Synchronization and assembly formation in the visual cortex

Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification

BACKGROUND

In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges.

NEW METHOD

This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise.

RESULTS

The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively.

COMPARISON WITH EXISTING METHODS

In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods.

CONCLUSION

The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Local dendritic balance enables learning of efficient representations in networks of spiking neurons

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

The Sensory Neocortex and Associative Memory

Most behaviors in mammals are directly or indirectly guided by prior experience and therefore depend on the ability of our brains to form memories. The ability to form an association between an initially possibly neutral sensory stimulus and its behavioral relevance is essential for our ability to navigate in a changing environment. The formation of a memory is a complex process involving many areas of the brain. In this chapter we review classic and recent work that has shed light on the specific contribution of sensory cortical areas to the formation of associative memories. We discuss synaptic and circuit mechanisms that mediate plastic adaptations of functional properties in individual neurons as well as larger neuronal populations forming topographically organized representations. Furthermore, we describe commonly used behavioral paradigms that are used to study the mechanisms of memory formation. We focus on the auditory modality that is receiving increasing attention for the study of associative memory in rodent model systems. We argue that sensory cortical areas may play an important role for the memory-dependent categorical recognition of previously encountered sensory stimuli.

Experience-dependent emergence of fine-scale networks in visual cortex

Visual cortical neurons selectively respond to particular features of visual stimuli and this selective responsiveness emerges from specific connectivity in the cortex. Most visual response properties are basically established by eye opening and are thereafter modified or refined by visual experience based on activity-dependent synaptic modifications during an early postnatal period. Visual deprivation during this period impairs development of visual functions, such as visual acuity. We previously demonstrated that fine-scale networks composed of a population of interconnected layer 2/3 (L2/3) pyramidal neurons receiving common inputs from adjacent neurons are embedded in a small area in rat visual cortex. We suggested that this network could be a functional unit for visual information processing. In this study, we investigated the effects of early visual experience on the development of fine-scale networks and individual synaptic connections in rat visual cortical slices. We used two kinds of deprivation, binocular deprivation and dark rearing, which allowed visual inputs with only diffuse light and no visual input, respectively. The probability and strength of excitatory connections to L2/3 pyramidal cells increased during the 2 weeks after eye opening, and these changes were prevented by dark rearing, but not binocular deprivation. Fine-scale networks were absent just after eye opening and established during the following 2 weeks in rats reared with normal visual experience, but not with either type of deprivation. These results indicate that patterned vision is required for the emergence of the fine-scale network, whereas diffuse light stimulation is sufficient for the maturation of individual synapses.

A review of cell assemblies

Since the cell assembly (CA) was hypothesised, it has gained substantial support and is believed to be the neural basis of psychological concepts. A CA is a relatively small set of connected neurons, that through neural firing can sustain activation without stimulus from outside the CA, and is formed by learning. Extensive evidence from multiple single unit recording and other techniques provides support for the existence of CAs that have these properties, and that their neurons also spike with some degree of synchrony. Since the evidence is so broad and deep, the review concludes that CAs are all but certain. A model of CAs is introduced that is informal, but is broad enough to include, e.g. synfire chains, without including, e.g. holographic reduced representation. CAs are found in most cortical areas and in some sub-cortical areas, they are involved in psychological tasks including categorisation, short-term memory and long-term memory, and are central to other tasks including working memory. There is currently insufficient evidence to conclude that CAs are the neural basis of all concepts. A range of models have been used to simulate CA behaviour including associative memory and more process- oriented tasks such as natural language parsing. Questions involving CAs, e.g. memory persistence, CAs' complex interactions with brain waves and learning, remain unanswered. CA research involves a wide range of disciplines including biology and psychology, and this paper reviews literature directly related to the CA, providing a basis of discussion for this interdisciplinary community on this important topic. Hopefully, this discussion will lead to more formal and accurate models of CAs that are better linked to neuropsychological data.

Rates and rhythms: a synergistic view of frequency and temporal coding in neuronal networks

In the CNS, activity of individual neurons has a small but quantifiable relationship to sensory representations and motor outputs. Coactivation of a few 10s to 100s of neurons can code sensory inputs and behavioral task performance within psychophysical limits. However, in a sea of sensory inputs and demand for complex motor outputs how is the activity of such small subpopulations of neurons organized? Two theories dominate in this respect: increases in spike rate (rate coding) and sharpening of the coincidence of spiking in active neurons (temporal coding). Both have computational advantages and are far from mutually exclusive. Here, we review evidence for a bias in neuronal circuits toward temporal coding and the coexistence of rate and temporal coding during population rhythm generation. The coincident expression of multiple types of gamma rhythm in sensory cortex suggests a mechanistic substrate for combining rate and temporal codes on the basis of stimulus strength.

An old hypothesis and new tools: Alfred Fessard's approach to the problem of consciousness

In 1954, a symposium was held in Canada on "Brain Mechanisms and Consciousness." It was a time for the promotion of international and interdisciplinary scientific cooperation, of new technological expectation, and of speculating about complex human behavior. Alfred Fessard's lecture on "Mechanisms of Nervous Integration and Conscious Experience" was one of the outstanding presentations, rich in critical analysis of the then available experimental data and in working hypothesis proposals. Reading the concept expressed by Fessard, it was found that several of his ideas had anticipated data obtained in modern research with new technologies.

A Hypothesis about the Biological Basis of Expert Intuition

It is well established that intuition plays an important role in experts’ decision making and thinking generally. However, the theories that have been developed at the cognitive level have limits in their explanatory power and lack detailed explanation of the underlying biological mechanisms. In this paper, we bridge this gap by proposing that Hebb's (1949) concept of cell assembly is the biological realization of Simon's (1974) concept of chunking. This view provides mechanisms at the biological level that are consistent with both biological and psychological findings. To further address the limits of previous theories, we introduce emotions as a component of intuition by showing how they modulate the perception-memory interaction. The idea that intuition lies at the crossroads between perception, knowledge, and emotional modulation sheds new light on the phenomena of expertise and intuition.

Neuronal assembly detection and cell membership specification by principal component analysis

In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership.

Interareal coordination of columnar architectures during visual cortical development

The formation of cortical columns is often conceptualized as a local process in which synaptic microcircuits confined to the volume of the emerging column are established and selectively refined. Many neurons, however, while wiring up locally are simultaneously building macroscopic circuits spanning widely distributed brain regions, such as different cortical areas or the two brain hemispheres. Thus, it is conceivable that interareal interactions shape the local column layout. Here we show that the columnar architectures of different areas of the cat visual cortex in fact develop in a coordinated manner, not adequately described as a local process. This is revealed by comparing the layouts of orientation columns (i) in left/right pairs of brain hemispheres and (ii) in areas V1 and V2 of individual brain hemispheres. Whereas the size of columns varied strongly within all areas considered, columns in different areas were typically closely matched in size if they were mutually connected. During development, we find that such mutually connected columns progressively become better matched in size as the late phase of the critical period unfolds. Our results suggest that one function of critical-period plasticity is to progressively coordinate the functional architectures of different cortical areas--even across hemispheres.

The nature of the memory trace and its neurocomputational implications

The brain processes underlying cognitive tasks must be very robust. Disruptions such as the destruction of large numbers of neurons, or the impact of alcohol and lack of sleep do not have negative effects except when they occur in an extreme form. This robustness implies that the parameters determining the functioning of networks of individual neurons must have large ranges or there must exist stabilizing mechanisms that keep the functioning of a network within narrow bounds. The simulation of a minimal neuronal architecture necessary to study cognitive tasks is described, which consists of a loop of three cell-assemblies. A crucial factor in this architecture is the critical threshold of a cell-assembly. When activated at a level above the critical threshold, the activation in a cell-assembly is subject to autonomous growth, which leads to an oscillation in the loop. When activated below the critical threshold, excitation gradually extinguishes. In order to circumvent the large parameter space of spiking neurons, a rate-dependent model of neuronal firing was chosen. The resulting parameter space of 12 parameters was explored by means of a genetic algorithm. The ranges of the parameters for which the architecture produced the required oscillations and extinctions, turned out to be relatively narrow. These ranges remained narrow when a stabilizing mechanism, controlling the total amount of activation, was introduced. The architecture thus shows chaotic behaviour. Given the overall stability of the operation of the brain, it can be concluded that there must exist other mechanisms that make the network robust. Three candidate mechanisms are discussed: synaptic scaling, synaptic homeostasis, and the synchronization of neural spikes.

Reliability, synchrony and noise

The brain is noisy. Neurons receive tens of thousands of highly fluctuating inputs and generate spike trains that appear highly irregular. Much of this activity is spontaneous - uncoupled to overt stimuli or motor outputs - leading to questions about the functional impact of this noise. Although noise is most often thought of as disrupting patterned activity and interfering with the encoding of stimuli, recent theoretical and experimental work has shown that noise can play a constructive role - leading to increased reliability or regularity of neuronal firing in single neurons and across populations. These results raise fundamental questions about how noise can influence neural function and computation.

Spectrotemporal analysis of evoked and induced electroencephalographic responses in primary auditory cortex (A1) of the awake monkey

Electroencephalography is increasingly being used to probe the functional organization of auditory cortex. Modulation of the electroencephalographic (EEG) signal by tones was examined in primary auditory cortex (A1) of awake monkeys. EEG data were measured at 4 laminar depths defined by current source density profiles evoked by best frequency (BF) tones. Midlaminar multiunit activity was used to define the tuning characteristics of A1 sites. Presentation of BF tones increased EEG power across the range of frequencies examined (4-290 Hz), with maximal effects evident within the first 100 ms after stimulus onset. The largest relative increases in EEG power generally occurred at very high gamma frequency bands (130-210 Hz). Increases in EEG power for frequencies less than 70 Hz primarily represented changes in phase-locked activity, whereas increases at higher frequencies primarily represented changes in non-phase-locked activity. Power increases in higher gamma bands were better correlated with the A1 tonotopic organization than power increases in lower frequency bands. Results were similar across the 4 laminar depths examined. These findings highlight the value of examining high-frequency EEG components in exploring the functional organization of auditory cortex and may enhance interpretation of related studies in humans.

Coherent oscillatory activity in monkey area v4 predicts successful allocation of attention

Attention serves to select objects from often complex scenes for enhanced processing and perception. In particular, the perception of shape depends critically on attention for integrating the various parts of the selected object into a coherent representation of object shape. To study whether oscillatory neuronal synchrony may serve as a mechanism of attention in shape perception, we introduced a novel shape-tracking task requiring sustained attention to a morphing shape. Attention was found to strongly increase oscillatory currents underlying the recorded field potentials in the gamma-frequency range, thus indicating enhanced neuronal synchrony within the population of V4 neurons representing the attended stimulus. Errors indicating a misdirection of attention to the distracter instead of the target were preceded by a corresponding shift of oscillatory activity from the target's neuronal representation to that of the distracter. No such effect was observed for errors unrelated to attention. Modulations of the attention-dependent enhancement of oscillatory activity occurred in correspondence with changing attentional demands during the course of a trial. The specificity of the effect of attentional errors together with the close coupling between attentional demand and oscillatory activity support the hypothesis that oscillatory neuronal synchrony serves as a mechanism of attention.

Neuroplasticity and schizophrenia

This article's title is also the name of a workshop sponsored by the International Congress on Schizophrenia Research that was focused on an appraisal of the potential role of neuroplastic processes in the etiology or course of schizophrenia. The workshop brought together clinical investigators of schizophrenia and basic scientists who study various aspects of neuroplasticity, including central nervous system (CNS) development, learning and memory, and drug action. The goal was to identify special opportunities to advance knowledge and understanding of schizophrenia pathology, treatment, or prevention by applying neuroplasticity concepts as a framework to theories of the illness. Although the focus of this workshop was schizophrenia, the phenomena considered are pertinent to other disorders, such as depression and drug abuse.

Organization of cell assemblies in the hippocampus

Neurons can produce action potentials with high temporal precision. A fundamental issue is whether, and how, this capability is used in information processing. According to the 'cell assembly' hypothesis, transient synchrony of anatomically distributed groups of neurons underlies processing of both external sensory input and internal cognitive mechanisms. Accordingly, neuron populations should be arranged into groups whose synchrony exceeds that predicted by common modulation by sensory input. Here we find that the spike times of hippocampal pyramidal cells can be predicted more accurately by using the spike times of simultaneously recorded neurons in addition to the animals location in space. This improvement remained when the spatial prediction was refined with a spatially dependent theta phase modulation. The time window in which spike times are best predicted from simultaneous peer activity is 10-30 ms, suggesting that cell assemblies are synchronized at this timescale. Because this temporal window matches the membrane time constant of pyramidal neurons, the period of the hippocampal gamma oscillation and the time window for synaptic plasticity, we propose that cooperative activity at this timescale is optimal for information transmission and storage in cortical circuits.

Functional implications of temporal structure in primate cortical information processing

Processing of information in the cerebral cortex of primates is characterized by distributed representations and processing in neuronal assemblies rather than by detector neurons, cardinal cells or command neurons. Responses of individual neurons in sensory cortical areas contain limited and ambiguous information on common features of the natural environment which is disambiguated by comparison with the responses of other, related neurons. Distributed representations are also capable to represent the enormous complexity and variability of the natural environment by the large number of possible combinations of neurons that can engage in the representation of a stimulus or other content. A critical problem of distributed representation and processing is the superposition of several assemblies activated at the same time since interpretation and processing of a population code requires that the responses related to a single representation can be identified and distinguished from other, related activity. A possible mechanism which tags related responses is the synchronization of neuronal responses of the same assembly with a precision in the millisecond range. This mechanism also supports the separate processing of distributed activity and dynamic assembly formation. Experimental evidence from electrophysiological investigations of non-human primates and human subjects shows that synchronous activity can be found in visual, auditory and motor areas of the cortex. Simultaneous recordings of neurons in the visual cortex indicate that individual neurons synchronize their activity with each other, if they respond to the same stimulus but not if they are part of different assemblies representing different contents. Furthermore, evidence for synchronous activity related to perception, expectation, memory, and attention has been observed.