Synchronization in neuronal transmission and its importance for information processing

A Nanoscale Bistable Resistor for an Oscillatory Neural Network

Coupled oscillators construct an oscillatory neural network (ONN) by mimicking the interactions among neurons in the human brain. This work demonstrates a fully CMOS-based oscillator consisting of a bistable resistor (biristor), which shares a structure identical with that of a metal-oxide-semiconductor field-effect transistor, except for the use of a gate electrode. The biristor-based oscillator (birillator) generates oscillating voltage signals in the form of spikes due to a single transistor latch phenomenon. When two birillators are connected with a coupling capacitor, they become synchronized with a phase difference of 180°. These coupled oscillation characteristics are experimentally investigated for an ONN. As practical applications of the ONN with coupled birillators, edge detection and vertex coloring are conducted by encoding information into phase differences between them. The proposed fully CMOS-based birillators are advantageous for low power consumption, high CMOS compatibility, and a compact footprint area.

New Criteria for Synchronization of Multilayer Neural Networks via Aperiodically Intermittent Control

In this paper, the globally asymptotic synchronization of multi-layer neural networks is studied via aperiodically intermittent control. Due to the property of intermittent control, it is very hard to deal with the effect of time-varying delays and ascertain the control and rest widths for intermittent control. A new lemma with generalized Halanay-type inequalities are proposed first. Then, by constructing a new Lyapunov–Krasovskii functional and utilizing linear programming methods, several useful criteria are derived to ensure the multilayer neural networks achieve asymptotic synchronization. Moreover, an aperiodically intermittent control is designed, which has no direct relationship with control widths and rest widths and extends existing aperiodically intermittent control techniques, the control gains are designed by solving the linear programming. Finally, a numerical example is provided to confirm the effectiveness of the proposed theoretical results.

Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control With Actuator Fault

This article considers global exponential synchronization almost surely (GES a.s.) for a class of switched discrete-time neural networks (DTNNs). The considered system switches from one mode to another according to transition probability (TP) and evolves with mode-dependent average dwell time (MDADT), i.e., TP-based MDADT switching, which is more practical than classical average dwell time (ADT) switching. The logarithmic quantization technique is utilized to design mode-dependent quantized output controllers (QOCs). Noticing that external perturbations are unavoidable, actuator fault (AF) is also considered. New Lyapunov-Krasovskii functionals and analytical techniques are developed to obtain sufficient conditions to guarantee the GES a.s. It is discovered that the TP matrix plays an important role in achieving the GES a.s., the upper bound of the dwell time (DT) of unsynchronized subsystems can be very large, and the lower bound of the DT of synchronized subsystems can be very small. An algorithm is given to design the control gains, and an optimal algorithm is provided for reducing conservatism of the given results. Numerical examples demonstrate the effectiveness and the merits of the theoretical analysis.

A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices by using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.

Fixed-Time Synchronization of Coupled Neural Networks With Discontinuous Activation and Mismatched Parameters

This article is concerned with fixed-time synchronization of the nonlinearly coupled neural networks with discontinuous activation and mismatched parameters. First, a novel lemma is proposed to study fixed-time stability, which is less conservative than those in most existing results. Then, based on the new lemma, a discontinuous neural network with mismatched parameters will synchronize to the target state within a settling time via two kinds of unified and simple controllers. The settling time is theoretically estimated, which is independent of the initial values of the considered network. In particular, the estimated settling time is closer to the real synchronization time than those given in the existing literature. Finally, two numerical simulations are presented to illustrate the effectiveness and correctness of our results.

H∞ synchronization of delayed neural networks via event-triggered dynamic output control

This paper investigates H_∞ exponential synchronization (ES) of neural networks (NNs) with delay by designing an event-triggered dynamic output feedback controller (ETDOFC). The ETDOFC is flexible in practice since it is applicable to both full order and reduced order dynamic output techniques. Moreover, the event generator reduces the computational burden for the zero-order-hold (ZOH) operator and does not induce sampling delay as many existing event generators do. To obtain less conservative results, the delay-partitioning method is utilized in the Lyapunov-Krasovskii functional (LKF). Synchronization criteria formulated by linear matrix inequalities (LMIs) are established. A simple algorithm is provided to design the control gains of the ETDOFC, which overcomes the difficulty induced by different dimensions of the system parameters. One numerical example is provided to demonstrate the merits of the theoretical analysis.

The Continuum Between Temperament and Mental Illness as Dynamical Phases and Transitions

The full range of biopsychosocial complexity is mind-boggling, spanning a vast range of spatiotemporal scales with complicated vertical, horizontal, and diagonal feedback interactions between contributing systems. It is unlikely that such complexity can be dealt with by a single model. One approach is to focus on a narrower range of phenomena which involve fewer systems but still cover the range of spatiotemporal scales. The suggestion is to focus on the relationship between temperament in healthy individuals and mental illness, which have been conjectured to lie along a continuum of neurobehavioral regulation involving neurochemical regulatory systems (e.g., monoamine and acetylcholine, opiate receptors, neuropeptides, oxytocin), and cortical regulatory systems (e.g., prefrontal, limbic). Temperament and mental illness are quintessentially dynamical phenomena, and need to be addressed in dynamical terms. A meteorological metaphor suggests similarities between temperament and chronic mental illness and climate, between individual behaviors and weather, and acute mental illness and frontal weather events. The transition from normative temperament to chronic mental illness is analogous to climate change. This leads to the conjecture that temperament and chronic mental illness describe distinct, high level, dynamical phases. This suggests approaching biopsychosocial complexity through the study of dynamical phases, their order and control parameters, and their phase transitions. Unlike transitions in physical systems, these biopsychosocial phase transitions involve information and semiotics. The application of complex adaptive dynamical systems theory has led to a host of markers including geometrical markers (periodicity, intermittency, recurrence, chaos) and analytical markers such as fluctuation spectroscopy, scaling, entropy, recurrence time. Clinically accessible biomarkers, in particular heart rate variability and activity markers have been suggested to distinguish these dynamical phases and to signal the presence of transitional states. A particular formal model of these dynamical phases will be presented based upon the process algebra, which has been used to model information flow in complex systems. In particular it describes the dual influences of energy and information on the dynamics of complex systems. The process algebra model is well-suited for dealing with the particular dynamical features of the continuum, which include transience, contextuality, and emergence. These dynamical phases will be described using the process algebra model and implications for clinical practice will be discussed.

A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble

The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.

Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate

Reliable propagation of slow-modulations of the firing rate across multiple layers of a feedforward network (FFN) has proven difficult to capture in spiking neural models. In this paper, we explore necessary conditions for reliable and stable propagation of time-varying asynchronous spikes whose instantaneous rate of changes-in fairly short time windows [20-100] msec-represents information of slow fluctuations of the stimulus. Specifically, we study the effect of network size, level of background synaptic noise, and the variability of synaptic delays in an FFN with all-to-all connectivity. We show that network size and the level of background synaptic noise, together with the strength of synapses, are substantial factors enabling the propagation of asynchronous spikes in deep layers of an FFN. In contrast, the variability of synaptic delays has a minor effect on signal propagation.

Cross-hemispheric gamma synchrony between prefrontal parvalbumin interneurons supports behavioral adaptation during rule shift learning

Organisms must learn novel strategies to adapt to changing environments. Activity in different neurons often exhibits synchronization that can dynamically enhance their communication and might create flexible brain states that facilitate changes in behavior. We studied the role of gamma-frequency (~40 Hz) synchrony between prefrontal parvalbumin interneurons, in mice learning multiple new cue-reward associations. Voltage indicators revealed cell type-specific increases of cross-hemispheric gamma synchrony between parvalbumin interneurons, when mice received feedback that previously learned associations were no longer valid. Disrupting this synchronization by delivering out-of-phase optogenetic stimulation caused mice to perseverate on outdated associations, an effect not reproduced by in-phase stimulation or out-of-phase stimulation at other frequencies. Gamma synchrony was specifically required when new associations utilized familiar cues that were previously irrelevant to behavioral outcomes, not when associations involved novel cues, or for reversing previously learned associations. Thus, gamma synchrony is indispensable for reappraising the behavioral salience of external cues.

Further information on research design is available in the Life Sciences Reporting Summary linked to this article.

PV+ Cells Enhance Temporal Population Codes but not Stimulus-Related Timing in Auditory Cortex

Spatio-temporal cortical activity patterns relative to both peripheral input and local network activity carry information about stimulus identity and context. GABAergic interneurons are reported to regulate spiking at millisecond precision in response to sensory stimulation and during gamma oscillations; their role in regulating spike timing during induced network bursts is unclear. We investigated this issue in murine auditory thalamo-cortical (TC) brain slices, in which TC afferents induced network bursts similar to previous reports in vivo. Spike timing relative to TC afferent stimulation during bursts was poor in pyramidal cells and SOM+ interneurons. It was more precise in PV+ interneurons, consistent with their reported contribution to spiking precision in pyramidal cells. Optogenetic suppression of PV+ cells unexpectedly improved afferent-locked spike timing in pyramidal cells. In contrast, our evidence suggests that PV+ cells do regulate the spatio-temporal spike pattern of pyramidal cells during network bursts, whose organization is suited to ensemble coding of stimulus information. Simulations showed that suppressing PV+ cells reduces the capacity of pyramidal cell networks to produce discriminable spike patterns. By dissociating temporal precision with respect to a stimulus versus internal cortical activity, we identified a novel role for GABAergic cells in regulating information processing in cortical networks.

Fast and flexible sequence induction in spiking neural networks via rapid excitability changes

Cognitive flexibility likely depends on modulation of the dynamics underlying how biological neural networks process information. While dynamics can be reshaped by gradually modifying connectivity, less is known about mechanisms operating on faster timescales. A compelling entrypoint to this problem is the observation that exploratory behaviors can rapidly cause selective hippocampal sequences to 'replay' during rest. Using a spiking network model, we asked whether simplified replay could arise from three biological components: fixed recurrent connectivity; stochastic 'gating' inputs; and rapid gating input scaling via long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of recent sensorimotor-evoked sequences, despite unchanged recurrent weights. LTP-IE 'tags' specific neurons with increased spiking probability under gating input, and ordering is reconstructed from recurrent connectivity. We further show how LTP-IE can implement temporary stimulus-response mappings. This elucidates a novel combination of mechanisms that might play a role in rapid cognitive flexibility.

Finite-Time Synchronization of Networks via Quantized Intermittent Pinning Control

This technical correspondence considers finite-time synchronization of dynamical networks by designing aperiodically intermittent pinning controllers with logarithmic quantization. The control scheme can greatly reduce control cost and save both communication channels and bandwidth. By using multiple Lyapunov functions and convex combination techniques, sufficient conditions formulated by a set of linear matrix inequalities are derived to guarantee that all the node systems are synchronized with an isolated trajectory in a finite settling time. Compared with existing results, the main characteristics of this paper are twofold: 1) quantized controller is used for finite-time synchronization and 2) the designed multiple Lyapunov functions are strictly decreasing. An optimal algorithm is proposed for the estimation of settling time. Numerical simulations are provided to demonstrate the effectiveness of the theoretical analysis.

Amyloid-β disrupts ongoing spontaneous activity in sensory cortex

The effect of Alzheimer's disease pathology on activity of individual neocortical neurons in the intact neural network remains obscure. Ongoing spontaneous activity, which constitutes most of neocortical activity, is the background template on which further evoked-activity is superimposed. We compared in vivo intracellular recordings and local field potentials (LFP) of ongoing activity in the barrel cortex of APP/PS1 transgenic mice and age-matched littermate CONTROLS, following significant amyloid-β (Aβ) accumulation and aggregation. We found that membrane potential dynamics of neurons in Aβ-burdened cortex significantly differed from those of nontransgenic

CONTROLS

durations of the depolarized state were considerably shorter, and transitions to that state frequently failed. The spiking properties of APP/PS1 neurons showed alterations from those of

CONTROLS

both firing patterns and spike shape were changed in the APP/PS1 group. At the population level, LFP recordings indicated reduced coherence within neuronal assemblies of APP/PS1 mice. In addition to the physiological effects, we show that morphology of neurites within the barrel cortex of the APP/PS1 model is altered compared to CONTROLS. These results are consistent with a process where the effect of Aβ on spontaneous activity of individual neurons amplifies into a network effect, reducing network integrity and leading to a wide cortical dysfunction.

Parameters for burst detection

Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics.

Variability and information content in auditory cortex spike trains during an interval-discrimination task

Processing of temporal information is key in auditory processing. In this study, we recorded single-unit activity from rat auditory cortex while they performed an interval-discrimination task. The animals had to decide whether two auditory stimuli were separated by either 150 or 300 ms and nose-poke to the left or to the right accordingly. The spike firing of single neurons in the auditory cortex was then compared in engaged vs. idle brain states. We found that spike firing variability measured with the Fano factor was markedly reduced, not only during stimulation, but also in between stimuli in engaged trials. We next explored if this decrease in variability was associated with an increased information encoding. Our information theory analysis revealed increased information content in auditory responses during engagement compared with idle states, in particular in the responses to task-relevant stimuli. Altogether, we demonstrate that task-engagement significantly modulates coding properties of auditory cortical neurons during an interval-discrimination task.

A spike-timing-based integrated model for pattern recognition

During the past few decades, remarkable progress has been made in solving pattern recognition problems using networks of spiking neurons. However, the issue of pattern recognition involving computational process from sensory encoding to synaptic learning remains underexplored, as most existing models or algorithms target only part of the computational process. Furthermore, many learning algorithms proposed in the literature neglect or pay little attention to sensory information encoding, which makes them incompatible with neural-realistic sensory signals encoded from real-world stimuli. By treating sensory coding and learning as a systematic process, we attempt to build an integrated model based on spiking neural networks (SNNs), which performs sensory neural encoding and supervised learning with precisely timed sequences of spikes. With emerging evidence of precise spike-timing neural activities, the view that information is represented by explicit firing times of action potentials rather than mean firing rates has been receiving increasing attention. The external sensory stimulation is first converted into spatiotemporal patterns using a latency-phase encoding method and subsequently transmitted to the consecutive network for learning. Spiking neurons are trained to reproduce target signals encoded with precisely timed spikes. We show that when a supervised spike-timing-based learning is used, different spatiotemporal patterns are recognized by different spike patterns with a high time precision in milliseconds.

The effects of pooling on spike train correlations

Neurons integrate inputs from thousands of afferents. Similarly, some experimental techniques record the pooled activity of large populations of cells. When cells in these populations are correlated, the correlation coefficient between the collective activity of two subpopulations is typically much larger than the correlation coefficient between individual cells: The act of pooling individual cell signals amplifies correlations. We give an overview of this phenomenon and present several implications. In particular, we show that pooling leads to synchronization in feedforward networks and that it can amplify and otherwise distort correlations between recorded signals.

Sequential Reinstatement of Neocortical Activity during Slow Oscillations Depends on Cells' Global Activity

During Slow Wave Sleep (SWS), cortical activity is dominated by endogenous processes modulated by slow oscillations (0.1–1 Hz): cell ensembles fluctuate between states of sustained activity (UP states) and silent epochs (DOWN states). We investigate here the temporal structure of ensemble activity during UP states by means of multiple single unit recordings in the prefrontal cortex of naturally sleeping rats. As previously shown, the firing rate of each PFC cell peaks at a distinct time lag after the DOWN/UP transition in a consistent order. We show here that, conversely, the latency of the first spike after the UP state onset depends primarily on the session-averaged firing rates of cells (which can be considered as an indirect measure of their intrinsic excitability). This latency can be explained by a simple homogeneous process (Poisson model) of cell firing, with sleep averaged firing rates employed as parameters. Thus, at DOWN/UP transitions, neurons are affected both by a slow process, possibly originating in the cortical network, modulating the time course of firing for each cell, and by a fast, relatively stereotyped reinstatement of activity, related mostly to global activity levels.

Dysregulation of coordinated neuronal firing patterns in striatum of freely behaving transgenic rats that model Huntington's disease

Altered neuronal activity in the striatum appears to be a key component of Huntington's disease (HD), a fatal, neurodegenerative condition. To assess this hypothesis in freely behaving transgenic rats that model HD (tgHDs), we used chronically implanted micro-wires to record the spontaneous activity of striatal neurons. We found that relative to wild-type controls, HD rats suffer from population-level deficits in striatal activity characterized by a loss of correlated firing and fewer episodes of coincident spike bursting between simultaneously recorded neuronal pairs. These results are in line with our previous report of marked alterations in the pattern of striatal firing in mouse models of HD that vary in background strain, genetic construct, and symptom severity. Thus, loss of coordinated spike activity in striatum appears to be a common feature of HD pathophysiology, regardless of HD model variability.

Directed Interactions Between Auditory and Superior Temporal Cortices and their Role in Sensory Integration

Recent studies using functional imaging and electrophysiology demonstrate that processes related to sensory integration are not restricted to higher association cortices but already occur in early sensory cortices, such as primary auditory cortex. While anatomical studies suggest the superior temporal sulcus (STS) as likely source of visual input to auditory cortex, little evidence exists to support this notion at the functional level. Here we tested this hypothesis by simultaneously recording from sites in auditory cortex and STS in alert animals stimulated with dynamic naturalistic audio–visual scenes. Using Granger causality and directed transfer functions we first quantified causal interactions at the level of field potentials, and subsequently determined those frequency bands that show effective interactions, i.e. interactions that are relevant for influencing neuronal firing at the target site. We found that effective interactions from auditory cortex to STS prevail below 20 Hz, while interactions from STS to auditory cortex prevail above 20 Hz. In addition, we found that directed interactions from STS to auditory cortex make a significant contribution to multisensory influences in auditory cortex: Sites in auditory cortex showing multisensory enhancement received stronger feed-back from STS during audio–visual than during auditory stimulation, while sites with multisensory suppression received weaker feed-back. These findings suggest that beta frequencies might be important for inter-areal coupling in the temporal lobe and demonstrate that superior temporal regions indeed provide one major source of visual influences to auditory cortex.

Throwing a glance at the neural code: rapid information transmission in the visual system

Our visual system can operate at fascinating speeds. Psychophysical experiments teach us that the processing of complex natural images and visual object recognition require a mere split second. Even in everyday life, our gaze seldom rests for long on any particular spot of the visual scene before a sudden movement of the eyes or the head shifts it to a new location. These observations challenge our understanding of how neurons in the visual system of the brain represent, process, and transmit the relevant visual information quickly enough. This article argues that the speed of visual processing provides an adjuvant framework for studying the neural code in the visual system. In the retina, which constitutes the first stage of visual processing, recent experiments have highlighted response features that allow for particularly rapid information transmission. This sets the stage for discussing some of the fundamental questions in the research of neural coding. How do downstream brain regions read out signals from the retina and combine them with intrinsic signals that accompany eye movements? And, how do the neural response features ultimately affect perception and behavior?

Dysregulated information processing by medium spiny neurons in striatum of freely behaving mouse models of Huntington's disease

Huntington's disease (HD) is an autosomal dominant condition that compromises behavioral output. Dysfunction of medium spiny neurons (MSNs), which are the sole output system of the striatum, is thought to underlie HD pathophysiology. What is not known is how HD alters MSN information processing during behavior, which likely drives the HD behavioral phenotype. We recorded from populations of MSNs in two freely behaving and symptomatic HD mouse models: R6/2 transgenics are based on a C57BL/6J*CBA/J background and show robust behavioral symptoms, whereas knock-in (KI) mice have a 129sv background and express relatively mild behavioral signs. At the single-unit level, we found that the MSN firing rate was elevated in R6/2 but not in KI mice compared with their respective wild-type (WT) controls. In contrast, burst activity, which corresponds to periods of high-frequency firing, was altered in both HD models compared with WT. At the population level, we found that correlated firing between pairs of MSNs was a prominent feature in WT that was reduced in both HD models. Similarly, coincident bursts, which are bursts between pairs of neurons that overlap in time and occur more often in pairs of MSNs that exhibit correlated firing, were decreased in HD mice. Our results indicate an important role in both bursting and correlated burst firing for information processing in MSNs. Dysregulation of this processing scheme, moreover, is a key component of HD pathophysiology regardless of the severity of HD symptoms, genetic construct, and background strain of the mouse models.

From single extracellular unit recording in experimental and human Parkinsonism to the development of a functional concept of the role played by the basal ganglia in motor control

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects the whole basal ganglia (BG). Various techniques have been used to study BG physiology and pathophysiology. Among these, extracellular single unit recording remains of particular importance. An impressive number of studies of BG electrophysiological activity have been carried out, both in non-human and in human primates, but the data collected show many omissions and disparities. BG activity has been well defined in the physiological situation, but remains far from clear in the Parkinsonian and virtually unexplored in the dopamine (DA)-replacement situation. This paper provides a brief synopsis of (i) recording techniques and (ii) BG electrophysiological activity in normal, Parkinsonian, and dopamine-replacement situations. We have restricted the data used to those obtained in BG structures of human and non-human primates. Only single unit recordings have been reported and four electrophysiological characteristics retained: mean firing frequency, firing pattern, periodic oscillation, and response to both passive and active movement. We have attempted to summarize (i) the commonly accepted characteristics of each BG structure in the three situations, (ii) discrepancies that exist, and (iii) missing elements. Then, the main successive theories aimed to explain the role played by BG in motor control are presented and discussed in the light of the most recently obtained results using the latest technological advances.

Understanding the nature of the general factor of intelligence: the role of individual differences in neural plasticity as an explanatory mechanism

The nature of the general factor of intelligence, or g, is examined. This article begins by observing that the finding of a general factor of intelligence appears to be inconsistent with current findings in neuroscience and cognitive science, where specific connections are argued to be critical for different intellectual abilities and the brain is argued to develop these connections in response to environmental stimuli. However, it is then observed that if people differed in neural plasticity, or the ability to adapt their connections to the environment, then those highly developed in one intellectual ability would be highly developed in other intellectual abilities as well. Simulations are then used to confirm that such a pattern would be obtained. Such a model is also shown to account for many other findings in the field of intelligence that are currently unexplained. A critical period for intellectual development is then emphasized.

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.

Enhanced spontaneous transmitter release is the earliest consequence of neocortical hypoxia that can explain the disruption of normal circuit function

After the onset of an acute episode of arrested circulation to the brain and consequent cerebral hypoxia, EEG changes and modifications of consciousness ensue within seconds. This in part reflects the rapid effect of hypoxia on the neocortex, where oxygen deprivation leads to impaired neuronal excitability and abnormal synaptic transmission. To identify the cellular mechanisms responsible for the earliest changes in neocortical function and to determine their time course, we have used patch-in-slice recording techniques to investigate the effects of acute hypoxia on the synaptic and intrinsic properties of layer 5 neurons. Coronal slices of mouse somatosensory cortex were maintained at 37 degrees C and challenged with episodes of hypoxia (3-4 min of exposure to 95% N(2), 5% CO(2)). In recordings with cell-attached patch electrodes, activation of ATP-sensitive potassium channels first became detectable 211 +/- 11 sec (range, 185-240 sec; n = 6 patches) after the onset of hypoxia. Similar recording techniques revealed no alterations in the properties of Na(+) currents in the first 4 min after the onset of hypoxia. The earliest hypoxia-induced disturbance was a marked increase in the frequency of spontaneous EPSCs and IPSCs, which began within 15-30 sec of the removal of oxygen. This rapid synaptic effect was not sensitive to TTX and was present in Ca(2+)-free perfusate, indicating that the hypoxia had a direct influence on the vesicular release mechanisms. The incoherent, massive increase in miniature PSCs would be expected to deplete the readily releasable pool of vesicles in cortical terminals, and to thereby markedly distort the neuronal interactions that underlie normal circuit function.

Comparative computations of spike synchronization in visual cortex of cats

In recent years it has been proposed that synchronous activity between neurons is a putative mechanism to bind together various trigger features of an image. Thus the measure of synchronization becomes an important issue since it may be an electrophysiological sign of visual perception. This paper describes and compares six techniques of computing synchronization strength, that is, the central peak of a cross-correlogram. Data were obtained in anesthetized cats prepared for electrophysiological recordings in a conventional fashion. Results indicate that: (1) eye fits are misleading. Visual inspection of cross-correlograms, may be interesting if one needs to estimate approximately synchronization strength and the presence of oscillations in the cross-correlograms, however it may be misleading if one wants to compare different cross-correlograms; (2) regression analysis to compare one method against the others yields a relatively poor correlation suggesting that methods are not directly comparable; (3) the sensitivity of each computational method is unequal. The results may indicate that some functional connections are either under- or over-evaluated depending upon the strategy employed to measure synchronization.

Neural coding: higher-order temporal patterns in the neurostatistics of cell assemblies

Recent advances in the technology of multiunit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblies. This has created the need for statistical approaches to detecting the presence of spatiotemporal patterns of more than two neurons in neuron spike train data. We mention three possible measures for the presence of higher-order patterns of neural activation--coefficients of log-linear models, connected cumulants, and redundancies--and present arguments in favor of the coefficients of log-linear models. We present test statistics for detecting the presence of higher-order interactions in spike train data by parameterizing these interactions in terms of coefficients of log-linear models. We also present a Bayesian approach for inferring the existence or absence of interactions and estimating their strength. The two methods, the frequentist and the Bayesian one, are shown to be consistent in the sense that interactions that are detected by either method also tend to be detected by the other. A heuristic for the analysis of temporal patterns is also proposed. Finally, a Bayesian test is presented that establishes stochastic differences between recorded segments of data. The methods are applied to experimental data and synthetic data drawn from our statistical models. Our experimental data are drawn from multiunit recordings in the prefrontal cortex of behaving monkeys, the somatosensory cortex of anesthetized rats, and multiunit recordings in the visual cortex of behaving monkeys.

Temporal coding of visual information in the thalamus

The amount of information a sensory neuron carries about a stimulus is directly related to response reliability. We recorded from individual neurons in the cat lateral geniculate nucleus (LGN) while presenting randomly modulated visual stimuli. The responses to repeated stimuli were reproducible, whereas the responses evoked by nonrepeated stimuli drawn from the same ensemble were variable. Stimulus-dependent information was quantified directly from the difference in entropy of these neural responses. We show that a single LGN cell can encode much more visual information than had been demonstrated previously, ranging from 15 to 102 bits/sec across our sample of cells. Information rate was correlated with the firing rate of the cell, for a consistent rate of 3.6 +/- 0.6 bits/spike (mean +/- SD). This information can primarily be attributed to the high temporal precision with which firing probability is modulated; many individual spikes were timed with better than 1 msec precision. We introduce a way to estimate the amount of information encoded in temporal patterns of firing, as distinct from the information in the time varying firing rate at any temporal resolution. Using this method, we find that temporal patterns sometimes introduce redundancy but often encode visual information. The contribution of temporal patterns ranged from -3.4 to +25.5 bits/sec or from -9.4 to +24.9% of the total information content of the responses.

Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo

Cortical neurons are sensitive to the timing of their synaptic inputs. They can synchronize their firing on a millisecond time scale and follow rapid stimulus fluctuations with high temporal precision. These findings suggest that cortical neurons have an enhanced sensitivity to synchronous synaptic inputs that lead to rapid rates of depolarization. The voltage-gated currents underlying action potential generation may provide one mechanism to amplify rapid depolarizations. We have tested this hypothesis by analyzing the relations between membrane potential fluctuations and spike threshold in cat visual cortical neurons recorded intracellularly in vivo. We find that visual stimuli evoke broad variations in spike threshold that are caused in large part by an inverse relation between spike threshold and the rate of membrane depolarization preceding a spike. We also find that spike threshold is inversely related to the rate of rise of the action potential upstroke, suggesting that increases in spike threshold result from a decrease in the availability of Na(+) channels. By using a simple neuronal model, we show that voltage-gated Na(+) and K(+) conductances endow cortical neurons with an enhanced sensitivity to rapid depolarizations that arise from synchronous excitatory synaptic inputs. Thus, the basic mechanism responsible for action potential generation also enhances the sensitivity of cortical neurons to coincident synaptic inputs.

High-frequency oscillations (20 to 120 Hz) and their role in visual processing

Oscillatory firing of neurons in response to visual stimuli has been observed to occur with different frequencies at multiple levels of the visual system. In the cat retina, oscillatory firing patterns occur with frequencies in the range of 60 to 120 Hz (omega-oscillations). These millisecond-precise temporal patterns are transmitted reliably to the cortex and may provide a feed-forward mechanism of response synchronization. In the cortex, visual responses often show oscillatory patterning with frequencies between 20 and 60 Hz (gamma-oscillations), which are not phase locked to the stimulus onset and therefore do not show up in regularly averaged evoked potentials. Gamma-oscillatory responses synchronize with millisecond precision over long distances and are mediated by the reciprocal corticocortical connectivity. Modulatory systems like the ascending reticular activating system facilitate synchronization and increase the strength of gamma-oscillations. During states of such functional cortical activation, the dominant frequency of the EEG is shifted from lower frequencies in the delta-/theta-range to higher frequencies in the gamma-range. Therefore, functional states indicate different degrees of temporal precision with which large neuronal populations interact. Response synchronization also depends on relations of global stimulus features. This suggests that millisecond-precise neuronal interactions serve as a fundamental mechanism for visual information processing.

Chloride-cotransport blockade desynchronizes neuronal discharge in the "epileptic" hippocampal slice

Antagonism of the chloride-cotransport system in hippocampal slices has been shown to block spontaneous epileptiform (i.e., hypersynchronized) discharges without diminishing excitatory synaptic transmission. Here we test the hypotheses that chloride-cotransport blockade, with furosemide or low-chloride (low-[Cl(-)](o)) medium, desynchronizes the firing activity of neuronal populations and that this desynchronization is mediated through nonsynaptic mechanisms. Spontaneous epileptiform discharges were recorded from the CA1 and CA3 cell body layers of hippocampal slices. Treatment with low-[Cl(-)](o) medium led to cessation of spontaneous synchronized bursting in CA1 >/=5-10 min before its disappearance from CA3. During the time that CA3 continued to burst spontaneously but CA1 was silent, electrical stimulation of the Schaffer collaterals showed that hyperexcited CA1 synaptic responses were maintained. Paired intracellular recordings from CA1 pyramidal cells showed that during low-[Cl(-)](o) treatment, the timing of action potential discharges became desynchronized; desynchronization was identified with phase lags in firing times of action potentials between pairs of neurons as well as a with a broadening and diminution of the CA1 field amplitude. Continued exposure to low-[Cl(-)](o) medium increased the degree of the firing-time phase shifts between pairs of CA1 pyramidal cells until the epileptiform CA1 field potential was abolished completely. Intracellular recordings during 4-aminopyridine (4-AP) treatment showed that prolonged low-[Cl(-)](o) exposure did not diminish the frequency or amplitude of spontaneous postsynaptic potentials. CA3 antidromic responses to Schaffer collateral stimulation were not significantly affected by prolonged low-[Cl(-)](o) exposure. In contrast to CA1, paired intracellular recordings from CA3 pyramidal cells showed that chloride-cotransport blockade did not cause a significant desynchronization of action potential firing times in the CA3 subregion at the time that CA1 synchronous discharge was blocked but did reduce the number of action potentials associated with CA3 burst discharges. These data support our hypothesis that the anti-epileptic effects of chloride-cotransport antagonism in CA1 are mediated through the desynchronization of population activity. We hypothesize that interference with Na(+),K(+),2Cl(-) cotransport results in an increase in extracellular potassium ([K(+)](o)) that reduces the number of action potentials that are able to invade axonal arborizations and varicosities in all hippocampal subregions. This reduced efficacy of presynaptic action potential propagation ultimately leads to a reduction of synaptic drive and a desynchronization of the firing of CA1 pyramidal cells.