Synchronization of neural activity is a promising mechanism of memory information processing in networks of columns

Synchronization of the oscillatory discharge of cortical neurons could be a part of the mechanism that is involved in cortical information processing. On the assumption that the basic functional unit is the column composed of local excitatory and inhibitory cells and generating oscillatory neural activity, a network model that attains associative memory function is proposed. The synchronization of oscillation in the model is studied analytically using a sublattice analysis. In particular, the retrieval of a single memory pattern can be studied in the system, which can be derived from the original network model of interacting columns and is formally equivalent to a system of an isolated column. The network model simulated numerically shows a remarkable performance in which retrieval is achieved simultaneously for more than one memory pattern. The manifestations of this simultaneous retrieval in the network dynamics are successive transitions of the network state from a synchronized oscillation for a memory pattern to that for another memory pattern.     

Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness

Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden “ignition” of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of “inattentional blindness,” in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness.     

Negatively correlated firing: the functional meaning of lateral inhibition within cortical columns

Lateral inhibition is a well documented aspect of neural architecture in the main sensory systems. Existing accounts of lateral inhibition focus on its role in sharpening distinctions between inputs that are closely related. However, these accounts fail to explain the functional role of inhibition in cortical columns, such as those in V1, where neurons have similar response properties. In this paper, we outline a model of position tracking using cortical columns of integrate-and-fire and Hodgkin-Huxley-type neurons which respond optimally to a particular location, to show that negatively correlated firing patterns arise from lateral inhibition in cortical columns and that this provides a clear benefit for population coding in terms of stability, accuracy, estimation time and neural resources.     

Clustered organization of neurons with similar extra-receptive field properties in the primary visual cortex

The primary visual cortex is organized into clusters of cells having similar classical receptive field (CRF) properties. Nonclassical, extra-receptive fields (ERFs) can either inhibit or facilitate the response elicited by stimulation within the CRF. Here, we report that in the primary visual cortex of cat, neurons with similar inhibitory or facilitatory ERF properties are also grouped into clusters. These clusters are randomly distributed in all cortical layers, with no detectable relationship with orientation and ocular dominance columns. This functional organization of neurons with respect to ERF properties may allow an efficient processing of global visual information.     

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.     

From functional architecture to functional connectomics

"Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex" by Hubel and Wiesel (1962) reported several important discoveries: orientation columns, the distinct structures of simple and complex receptive fields, and binocular integration. But perhaps the paper's greatest influence came from the concept of functional architecture (the complex relationship between in?vivo physiology and the spatial arrangement of neurons) and several models of functionally specific connectivity. They thus identified two distinct concepts, topographic specificity and functional specificity, which together with cell-type specificity constitute the major determinants of nonrandom cortical connectivity. Orientation columns are iconic examples of topographic specificity, whereby axons within a column connect with cells of a single orientation preference. Hubel and Wiesel also saw the need for functional specificity at a finer scale in their model of thalamic inputs to simple cells, verified in the 1990s. The difficult but potentially more important question of functional specificity between cortical neurons is only now becoming tractable with new experimental techniques.     

Matching the modules: cortical maps and long-range intrinsic connections in visual cortex during development.

Visual cortical neurons exhibit a high degree of response selectivity and are grouped into small columns according to their response preferences. The columns are located at regularly spaced intervals covering the whole cortical representation of the visual field with a modular system of feature-selective neurons. The selectivity of these cells and their modular arrangement is thought to emerge from interactions in the network of specific intracortical and thalamocortical connections. Understanding the ontogenesis of this complex structure and contributions of intrinsic and extrinsic, experience-dependent mechanisms during cortical development can provide new insights into the way the visual cortex processes information about the environment. Available data about the development of connections and response properties in the visual cortex suggest that maturation proceeds in two distinct steps. In the first phase, mechanisms inherent to the cortex establish a crude framework of interconnected neural modules which exhibit the basic but still immature traits of the adult state. Relevant mechanisms in this phase are assumed to consist of molecular cues and patterns of spontaneous neural activity in cortical and corticothalamic interconnections. In a second phase, the primordial layout becomes refined under the control of visual experience establishing a fine-tuned network of connections and mature response properties.     

A zonal model of cortical functions

A model of cortical functions is developed with the object of simulating the observed behavior of individual neurons organized in unit circuits and functional systems of the cerebellum, the cerebrum and the hippocampal formation. The neuronal model is capable of representing refractory and potentiated states, as well as the firing and lowest resting states. The unit circuits of each system consist of all common types of cells with known synaptic connections. In the cerebral system these unit circuits are interconnected to form columns as well as zones. A new discrete neural network equation, which takes account of interactions with the extracellular field, is proposed to simulate electrical activity in these circuits. A coherent theory of cortical activity and functions is derived that accounts for many of the observed phenomena, including those associated with the development of long-term potentiation and sequential memory. Three appendices are devoted to the theory of extracellular interactions, the derivation of non-linear network equations, and a computer program to simulate learning in the cortex.     

Dendritic slow dynamics enables localized cortical activity to switch between mobile and immobile modes with noisy background input

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity--called a bump--can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability.     

A network model for activity-dependent sleep regulation

We develop and characterize a dynamical network model for activity-dependent sleep regulation. Specifically, in accordance with the activity-dependent theory for sleep, we view organism sleep as emerging from the local sleep states of functional units known as cortical columns; these local sleep states evolve through integration of local activity inputs, loose couplings with neighboring cortical columns, and global regulation (e.g. by the circadian clock). We model these cortical columns as coupled or networked activity-integrators that transition between sleep and waking states based on thresholds on the total activity. The model dynamics for three canonical experiments (which we have studied both through simulation and system-theoretic analysis) match with experimentally observed characteristics of the cortical-column network. Most notably, assuming connectedness of the network graph, our model predicts the recovery of the columns to a synchronized state upon temporary overstimulation of a single column and/or randomization of the initial sleep and activity-integration states. In analogy with other models for networked oscillators, our model also predicts the possibility for such phenomena as mode-locking.     

Relationship of correlated spontaneous activity to functional ocular dominance columns in the developing visual cortex

Utilizing a multielectrode array to record spontaneous and visually evoked activity of cortical neurons in area 17, we investigate the relationship between long-range correlated spontaneous activity and functional ocular dominance columns during early ferret postnatal development (P24-P29). In regions of visual cortex containing alternating ocular dominance patches, periodic fluctuations in correlated activity are observed in which spontaneous activity is most highly correlated between cortical patches exhibiting the same eye preference. However, these fluctuations are present even within large contralateral eye-dominated bands which lack any periodic alternations in ocular dominance. Thus, the organization of ocular dominance columns cannot fully account for the patterns of correlated activity we observe. Our results suggest that patterns of long-range correlated activity reflect an intrinsic periodicity of cortical connectivity that is constrained by segregated eye-specific LGN afferents.     

Neuronal correlates of corticalization in mammals: a theory

The cerebral cortex of mammals has been found to be uniformly organized, and to be composed of elementary processing units or modules having an essentially constant number of neurons. In the present paper the hypothesis is put forward that the relative proportion of local circuit neurons (LCNs) within a module reflects the evolutionary level of corticalization of a mammal. The modules, in turn, are interconnected so as to form basic neuronal networks or columns with a species-specific width varying from 90 to 310 microns. A mathematical formalism is presented from which the hypothetical ratio between LCNs and projection neurons, as well as the size of the cortical column and the number of modular units that it contains, can be calculated.     

Functional topography of corticothalamic feedback enhances thalamic spatial response tuning in the somatosensory whisker/barrel system

Corticothalamic (CT) projections are approximately 10 times more numerous than thalamocortical projections, yet their function in sensory processing is poorly understood. In particular, the functional significance of the topographic precision of CT feedback is unknown. We addressed these issues in the rodent somatosensory whisker/barrel system by deflecting individual whiskers and pharmacologically enhancing activity in layer VI of single whisker-related cortical columns. Enhancement of corticothalamic activity in a cortical column facilitated whisker-evoked responses in topographically aligned thalamic barreloid neurons, while activation of an adjacent column weakly suppressed activity at the same thalamic site. Both effects were more pronounced when stimulating the preferred, or principal, whisker than for adjacent whiskers. Thus, facilitation by homologous CT feedback sharpens thalamic receptive field focus, while suppression by nonhomologous feedback diminishes it. Our findings demonstrate that somatosensory cortex can selectively regulate thalamic spatial response tuning by engaging topographically specific excitatory and inhibitory mechanisms in the thalamus.     

Organizational levels of the cerebral cortex: An integrated model

We propose a theoretical model of the cerebral cortex which is based on its cellular components and integrates its different levels of organization: (1) cells have general adaptive and memorization properties; (2) cortical columns are repetitive interneuronal circuits which determine an adaptive processing specific to the cerebral cortex; (3) cortical maps effect selective combinations which are very efficient to learn basic behaviourial adaptations such as invariant recognition of forms, visually-guided hand movements, or execution of structured motor programs; (4) the network between cortical areas has a global architecture which integrates successive learning experiences into coherent functions such as the human language.     

Chaos and synchrony in a model of a hypercolumn in visual cortex

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This behavior is in marked contrast to the behavior of cortical neurons in vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show a significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a hypercolumn in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model of excitable voltage-clamped cells with several cellular and synaptic conductances. A slow potassium current is included in the dynamics of the excitatory population to reproduce the observed adaptation of the spike trains emitted by these neurons. The pattern of connectivity has a spatial structure which is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. These results show that the cooperative dynamics of large neuronal networks are capable of generating variability and synchrony similar to those observed in cortex. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and their temporal and spatial features are analyzed. In other parameter regimes, the network exhibits two additional states: synchronized oscillations and an asynchronous state. We use our model to study cortical mechanisms for orientation selectivity. It is shown that in a suitable parameter regime, when the input is not oriented, the network has a continuum of states, each representing an inhomogeneous population activity which is peaked at one of the orientation columns. As a result, when a weakly oriented input stimulates the network, it yields a sharp orientation tuning. The properties of the network in this regime, including the appearance of virtual rotations and broad stimulus-dependent cross-correlations, are investigated. The results agree with the predictions of the mean field theory which was previously derived for a simplified model of stochastic, two-state neurons. The relation between the results of the model and experiments in visual cortex are discussed.     

Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways

Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture.     

Binaural and frequency representation in the primary auditory cortex of the big brown bat, Eptesicus fuscus

This study examines the binaural and frequency representation in the primary auditory cortex (AC) of the big brown bat, Eptesicus fuscus, by using an ear-phone stimulation system. All 306 cortical neurons studied were excited by contralateral sound stimulation but they were either excited, inhibited or not affected by ipsilateral sound stimulation. These cortical neurons were columnarly organized according to their binaural and frequency-tuning properties. The excitation-excitation columns which occupy about 15% of the AC are mainly aggregated within an oval-shaped area of the central AC. The excitation-inhibition neurons and binaural neurons with mixed properties are distributed in the remaining 85% of the surrounding primary AC. Although the best frequency (BF) of these neurons shows a tendency to decrease from high to low along the anteroposterior axis of the primary AC, systematic variation in BF is not always consistent across the entire mapping area. In particular, BFs of cortical neurons isolated in the anterior AC vary quite unsystematically such that neurons with similar BFs are aggregated in isolated patches. Isofrequency and binaural columns are segregated into bands that intersect each other. Accepted: 13 August 1997     

Axonal Dynamics of Excitatory and Inhibitory Neurons in Somatosensory Cortex

Cortical topography can be remapped as a consequence of sensory deprivation, suggesting that cortical circuits are continually modified by experience. To see the effect of altered sensory experience on specific components of cortical circuits, we imaged neurons, labeled with a genetically modified adeno-associated virus, in the intact mouse somatosensory cortex before and after whisker plucking. Following whisker plucking we observed massive and rapid reorganization of the axons of both excitatory and inhibitory neurons, accompanied by a transient increase in bouton density. For horizontally projecting axons of excitatory neurons there was a net increase in axonal projections from the non-deprived whisker barrel columns into the deprived barrel columns. The axon collaterals of inhibitory neurons located in the deprived whisker barrel columns retracted in the vicinity of their somata and sprouted long-range projections beyond their normal reach towards the non-deprived whisker barrel columns. These results suggest that alterations in the balance of excitation and inhibition in deprived and non-deprived barrel columns underlie the topographic remapping associated with sensory deprivation.     

Altered Network Communication Following a Neuroprotective Drug Treatment

Preconditioning is defined as a range of stimuli that allow cells to withstand subsequent anaerobic and other deleterious conditions. While cell protection under preconditioning is well established, this paper investigates the influence of neuroprotective preconditioning drugs, 4-aminopyridine and bicuculline (4-AP/bic), on synaptic communication across a broad network of in vitro rat cortical neurons. Using a permutation test, we evaluated cross-correlations of extracellular spiking activity across all pairs of recording electrodes on a 64-channel multielectrode array. The resulting functional connectivity maps were analyzed in terms of their graph-theoretic properties. A small-world effect was found, characterized by a functional network with high clustering coefficient and short average path length. Twenty-four hours after exposure to 4-AP/bic, small-world properties were comparable to control cultures that were not treated with the drug. Four hours following drug washout, however, the density of functional connections increased, while path length decreased and clustering coefficient increased. These alterations in functional connectivity were maintained at four days post-washout, suggesting that 4-AP/bic preconditioning leads to long-term effects on functional networks of cortical neurons. Because of their influence on communication efficiency in neuronal networks, alterations in small-world properties hold implications for information processing in brain systems. The observed relationship between density, path length, and clustering coefficient is captured by a phenomenological model where connections are added randomly within a spatially-embedded network. Taken together, results provide information regarding functional consequences of drug therapies that are overlooked in traditional viability studies and present the first investigation of functional networks under neuroprotective preconditioning.