lomo, 1973; Dudek and Bear, 1992; Kirkwood et al., 1996). These changes in the connectivity, in turn, influence the neuronal activities. Along these lines, theoretical studies show (Rochester et al., 1956; Riedel and Schild, 1992; Gerstner and Kistler, 2002; Kolodziejski et al., 2010) that Hebbian synaptic plasticity alone induces a positive feedback loop leading to unrestricted synaptic (and thus neuronal) dynamics. On the other hand, homeostatic synaptic plasticity, as synaptic scaling (Turrigiano et al., 1998), act conversely to Hebbian synaptic plasticity. If neuronal activities are high, synaptic efficacies are decreased, while, if activities are low, efficacies are increased (high activity → weaker connections; low activity → stronger connections; Turrigiano et al., 1998; Hou et al., 2008, 2011; Ibata et al., 2008). Thereby, homeostatic synaptic plasticity alone induces a negative feedback loop and, thus, stabilizes the dynamics. As several theoretical results indicate (Tetzlaff et al., 2011; Zenke et al., 2013; Toyoizumi et al., 2014), the combination of both plasticity processes lead to desired, stable dynamics.

We will argue in this review that, analogous to functional synaptic plasticity, structural plasticity can also be categorized into two different classes of activity-dependency: (i) One class of structural changes maps features of the neuronal activity onto the connectivity, such that the connectivity is strengthened with high activity levels and vice versa. These changes will be referred to as Hebbian structural plasticity (Hebb, 1949; Helias et al., 2008). (ii) The other class of structural changes weakens (strengthens) the connectivity given high (low) neuronal activities and, thus, stabilizes the dynamics. This class is named homeostatic structural plasticity (Butz et al., 2009).

Note, this classification is phenomenological. Changes in connectivity (synaptic as well as structural) are not directly linked to neuronal activity. Neuronal activity initiates such changes by triggering secondary processes as molecular signaling cascades, which lead to the corresponding changes. For the here discussed plasticity processes, these underlying signaling cascades can have different degrees of similarity, which we will not consider in detail. The focus of this review is to systematize the qualitative links between the neuronal activity level and resulting connectivity changes.

Moreover, we focus on morphological changes of connections between excitatory neurons only. The dynamics of inhibitory synapses has been reviewed, for instance, by Vogels et al. (2013) for inhibitory synaptic plasticity and by Flores and Méndez (2014) for inhibitory structural plasticity. Further non-synaptic homeostatic mechanisms stabilizing neural network dynamics have been reviewed in Turrigiano and Nelson (2004), Marder and Goaillard (2006), or Yin and Yuan (2014).

In the following, as structural and synaptic plasticity are linked to each other, we first briefly outline the main findings for synaptic plasticity. Then, we review the morphological changes of synapses induced by synaptic plasticity and relate these changes to the dynamics of synapses and, thus, to structural plasticity. Following this, we summarize the experimental evidence of activity-dependent structural changes and categorize these, similar to synaptic plasticity, into the two classes of Hebbian and homeostatic structural plasticity. We also briefly review indications of Hebbian and homeostatic processes occurring during development. Finally, we sort theoretical investigations studying the dynamics of structural plasticity by this categorization and, based on their results, arrive at conclusions about the different functional roles of Hebbian and homeostatic structural plasticity.
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Old 29-06-2016, 11:19 AM   #13
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Default Emergent spatial patterns of excitatory and inhibitory synaptic strengths drive somatotopic representational discontinuities and their plasticity

in a computational model of primary sensory cortical area 3b

http://journal.frontiersin.org/artic...00072/abstract

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Mechanisms underlying the emergence and plasticity of representational discontinuities in the mammalian primary somatosensory cortical representation of the hand are investigated in a computational model. The model consists of an input lattice organized as a three-digit hand forward-connected to a lattice of cortical columns each of which contains a paired excitatory and inhibitory cell. Excitatory and inhibitory synaptic plasticity of feedforward and lateral connection weights is implemented as a simple covariance rule and competitive normalization. Receptive field properties are computed independently for excitatory and inhibitory cells and compared within and across columns. Within digit representational zones intracolumnar excitatory and inhibitory receptive field extents are concentric, single-digit, small, and unimodal. Exclusively in representational boundary-adjacent zones, intracolumnar excitatory and inhibitory receptive field properties diverge: excitatory cell receptive fields are single-digit, small, and unimodal; and the paired inhibitory cell receptive fields are bimodal, double-digit, and large. In simulated syndactyly (webbed fingers), boundary-adjacent intracolumnar receptive field properties reorganize to within-representation type; divergent properties are reacquired following syndactyly release. This study generates testable hypotheses for assessment of cortical laminar-dependent receptive field properties and plasticity within and between cortical representational zones. For computational studies, present results suggest that concurrent excitatory and inhibitory plasticity may underlie novel emergent properties.
Keywords: Somatosensory Cortex, Area 3b, Syndactyly, inhibitory synaptic plasticity, neural plasticity, somatotopy, cortical column, receptive field, emergent properties
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Old 22-07-2016, 10:54 AM   #14
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Default Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates

http://journals.plos.org/plosbiology...l.pbio.1002512

Abstract

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Mammals show a wide range of brain sizes, reflecting adaptation to diverse habitats. Comparing interareal cortical networks across brains of different sizes and mammalian orders provides robust information on evolutionarily preserved features and species-specific processing modalities. However, these networks are spatially embedded, directed, and weighted, making comparisons challenging. Using tract tracing data from macaque and mouse, we show the existence of a general organizational principle based on an exponential distance rule (EDR) and cortical geometry, enabling network comparisons within the same model framework. These comparisons reveal the existence of network invariants between mouse and macaque, exemplified in graph motif profiles and connection similarity indices, but also significant differences, such as fractionally smaller and much weaker long-distance connections in the macaque than in mouse. The latter lends credence to the prediction that long-distance cortico-cortical connections could be very weak in the much-expanded human cortex, implying an increased susceptibility to disconnection syndromes such as Alzheimer disease and schizophrenia. Finally, our data from tracer experiments involving only gray matter connections in the primary visual areas of both species show that an EDR holds at local scales as well (within 1.5 mm), supporting the hypothesis that it is a universally valid property across all scales and, possibly, across the mammalian class.
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Old 17-09-2016, 10:03 AM   #15
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Default Different Stages, Different Signals: The Modulating Effect of Cognitive Conflict on Subsequent Processing

http://journals.plos.org/plosone/art...l.pone.0163263

Abstract

Quote:
The present study used event-related potentials (ERPs) to investigate the function of signals induced by cognitive conflict during the detection stage and the resolution stage of perceptual processing. The study used a combination of the Stroop task and an affective priming task to examine the conflict priming effect when the stimulus onset asynchrony (SOA) was 200 ms or 800 ms. Behavioral results showed that the RTs were shorter for positive targets following congruent primes relative to incongruent primes, and for negative targets following incongruent primes relative to congruent primes when the SOA was 200 ms. ERP results showed that the N2 amplitudes (200–300 ms) for incongruent stimuli were significantly larger than for congruent stimuli in the Stroop task, which indicated a significant conflict effect. Moreover, the N400 amplitudes (500–700 ms) for positive targets after congruent primes were significantly lower than those after incongruent primes when the SOA was 200 ms, which showed a significant negative priming effect. While the SOA was 800 ms, behavioral results showed that the RTs were shorter for positive targets following incongruent primes relative to congruent primes. ERP results showed that the N2 amplitudes (200–300 ms) for incongruent stimuli were significantly larger than for congruent stimuli in the Stroop task, which indicated a significant conflict effect. The N400 amplitudes (1100–1300 ms) for the negative targets after congruent primes were significantly lower than those after incongruent primes when the SOA was 800 ms, which showed a significant positive priming effect. The results demonstrated that the functions of signals induced by cognitive conflict were reversed in two different cognitive processing stages.
Quote:
Cognitive control refers to the human ability to adjust actions and goals in response to external and internal demands during ongoing information processing [1]. In everyday life, for example, if right-handed persons are required to write or eat with their left hand, they would find it difficult to do and they would have to pay more attention to complete the task. In the laboratory, cognitive control is mainly studied by using a conflict-response paradigm, as conflict is a primer for cognitive control processing [2]. Over the past few decades, studies on control did not just involve “cold” cognition, but involved emotional processing and affective adjustments [3, 4]. Evidences from brain imaging researches have shown that the anterior cingulate cortex (ACC) plays a major role in cognitive-control processing [2
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Old 30-09-2016, 03:20 PM   #16
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Default Distinguishing Representations as Origin and Representations as Input: Roles for Individual Neurons

http://journal.frontiersin.org/artic...016.01537/full

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It is widely perceived that there is a problem in giving a naturalistic account of mental representation that deals adequately with the issue of meaning, interpretation, or significance (semantic content). It is suggested here that this problem may arise partly from the conflation of two vernacular senses of representation: representation-as-origin and representation-as-input. The flash of a neon sign may in one sense represent a popular drink, but to function as a representation it must provide an input to a ‘consumer’ in the street. The arguments presented draw on two principles – the neuron doctrine and the need for a venue for ‘presentation’ or ‘reception’ of a representation at a specified site, consistent with the locality principle. It is also argued that domains of representation cannot be defined by signal traffic, since they can be expected to include ‘null’ elements based on non-firing cells. In this analysis, mental representations-as-origin are distributed patterns of cell firing. Each firing cell is given semantic value in its own right – some form of atomic propositional significance – since different axonal branches may contribute to integration with different populations of signals at different downstream sites. Representations-as-input are patterns of local co-arrival of signals in the form of synaptic potentials in dendrites. Meaning then draws on the relationships between active and null inputs, forming ‘scenarios’ comprising a molecular combination of ‘premises’ from which a new output with atomic propositional significance is generated. In both types of representation, meaning, interpretation or significance pivots on events in an individual cell. (This analysis only applies to ‘occurrent’ representations based on current neural activity.) The concept of representations-as-input emphasizes the need for an internal ‘consumer’ of a representation and the dependence of meaning on the co-relationships involved in an input interaction between signals and consumer. The acceptance of this necessity provides a basis for resolving the problem that representations appear both as distributed (representation-as-origin) and as local (representation-as-input). The key implications are that representations in the brain are massively multiple both in series and in parallel, and that individual cells play specific semantic roles. These roles are discussed in relation to traditional concepts of ‘gnostic’ cell types.
Introduction
Quote:
Concepts of mental representation are widely invoked in neurobiology, linguistics, artificial intelligence, and philosophy. Yet, as Seager and Bourget (2007) note: “there is no acknowledged theory of mental representation.” This appears to be partly because people differ in terms of the explanatory work they want such a theory to do (Stich, 1992). It also reflects an impasse in reaching a consensus on how mental representations could fit into a naturalistic account of the brain; what sort of substrate, or causal nexus could support a mental representation, and how? I shall argue that these are interdependent questions and that a careful assessment of the logical constraints on substrate, in terms of physical dynamics and their location, may clarify the ways in which mental representation may be a useful concept, as well as vice versa.

From the outset I wish to emphasize that the problem I address relates only to what may be called ‘occurrent’ or ‘active’ representations in which signals are sent and received on specific occasions. There is another use of the term that might be called a ‘dispositional representation’ – an acquired pattern of cellular connectivity underlying memory, knowledge, or concept acquisition, that disposes the brain to generate occurrent representations in response to stimuli (Simmons and Barsalou, 2003). I will be using ‘representation’ to mean ‘occurrent representation.’

The naturalization problem is not so much about whether a representation is to the right, left, front or back of the brain, or what connection tracts are involved. The more basic problem is defining the type, or level, of biophysical location that could support a fitting causal role, and with appropriate information capacity (‘bandwidth’). There are those who would argue that we have a rough answer: that representations can be equated with patterns of neural activity, or firing. However, as discussed below, this fails to address key problems, justifiably of concern to philosophers of mind. Meaning is not to be solved so easily.

It might be argued that searching for a detailed substrate type for mental representation is overly reductionist or, in theoretical modeling terms, simply premature. It might even be considered immaterial to understanding of how a representation can have a meaning, either in terms of external referents or internal ‘meaning to the subject.’ However, I think the search is justified on the following grounds. Firstly, spatial pattern is about the only way meaning can be encoded in a brain at any point in time, as far as we know, so at least type of spatial pattern and location is likely to be central to a theory of meaning. Secondly, recognizing that reductive analysis of mechanism is only part of the story does not mean that fruitful progress in neural mechanisms should be abandoned half-finished and replaced by hand-waving. Rather than, as Marr (1982) advocated, treating the biophysical and ‘functional’ levels of analysis as incommensurable, to be able to test viability of theories I believe, with Trehub (1991), that we need some idea of how and where they could correspond.

Moreover, the ability to suggest at least one plausible physical example for any theoretical model is a requirement that is arguably never premature. A search for such examples can render explicit contradictions in popular concepts. The key proposal here is that neuropsychology may benefit from a greater focus on the input aspect of mental representation. The author’s background is in immunology. It was not until we insisted on a grounding in a dynamics of integration of signals into individual cells that we began to understand leucocyte behavior in immune recognition and memory (Male et al., 2012). Hypotheses that could not be so grounded were discarded. The gap between work on post-synaptic integration (e.g., Branco and Häusser, 2011; Smith et al., 2013; Ishikawa et al., 2015) and psychology may still be harder to bridge but the possibility of grounding in plausible input mechanisms should be an acid test of all models of mental representation.
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Old 21-10-2016, 05:42 PM   #17
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Default Disrupted nodal and hub organization account for brain network abnormalities in Parkinson’s disease

http://journal.frontiersin.org/artic...00259/abstract

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The recent application of graph theory to brain networks promises to shed light on complex diseases such as Parkinson’s disease. This study aimed to investigate functional changes in sensorimotor and cognitive networks in parkinsonian patients, with a focus on inter- and intra-connectivity organization in the disease-associated nodal and hub regions using the graph theoretical analyses. Resting-state functional MRI data of a total of 65 participants, including 23 healthy controls and 42 patients, were investigated in 120 nodes for local efficiency, betweenness centrality, and degree. Hub regions were identified in the healthy control and patient groups. We found nodal and hub changes in patients compared with healthy controls, including the right pre-supplementary motor area, left anterior insula, bilateral mid-insula, bilateral dorsolateral prefrontal cortex, and right caudate nucleus. In general, nodal regions within the sensorimotor network (i.e. right pre-supplementary motor area and right mid-insula) displayed weakened connectivity, with the former node associated with more severe bradykinesia, and impaired integration with default mode network regions. The left mid-insula also lost its hub properties in patients. Within the executive networks, the left anterior insular cortex lost its hub properties in patients, while a new hub region was identified in the right caudate nucleus, paralleled by an increased level of inter- and intra-connectivity in the bilateral dorsolateral prefrontal cortex possibly representing compensatory mechanisms. These findings highlight the diffuse changes in nodal organization and regional hub disruption accounting for the distributed abnormalities across brain networks and the clinical manifestations of Parkinson’s disease.
Keywords: Parkinson’s disease, bradykinesia, cognitive impairment, Resting-state fMRI, brain network
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Old 08-11-2016, 08:52 PM   #18
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Default Quantum Walks in Brain Microtubules—A Biomolecular Basis for Quantum Cognition?

http://onlinelibrary.wiley.com/doi/1...ops.12068/full

Abstract

Quote:
Cognitive decisions are best described by quantum mathematics. Do quantum information devices operate in the brain? What would they look like? Fuss and Navarro (2013) describe quantum lattice registers in which quantum superpositioned pathways interact (compute/integrate) as ‘quantum walks’ akin to Feynman's path integral in a lattice (e.g. the ‘Feynman quantum chessboard’). Simultaneous alternate pathways eventually reduce (collapse), selecting one particular pathway in a cognitive decision, or choice. This paper describes how quantum walks in a Feynman chessboard are conceptually identical to ‘topological qubits’ in brain neuronal microtubules, as described in the Penrose-Hameroff 'Orch OR' theory of consciousness.


A New Spin on Neural Processing Quantum Cognition

http://journal.frontiersin.org/artic...016.00541/full

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Although quantum mechanics is fundamental for understanding molecular mechanisms in physics and chemistry, it has usually been assumed to be unimportant for understanding molecular mechanisms of biological systems. However, there is increasing evidence that quantum mechanics is important for understanding some biological phenomena (Lambert et al., 2013), such as energy transfer in photosynthesis (Fassioli et al., 2014), navigation by birds using the earth's magnetic field (Hiscock et al., 2016), and electron and hydrogen tunneling in biochemical reactions (Klinman and Kohen, 2013). There have also been proposals that quantum mechanics may help explain aspects of brain function.

Discussions about quantum mechanics and the brain began with questions on the role of measurement or observation in quantum mechanics (Stapp, 1991; Theise and Kafatos, 2013). Further developments began to highlight the possibility that quantum mechanics might help explain neural mechanisms involved in consciousness or synaptic function (Stapp, 1991; Beck and Eccles, 1992). Another topic that emerged was whether quantum mechanisms might be employed by the brain to perform calculations, i.e., the possibility of quantum computing in the brain (Penrose, 1989). For example, a model of consciousness was developed that involves quantum computations in neuronal microtubules (Tegmark, 2000; Penrose and Hameroff, 2011; Hameroff and Penrose, 2014a,b; Reimers et al., 2014; Craddock et al., 2015). Other proposals have focused on the quantum phenomenon of spin (see below). Hu and Wu (2004) suggested that nuclear spins of hydrogen, nitrogen, and phosphorus in neuronal cellular components and electron spins of diffusible oxygen and nitric oxide in the brain might mediate consciousness. Electron spins in the brain have also been suggested as a potential target of transcranial magnetic stimulation therapies (Chervyakov et al., 2015). Other perspectives have led to application of quantum probability theory to human decision making (Wang et al., 2014; Kvam et al., 2015). Finally, the above mentioned navigation by birds may involve a quantum mechanical cryptochrome radical-pair (spin dynamic) mechanism in neuronal retinal ganglion cells that transmit information to the brain (Mouritsen et al., 2004; Hiscock et al., 2016).
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Old 22-11-2016, 02:48 AM   #19
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Default Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

http://journal.frontiersin.org/artic...016.00095/full

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There is considerable scientific interest in understanding how cell assemblies—the long-presumed computational motif—are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2i–1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors—the synaptic switch for learning and memory—were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques—which preferentially encode specific and low-combinatorial features and project inter-cortically—is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6—which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems—is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain’s basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.
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Old 22-12-2016, 12:12 AM   #20
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Default The Last Frontier: The Molecular Basis of Brain Plasticity and How Neurons Learn

http://neurosciencenews.com/brain-pl...learning-5792/

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In a new paper published in the journal Neuron, Kosik explores the nature of brain plasticity and proposes a theory about how neurons learn.

“It’s fairly well established scientifically that the learning units in the brain are the synapses,” said Kosik, UCSB’s Harriman Professor of Neuroscience and co-director of the campus’s Neuroscience Research Institute. “Many neuroscientists think that synaptic learning requires new proteins made locally right at the synapse, which acts as its own control center.”

A neuron are large cell that contains a cell body and dendrites, branched extensions along which impulses received from other cells at the synapses are transmitted to the cell body. In theory, learning takes place at synaptic junctions. But the sheer number of synapses — multiple thousands — makes it unlikely that all of them can make the RNA responsible for creating new proteins.
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Old 27-12-2016, 12:17 AM   #21
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Default Inhibitory neurons form neural networks that become broader as they mature, a new study reports.

http://neurosciencenews.com/brain-ne...ormation-5811/

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The studies of excitatory maps have revealed that they begin as a diffuse and overlapping network of cells. “With time,” said Arenkiel, “experience sculpts this diffuse pattern of activity into better defined areas, such that individual mouse whiskers, for instance, are represented by discrete segments of the brain cortex. This progression from a diffuse to a refined pattern occurs in many areas of the brain.”
Quote:
The scientists expected that inhibitory networks would mature in a way similar to that of excitatory networks. That is, the more the animal experienced a scent, the better defined the networks of activity would become. Surprisingly, the scientists discovered that the inhibitory brain circuits of the mouse sense of smell develop in a manner opposite to the excitatory circuits. Instead of becoming narrowly defined areas, the inhibitory circuits become broader. Thanks to this new finding scientists now better understand how the brain organizes and processes information.
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Old 04-01-2017, 06:53 PM   #22
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Default A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

http://www.jneurosci.org/content/37/1/83?etoc=

Abstract

Quote:
A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism.



Working memory operates over the same representations as attention

http://journals.plos.org/plosone/art...l.pone.0179382

Abstract

Quote:
A recent study observed a working memory (WM) Stroop effect with a magnitude equivalent to that of the classic Stroop effect, indicating that WM operates over the same representations as attention. However, more research is needed to examine this proposal. One unanswered question is whether the WM Stroop effect occurs when the WM item and the perceptual task do not have an overlapping response set. We addressed this question in Experiment 1 by conducting an attentional word-color task and a WM word-color task. The results showed that a WM Stroop effect also occurred in that condition, as a word that only indirectly evoked a color representation could interfere with the color judgement in both the attentional task and WM task. In Experiment 2, we used a classic Simon task and a WM Simon task to examine whether holding visuo-spatial information rather than verbal information in WM could interfere with perceptual judgment as well. We observed a WM Simon effect of equivalent magnitude to that of the classic Simon effect. The well-known stimulus-response compatibility effect also existed in the WM domain. The two experiments together demonstrated that WM operates over the same representations as attention, which sheds new light on the hypothesis that working memory is internally directed attention.
Update 14/06/2017




Stable and Dynamic Coding for Working Memory in Primate Prefrontal Cortex

http://www.jneurosci.org/content/37/27/6503?etoc=

Abstract

Quote:
Working memory (WM) provides the stability necessary for high-level cognition. Influential theories typically assume that WM depends on the persistence of stable neural representations, yet increasing evidence suggests that neural states are highly dynamic. Here we apply multivariate pattern analysis to explore the population dynamics in primate lateral prefrontal cortex (PFC) during three variants of the classic memory-guided saccade task (recorded in four animals). We observed the hallmark of dynamic population coding across key phases of a working memory task: sensory processing, memory encoding, and response execution. Throughout both these dynamic epochs and the memory delay period, however, the neural representational geometry remained stable. We identified two characteristics that jointly explain these dynamics: (1) time-varying changes in the subpopulation of neurons coding for task variables (i.e., dynamic subpopulations); and (2) time-varying selectivity within neurons (i.e., dynamic selectivity). These results indicate that even in a very simple memory-guided saccade task, PFC neurons display complex dynamics to support stable representations for WM.

SIGNIFICANCE STATEMENT Flexible, intelligent behavior requires the maintenance and manipulation of incoming information over various time spans. For short time spans, this faculty is labeled “working memory” (WM). Dominant models propose that WM is maintained by stable, persistent patterns of neural activity in prefrontal cortex (PFC). However, recent evidence suggests that neural activity in PFC is dynamic, even while the contents of WM remain stably represented. Here, we explored the neural dynamics in PFC during a memory-guided saccade task. We found evidence for dynamic population coding in various task epochs, despite striking stability in the neural representational geometry of WM. Furthermore, we identified two distinct cellular mechanisms that contribute to dynamic population coding.
Update 05/07/2017
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Old 04-01-2017, 10:18 PM   #23
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Default Mapping How the Brain Stores Memories

http://neurosciencenews.com/memory-s...-mapping-5852/

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A new study led by scientists at The Scripps Research Institute (TSRI) sheds light on how the brain stores memories. The research, published recently in the journal eLife, is the first to demonstrate that the same brain region can both motivate a learned behavior and suppress that same behavior.



“We’ll always have Paris.” Or will we?

http://neurosciencenews.com/fading-happy-memories-7120/

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Buy a memento
When patients say to me "OMG, I'm never going to remember all this" I hand them a note pad and a pen. The note pad has my contact details on it. I encourage them to draw as well as write.
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Old 13-01-2017, 12:06 PM   #24
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Default Could a Neuroscientist Understand a Microprocessor?

http://journals.plos.org/ploscompbio...l.pcbi.1005268

Abstract

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There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.
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Old 16-02-2017, 08:23 PM   #25
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Default The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons

http://journal.frontiersin.org/artic...017.00186/full

Quote:
The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.
Quote:
The cortical column – a cluster of neurons that have similar response properties and which are located physically together in a columnar form across layers of the cortex – is now widely accepted in neuroscience as the fundamental processing unit of the neocortex (Mountcastle, 1997; Horton and Adams, 2005; DeFelipe, 2012). There are some very interesting findings from studies of the cortical columns and it makes sense to understand the nature and operation of cortical columns from a representational and computational point of view. So that is a major focus of this paper.

Encoding of complex abstract concepts is the second major focus of this paper. Distributed representation theorists have always questioned whether the brain is capable of abstracting complex concepts and encoding them in single cells (neurons) or in a collection of cells dedicated to that concept.
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Old 24-02-2017, 10:52 PM   #26
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Some background info on cortical columns you may find interesting.

Quote:
Confusing cortical columns
Pasko Rakic

The late developmental neurobiologist, and a member of the National Academy of Sciences, Victor Hamburger told me during one of our discussions about the distinction between boring data and exciting concepts, “one can spend an entire lifetime correcting a flawed paper published in reputable journal and still loose the battle if people like the basic idea” (V. Hamburger, personal communication). An example of the longevity of basically incorrect information is the phenomenon of “The basic uniformity in structure of the neocortex,” published in 1980 by Rockel, Hiorns, and Powell (1). This highly influential paper had obvious problems at almost every level: The authors selected an arbitrary 30-μm-wide, 25-μm-deep vertical cortical “column” between the pia and the bottom of the cortex, because the ruler in the graticule of the oil-immersion eyepiece on their microscope had a 30-μm marker and their histological sections were 25 μm thick; then, they estimated that the number of neurons within this “minicolumn” is 110 in all cytoarchitectonic areas examined, without any correction for the cell size; and finally, based on this dubious finding, they made a broad generalization that the magic number of 110 is constant in all mammalian species (rodents, carnivore, and primates, including human) in all cytoarchitectonic areas (except the primary visual cortex in primates). This finding led them to conclude that, “the intrinsic structure of the neocortex is basically more uniform than has been thought and that differences in cytoarchitecture and function reflects differences in connections.”
It's a short text
http://www.pnas.org/content/105/34/12099.full
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Old 04-03-2017, 07:33 PM   #27
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Default Understanding the Brain With the Help of Artificial Intelligence

http://neurosciencenews.com/artifici...obiology-6195/

Quote:
Summary: Researchers have trained neural networks to accelerate the reconstruction of neural circuits.
Quote:
Neurons need company. Individually, these cells can achieve little, however when they join forces neurons form a powerful network which controls our behaviour, among other things. As part of this process, the cells exchange information via their contact points, the synapses. Information about which neurons are connected to each other when and where is crucial to our understanding of basic brain functions and superordinate processes like learning, memory, consciousness and disorders of the nervous system. Researchers suspect that the key to all of this lies in the wiring of the approximately 100 billion cells in the human brain.

To be able to use this key, the connectome, that is every single neuron in the brain with its thousands of contacts and partner cells, must be mapped. Only a few years ago, the prospect of achieving this seemed unattainable. However, the scientists in the Electrons – Photons – Neurons Department of the Max Planck Institute of Neurobiology refuse to be deterred by the notion that something seems “unattainable”. Hence, over the past few years, they have developed and improved staining and microscopy methods which can be used to transform brain tissue samples into high-resolution, three-dimensional electron microscope images. Their latest microscope, which is being used by the Department as a prototype, scans the surface of a sample with 91 electron beams in parallel before exposing the next sample level. Compared to the previous model, this increases the data acquisition rate by a factor of over 50. As a result an entire mouse brain could be mapped in just a few years rather than decades.
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Old 08-03-2017, 12:16 PM   #28
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Default Progress towards a circuit diagram of the brain

https://www.sciencedaily.com/release...0307113536.htm

Quote:
Precise knowledge of the connections in the brain – the links between all the nerve cells – is a prerequisite for better understanding this most complex of organs. Now researchers have developed a new algorithm for analyzing image data.
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Old 08-03-2017, 12:21 PM   #29
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Default Brainlike computers are a black box. Scientists are finally peering inside

http://www.sciencemag.org/news/2017/...et_cid=1203472

Quote:
Neural networks, also called neural nets, are loosely based on the brain’s use of layers of neurons working together. Like the human brain, they aren't hard-wired to produce a specific result—they “learn” on training sets of data, making and reinforcing connections between multiple inputs. A neural net might have a layer of neurons that look at pixels and a layer that looks at edges, like the outline of a person against a background. After being trained on thousands or millions of data points, a neural network algorithm will come up with its own rules on how to process new data. But it's unclear what the algorithm is using from those data to come to its conclusions.

“Neural nets are fascinating mathematical models,” says Wojciech Samek, a researcher at Fraunhofer Institute for Telecommunications at the Heinrich Hertz Institute in Berlin. “They outperform classical methods in many fields, but are often used in a black box manner.”

In an attempt to unlock this black box, Samek and his colleagues created software that can go through such networks backward in order to see where a certain decision was made, and how strongly this decision influenced the results. Their method, which they will describe this month at the Centre of Office Automation and Information Technology and Telecommunication conference in Hanover, Germany, enables researchers to measure how much individual inputs, like pixels of an image, contribute to the overall conclusion. Pixels and areas are then given a numerical score for their importance. With that information, researchers can create visualizations that impose a mask over the image. The mask is most bright where the pixels are important and darkest in regions that have little or no effect on the neural net’s output.
Quote:
This work could improve neural networks, Samek suggests. That includes helping reduce the amount of data needed, one of the biggest problems in AI development, by focusing in on what the neural nets need. It could also help investigate errors when they occur in results, like misclassifying objects in an image.

Other researchers are working on similar processes to look into how algorithms make decisions, including neural nets for visuals as well as text. Continued research is important as algorithms make more decisions in our daily lives, says Sara Watson, a technology critic with the Berkman Klein Center for Internet & Society at Harvard University. The public needs tools to be able to understand how AI makes decisions. Algorithms, far from being perfect arbitrators of truth, are only as good as the data they’re given, she notes.



Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network

http://journals.plos.org/plosone/art...l.pone.0178683

Abstract

Quote:
Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.
Update 29/05/2017




Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease

http://journals.plos.org/ploscompbio...l.pcbi.1005550

Abstract

Quote:
Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.

Author summary

While the structural connectome of the brain has emerged as a powerful tool towards understanding the progression of neurologic and psychiatric disorders, links between the anatomy of connections within the brain and the effects of localized white matter pathology on cognition are still an active area of investigation. Here, we propose the use of the diffusion process towards understanding perturbations of brain connectivity. We find that while the dynamics of this process are strongly conserved in healthy subjects, they display significant, interpretable deviations in agenesis of the corpus callosum, one of the most common brain malformations. These findings, including the strong similarity between regions identified to be crucial towards diffusion and nexus regions of white matter from edge density imaging, show converging evidence towards understanding the relationship between white matter anatomy and the structural connectome.
Update 24/06/2017
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Old 08-03-2017, 04:55 PM   #30
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Quote:
Other researchers are working on similar processes to look into how algorithms make decisions, including neural nets for visuals as well as text.

Continued research is important as algorithms make more decisions in our daily lives, says Sara Watson,....

Algorithms, far from being perfect arbitrators of truth, are only as good as the data they’re given, she notes.
That those algorithms are far from being perfect is somehow illustrated here:

Quote:
Computer bots are more like humans than you might think, having fights lasting years
Bots interact with one another, whether or not by design, and it leads to unpredictable consequences
February 23, 2017

Researchers say 'benevolent bots', otherwise known as software robots, that are designed to improve articles on Wikipedia sometimes have online 'fights' over content that can continue for years. Editing bots on Wikipedia undo vandalism, enforce bans, check spelling, create links and import content automatically, whereas other bots (which are non-editing) can mine data, identify data or identify copyright infringements. The team analysed how much they disrupted Wikipedia, observing how they interacted on 13 different language editions over ten years (from 2001 to 2010). They found that bots interacted with one another, whether or not this was by design, and it led to unpredictable consequences. The research paper, published in PLOS ONE, concludes that bots are more like humans than you might expect. Bots appear to behave differently in culturally distinct online environments. The paper says the findings are a warning to those using artificial intelligence for building autonomous vehicles, cyber security systems or for managing social media. It suggests that scientists may have to devote more attention to bots' diverse social life and their different cultures.
https://www.sciencedaily.com/release...0223142117.htm
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Old 08-03-2017, 06:23 PM   #31
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Yes,

I saw the PLOS paper. It is fairly close to home for me as I have a family member who takes on short term contracts and always gets short listed when his CV is read by a person, but can't get onto shortlists selected by algorithm.

Diagnostic algorithms may have holes in them, which can be disastrous when hard pressed clinicians go into robot mode. Decades ago when I was carrying a respiratory bleep, I spent almost a minute listening for breath sounds before I realised that the patient was deceased. I was incredibly pressed for time, but no excuse.
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Old 10-03-2017, 11:42 AM   #32
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Default Mnemonic Training Reshapes Brain Networks to Support Superior Memory

http://www.cell.com/neuron/fulltext/...273(17)30087-9

Highlights

Quote:
•Memory champions show distributed functional brain network connectivity changes

•Mnemonic strategies for superior memory can be learned by naive subjects

•Mnemonic training induces similarity with memory champion brain connectivity

•Brain network dynamics of this effect differ between task and resting state
Summary
Quote:
Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world’s most successful memory athletes and matched controls with fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that, in a group of naive controls, functional connectivity changes induced by 6 weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain’s functional network organization to enable superior memory performance.
Junior doctors used to be able to do this. I remember someone writing an entire page from a textbook on Parkinsons Disease for me from memory, I wonder if the current generation can do this.

Surgical trainees can still draw beautifully




Only Three Fingers Write, but the Whole Brain Works†: A High-Density EEG Study Showing Advantages of Drawing Over Typing for Learning

http://journal.frontiersin.org/artic...017.00706/full

Quote:
Are different parts of the brain active when we type on a keyboard as opposed to when we draw visual images on a tablet? Electroencephalogram (EEG) was used in young adults to study brain electrical activity as they were typing or describing in words visually presented PictionaryTM words using a keyboard, or as they were drawing pictures of the same words on a tablet using a stylus. Analyses of temporal spectral evolution (time-dependent amplitude changes) were performed on EEG data recorded with a 256-channel sensor array. We found that when drawing, brain areas in the parietal and occipital regions showed event related desynchronization activity in the theta/alpha range. Existing literature suggests that such oscillatory neuronal activity provides the brain with optimal conditions for learning. When describing the words using the keyboard, upper alpha/beta/gamma range activity in the central and frontal brain regions were observed, especially during the ideation phase. However, since this activity was highly synchronized, its relation to learning remains unclear. We concluded that because of the benefits for sensory-motor integration and learning, traditional handwritten notes are preferably combined with visualizations (e.g., small drawings, shapes, arrows, symbols) to facilitate and optimize learning.
Introduction
Quote:
The general effectiveness of notetaking in educational settings is well-documented, but the evidence mainly stems from a time when laptop use in classrooms was not very common. Previous research has focused on how encoding affects learning (e.g., Kiewra, 1989). The encoding hypothesis proposes that the processing that occurs during notetaking enhances recall and retention. Notetaking can be generative (e.g., summarizing, reframing, paraphrasing) or non-generative (i.e., verbatim transcribing). Verbatim notetaking typically involves relatively shallow cognitive processing (Craik and Lockhart, 1972; Kiewra, 1985). Greater encoding benefits have been observed the more deeply information is processed during notetaking (DiVesta and Gray, 1973). Studies have shown that non-verbatim notetaking leads to better performance than verbatim notetaking, especially on conceptual items (Aiken et al., 1975; Bretzing and Kulhavy, 1979; Slotte and Lonka, 1999; Igo et al., 2005). Traditional laptop use, using the keyboard, promotes verbatim transcription of lecture content because most students can type much faster than they can write (Brown, 1988). Thus, typing may undermine the encoding benefits seen in past notetaking studies.
09/05/2017
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Old 10-03-2017, 04:52 PM   #33
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Default Potentiation of motor sub-networks for motor control but not working memory: Interaction of dACC and SMA revealed by resting-state directed functional connectivity

http://journals.plos.org/plosone/art...l.pone.0172531

Abstract

Quote:
The dorsal Anterior Cingulate Cortex (dACC) and the Supplementary Motor Area (SMA) are known to interact during motor coordination behavior. We previously discovered that the directional influences underlying this interaction in a visuo-motor coordination task are asymmetric, with the dACC→SMA influence being significantly greater than that in the reverse direction. To assess the specificity of this effect, here we undertook an analysis of the interaction between dACC and SMA in two distinct contexts. In addition to the motor coordination task, we also assessed these effects during a (n-back) working memory task. We applied directed functional connectivity analysis to these two task paradigms, and also to the rest condition of each paradigm, in which rest blocks were interspersed with task blocks. We report here that the previously known asymmetric interaction between dACC and SMA, with dACC→SMA dominating, was significantly larger in the motor coordination task than the memory task. Moreover the asymmetry between dACC and SMA was reversed during the rest condition of the motor coordination task, but not of the working memory task. In sum, the dACC→SMA influence was significantly greater in the motor task than the memory task condition, and the SMA→dACC influence was significantly greater in the motor rest than the memory rest condition. We interpret these results as suggesting that the potentiation of motor sub-networks during the motor rest condition supports the motor control of SMA by dACC during the active motor task condition.
Introduction

Quote:
How are brain networks potentiated for action? As with the muscles in the body, the potential for dynamics in the brain may be encoded in the relationship between the system’s rest state and its active state. This relationship has been extensively discussed in terms of the metabolic demands of the brain in both rest and task-active states, particularly from the perspective of the fMRI signal [1]. The explosion of interest in resting-state fMRI signals can in part be traced to these initial theoretical discussions. Nevertheless, much of resting-state fMRI (rsfMRI) research has been driven by the search for understanding default mode function in the brain [2], or in discovering network structure from spontaneous fluctuations in the fMRI signal [3, 4]. In large part, these initiatives have uncovered general structural constraints driving rsfMRI fluctuations that are spontaneous, induced by physiological stimulation [5], or constrained by task-active processing [6]. Yet, a parallel literature continues to investigate resting-state connectivity and its relationship to network function in the task-active state [7, 8]. These investigations indicate that functional connectivity between networks in the rest state, is predictive of the same in the task state [9].


Is “Allostasis” The Brain’s Essential Function?......By Neuroskeptic May 5, 2017

http://blogs.discovermagazine.com/ne.../#.WQ-RaojysdV

Quote:
A paper just published in Nature Human Behaviour makes some big claims about the brain. It’s called Evidence for a large-scale brain system supporting allostasis and interoception in humans, but how much is evidence and how much is speculation?

The authors, Ian R. Kleckner and colleagues of Northeastern University, argue that a core function of the brain is allostasis, which they define as the process by which the brain “efficiently maintains energy regulation in the body”. Allostasis entails “anticipating the body’s energy needs [and] preparing to meet those needs before they arise.” Kleckner et al. point to “physical movements to cool the body’s temperature before it gets too hot” as one example of allostasis.

A concept closely related to allostasis is interoception, the process by which the brain receives information about the body’s internal state from sensory nerves inside the body.
There is a buzz around this paper at the moment. It's worth reading what NeuroSkeptic has to say and the comments following his piece.

Update 07/05/2017
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