Time as Coding Space in Neocortical Processing: A Hypothesis

The influence of synaptic pathways on the synchronization patterns of regularly structured mChialvo map network

Synchronization of interconnecting units is one of the hottest topics many researchers are interested in. In addition, this emerging phenomenon is responsible for many biological processes, and thus, the synchronization of interacting neurons is an important field of study in neuroscience. Employing the memristive Chialvo (mChialvo) neuron map, this paper investigates the effect of electrical, inner-linking, chemical, and hybrid coupling functions on the synchronization state of a neuronal network with regular structure. Master stability function (MSF) analysis is performed to obtain the necessary conditions for synchronizing the built networks. Afterward, the MSF-based results are confirmed by calculating the synchronization error. Besides, the dynamics of the synchronous neurons are discussed based on the bifurcation analysis. Our results suggest that, compared to the electrical and inner-linking functions, chemical synapses facilitate mChialvo neurons' synchronization since the neurons can achieve synchrony with a negligible chemical coupling strength. Further studies reveal that based on the active synapses, coupled mChialvo neurons can reach cluster synchronization, chimera state, sine-like synchronization, phase synchronization, and cluster phase synchronization.

Neuronal selectivity for spatial positions of offers and choices in five reward regions

When we evaluate an option, how is the neural representation of its value linked to information that identifies it, such as its position in space? We hypothesized that value information and identity cues are not bound together at a particular point but are represented together at the single unit level throughout the entirety of the choice process. We examined neuronal responses in two-option gambling tasks with lateralized and asynchronous presentation of offers in five reward regions: orbitofrontal cortex (OFC, area 13), ventromedial prefrontal cortex (vmPFC, area 14), ventral striatum (VS), dorsal anterior cingulate cortex (dACC), and subgenual anterior cingulate cortex (sgACC, area 25). Neuronal responses in all areas are sensitive to the positions of both offers and of choices. This selectivity is strongest in reward-sensitive neurons, indicating that it is not a property of a specialized subpopulation of cells. We did not find consistent contralateral or any other organization to these responses, indicating that they may be difficult to detect with aggregate measures like neuroimaging or studies of lesion effects. These results suggest that value coding is wed to factors that identify the object throughout the reward system and suggest a possible solution to the binding problem raised by abstract value encoding schemes.

Solving the Binding Problem with Separated Extraction of Information by Oscillatory Self-Organizing Maps

One solution to the binding problem is the hypothesis that property binding should be represented by phaselocking among neuronal oscillatory firing. We introduce this synchronous firing hypothesis into the selforganizing maps, or SOMs. Here we propose oscillatory self-organizing maps. Using the computer simulation, we show that our model composed of the oscillatory SOMs can separate and extract information of the shapes and the colors from two simultaneous inputs, solving the binding problem.

TrkB signaling in parvalbumin-positive interneurons is critical for gamma-band network synchronization in hippocampus

Although brain-derived neurotrophic factor (BDNF) is known to regulate circuit development and synaptic plasticity, its exact role in neuronal network activity remains elusive. Using mutant mice (TrkB-PV(-/-)) in which the gene for the BDNF receptor, tyrosine kinase B receptor (trkB), has been specifically deleted in parvalbumin-expressing, fast-spiking GABAergic (PV+) interneurons, we show that TrkB is structurally and functionally important for the integrity of the hippocampal network. The amplitude of glutamatergic inputs to PV+ interneurons and the frequency of GABAergic inputs to excitatory pyramidal cells were reduced in the TrkB-PV(-/-) mice. Functionally, rhythmic network activity in the gamma-frequency band (30-80 Hz) was significantly decreased in hippocampal area CA1. This decrease was caused by a desynchronization and overall reduction in frequency of action potentials generated in PV+ interneurons of TrkB-PV(-/-) mice. Our results show that the integration of PV+ interneurons into the hippocampal microcircuit is impaired in TrkB-PV(-/-) mice, resulting in decreased rhythmic network activity in the gamma-frequency band.

Neurophysiology and neuroanatomy of pitch perception: auditory cortex

We present original results and review literature from the past fifty years that address the role of primate auditory cortex in the following perceptual capacities: (1) the ability to perceive small differences between the pitches of two successive tones; (2) the ability to perceive the sign (i.e., direction) of the pitch difference [higher (+) vs. lower (-)]; and (3) the ability to abstract pitch constancy across changes in stimulus acoustics. Cortical mechanisms mediating pitch perception are discussed with respect to (1) gross and microanatomical distribution; and (2) candidate neural coding schemes. Observations by us and others suggest that (1) frequency-selective neurons in primary auditory cortex (A1) and surrounding fields play a critical role in fine-grained pitch discrimination at the perceptual level; (2) cortical mechanisms that detect pitch differences are neuroanatomically dissociable from those mediating pitch direction discrimination; (3) cortical mechanisms mediating perception of the "missing fundamental frequency (F0)" are neuroanatomically dissociable from those mediating pitch perception when F0 is present; (4) frequency-selective neurons in both right and left A1 contribute to pitch change detection and pitch direction discrimination; (5) frequency-selective neurons in right A1 are necessary for normal pitch direction discrimination; (6) simple codes for pitch that are based on single- and multiunit firing rates of frequency-selective neurons face both a "hyperacuity problem" and a "pitch constancy problem"-that is, frequency discrimination thresholds for pitch change direction and pitch direction discrimination are much smaller than neural tuning curves predict, and firing rate patterns change dramatically under conditions in which pitch percepts remain invariant; (7) cochleotopic organization of frequency-selective neurons bears little if any relevance to perceptual acuity and pitch constancy; and (8) simple temporal codes for pitch capable of accounting for pitches higher than a few hundred hertz have not been found in the auditory cortex. The cortical code for pitch is therefore not likely to be a function of simple rate profiles or synchronous temporal patterns. Studies motivated by interest in the neurophysiology and neuroanatomy of music perception have helped correct longstanding misconceptions about the functional role of auditory cortex in frequency discrimination and pitch perception. Advancing knowledge about the neural coding of pitch is of fundamental importance to the future design of neurobionic therapies for hearing loss.

Brain function, nonlinear coupling, and neuronal transients

The brain can be regarded as an ensemble of connected dynamical systems and as such conforms to some simple principles relating the inputs and outputs of its constituent parts. The ensuing implications, for the way we think about, and measure, neuronal interactions, can be quite profound. These range from 1) implications for which aspects of neuronal activity are important to measure and how to characterize coupling among neuronal populations; 2) implication for understanding the emergence of dynamic receptive fields and functionally specialized brain architectures; and 3) teleological implications pertaining to the genesis of dynamic instability and complexity, which is necessary for adaptive self-organization. This review focuses on the first set of implications by looking at neuronal interactions, coupling, and implicit neuronal codes from a dynamical perspective. By considering the brain in this light, one can show that a sufficient description of neuronal activity must comprise activity at the current time and its recent history. This history constitutes a neuronal transient. Such transients represent an essential metric of neuronal interactions and, implicitly, a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in different neuronal populations, reflects the underlying coupling among brain systems. A complete description of this coupling, or effective connectivity, can be expressed in terms of generalized convolution kernels (Volterra kernels) that embody high-order or nonlinear interactions. This coupling may be synchronous, and possibly oscillatory, or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays an important role in functional integration.

The labile brain. I. Neuronal transients and nonlinear coupling

In this, the first of three papers, the nature of, and motivation for, neuronal transients is described in relation to characterizing brain dynamics. This paper deals with some basic aspects of neuronal dynamics, interactions, coupling and implicit neuronal codes. The second paper develops neuronal transients and nonlinear coupling in the context of dynamic instability and complexity, and suggests that instability or lability is necessary for adaptive self-organization. The final paper addresses the role of neuronal transients through information theory and the emergence of spatio-temporal receptive fields and functional specialization. By considering the brain as an ensemble of connected dynamic systems one can show that a sufficient description of neuronal dynamics comprises neuronal activity at a particular time and its recent history This history constitutes a neuronal transient. As such, transients represent a fundamental metric of neuronal interactions and, implicitly, a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in distinct neuronal populations, reflects the underlying coupling among populations. This coupling may be synchronous (and possibly oscillatory) or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays a role in functional integration that may be as important as synchronous interactions. The distinction between linear and nonlinear coupling has fundamental implications for the analysis and characterization of neuronal interactions, most of which are predicated on linear (synchronous) coupling (e.g. cross-correlograms and coherence). Using neuromagnetic data it is shown that nonlinear (asynchronous) coupling is, in fact, more abundant and can be more significant than synchronous coupling.

Autoregressive modeling of the EEG in systemic kainic acid-induced epileptogenesis

Background activity as well as three kinds of bilateral epileptiform discharges, recorded from the cerebral cortex and hippocampus of freely behaving rats treated with intravenous kainic acid (KA), were analysed by the directed transfer function (DTF) method within multivariate autoregressive modeling of the EEG. This method reveals statistical influence (flow of activity) between brain regions at different frequencies. There was no significant influence between rhythms in different brain regions in the background EEG. Early after KA administration, low frequency rhythms (< 10Hz) in the frontal cortex began to lead slow rhythms in other areas and high frequency rhythms (20-60 Hz), possibly gamma oscillations, intensified in the hippocampus. In spike-wave discharges, frontal cortex led both low and high frequency rhythms. Initially during generalised non-convulsive discharges, slow rhythms originated from frontal cortex and high frequency rhythms from hippocampus while later, slow rhythms as well, often arose from hippocampus. During the convulsive discharge, the flow of activity of dominant slow rhythms repeatedly changed between hippocampus and neocortex, with more frequent dominance of the hippocampus, while hippocampus continued to lead high frequency rhythms. We conclude that KA-induced epileptiform discharges are cortical and hippocampal events, specifically that the frontal cortex is early to express low frequency rhythms and the hippocampus, high frequency rhythms. More generally, the findings suggest that epileptiform discharges result from interacting rhythms of different frequencies that arise from different structures, and that gamma oscillations possibly contribute to widespread synchronisation during some forms of epileptogenesis.

Frequency analysis of the EEG during spatial selective attention

In this study, we recorded the event-related potentials (ERPs) elicited by stimuli appearing at attended and unattended locations. The voltage amplitudes and latencies of the P1, N1, P2, N2 and P3 visual components showed statistically significant differences in the attended condition with respect to the unattended one. The power spectral density of the EEG following stimulus onset was calculated. The difference between the spectral densities of the attended and unattended conditions was computed. Statistically significant differences were found in the decrease of alpha (9-11 Hz) and the increase of beta (15-17 Hz) frequencies during the attention condition with respect to the unattended condition. These results suggest that the arrival of a visual stimulus during the attended condition generates a complex reorganization of neuronal activity in both time and frequency domains.

Dynamic binding of visual features by neuronal/stimulus synchrony

When people see a visual scene, certain parts of the visual scene are treated as belonging together and we regard them as a perceptual unit, which is called a "figure". People focus on figures, and the remaining parts of the scene are disregarded as "ground". In Gestalt psychology this process is called "figure-ground segregation". According to current perceptual psychology, a figure is formed by binding various visual features in a scene, and developments in neuroscience have revealed that there are many feature-encoding neurons, which respond to such features specifically. It is not known, however, how the brain binds different features of an object into a coherent visual object representation. Recently, the theory of binding by neuronal synchrony, which argues that feature binding is dynamically mediated by neuronal synchrony of feature-encoding neurons, has been proposed. This review article portrays the problem of figure-ground segregation and features binding, summarizes neurophysiological and psychophysical experiments and theory relevant to feature binding by neuronal/stimulus synchrony, and suggests possible directions for future research on this topic.

Keeping in mind the mind: mental functions, networks and neurosurgery

The object of the neurosurgeons daily endeavour, the human brain, is less well understood in its overall organization than any other organ. This puts the neurosurgeon in a very difficult position. However, a substantial body of knowledge has been accumulated during recent years, and scientists from a variety of different disciplines have worked out theoretical frameworks to accomodate the available data. Here we present some of the evolving concepts on the organization of the substrate of the mind. Review of the literature shows that application of mathematical neural network models to the nervous system is very successful in explaining function. An implicit aspect of neural network models is that information storage is not localized in certain neurons but that the information is stored as the global pattern of activity in the network. Because networks of the brain involve often millions of neurons, exact identification and comparison with the theoretical models is not possible today.