Interaction between neurons of the frontal cortex and hippocampus during the realization of choice of food reinforcement quality in cats

Six cats were subjected to the procedure of appetitive instrumental conditioning (with light as a conditioned stimuls) by the method of the "active choice" of reinforcement quality. Short-delay conditioned bar-press responses were rewarded with bread-meat mixture, and the delayed responses were reinforced by meat. The animals differed in behavior strategy: four animals preferred the bar-pressing with a long delay (the so-called "self-control" group), and two cats preferred the bar-pressing with a short delay (the so-called "impulsive" group). Multiunit activity in the frontal cortex and hippocampus (CA3) was recorded via chronically implanted nichrome wire semimicroelectrodes. An interaction between the neighboring neurons in the frontal cortex and hippocampus (within local neural networks) and between the neurons of the frontal cortex and hippocampus (distributed neural networks in frontal-hippocampal and hippocampal-frontal directions) was evaluated by means of statistical crosscorrelation analysis of spike trains. Crosscorrelations between neuronal spike trains in the delay range of 0-100 ms were explored. It was shown that the number of crosscorrelations between the neuronal discharges both in the local and distributed networks was significantly higher in the "self-control" cats. It was suggested that the local and distributed neural networks of the frontal cortex and hippocampus are involved in the system of brain structures which determine the behavioral strategy of animals in the "self-control" group.     

The individual organization of frontal-hippocampal networks during realization of different behavioral tasks

Four cats were subjected to appetitive instrumental conditioning with light as a conditioned stimulus by the method of "active choice" of the reinforcement quality: short-delay conditioned bar-press responses were followed by bread-meat mixture and the delayed responses--by meat. The animals differed in behavior strategy: four animals preferred bar-pressing with long delay (so called "self-control" group); two animal preferred bar-pressing with short-delay (so called "impulsive" group). Then all the animals were learned to short-delay (1 s) instrumental conditioned reflex to light (CS+) reinforced by meat. The multiunit activity in the frontal cortex and the hippocampus (CA3) was recorded through chronically implanted nichrome-wire semimicroelectrodes. The interactions among the neighboring neurons in the frontal cortex and hippocampus (within the local neuronal networks) and between the neurons of the frontal cortex and hippocampus (distributed neuronal networks of frontal-hippocampal and hippocampal-frontal directions) were evaluated by means of statistical crosscorrelation analysis of the spike trains. Crosscorrelation interneuronal connections in the delay range 0-100 ms were explored. It was shown that the functional organization of the frontal and hippocampal neuronal networks differed in choice behavior and was similar during realization of short-delayed conditioned reflex. We suggest that the local and distributed neural networks of the frontal cortex and hippocampus take part in the realization of cognitive behavior, in particularly in the processes of the decision making.     

Functional organization of local neuronal networks in the cat neocortex. Dependence on the food motivation

The multiple unit activity (MUA) from clusters of adjacent neurones in deep layers of the frontal and motor cortex was recorded in alert cats with different levels of alimentary motivation. Up to 7 spike trains were selected from the MUA. Neurones in the local circuits could be divided into 2 groups: large neurones with prevailing divergent characteristics, and small neurones with prevailing convergent characteristics. A 24-hour food deprivation altered the cross-correlation interneuronal connections with a time delay within the range of 2 to 100 ms.     

The interrelations between neurons of the amygdala and hypothalamus during conditioning with selection of food reinforcement quality in cats

Three cats were subjected to appetitive instrumental conditioning to light by the method of the "active choice" of the reinforcement quality. The short-delayed conditioned bar-pressings were reinforced by bread-meat mixture and the delayed response by meat. The animals differed in behavior strategy: two animals preferred bar-pressing with long delay (the so-called "self-control" group) and one animal preferred bar-pressing with short delay (the so-called "impulsive" group). The multiunit activity of the basolateral amygdala and nucleus lateralis of the hypothalamus was recorded through chronically implanted nichrome wire semimicroelecrodes. The interactions between the neighboring neurons in the lateral hypothalamus and basolateral amygdala (within the local neuronal network) and between the neurons of the basolateral amygdala and lateral hypothalamus (distributed neuronal networks in the direction amygdala--hypothalamus and vice versa) were evaluated by means of statistical crosscorrelation analysis of spike trains. The crosscorrelational interneuronal connections in the delay range of 0-100 ms were examined. It was shown that the number of crosscorrelations between the discharges on neurons both in the local networks of basolateral amygdala and distributed networks was significantly higher in "impulsive" cats. In both groups of animals, the percentage of crosscorrelations between neighbouring neurons in the local networks of the lateral hypothalamus was similar. We suggest that the local networks of the basolateral amygdala and amygdalar-hypothalamic distributed neuronal networks are involved in the system of brain structures which determine the individual features of animal behavior.     

Interrelations between neurons of the frontal cortex and hippocampus under cholinergic deficit in choice behavior of cats

Appetitive instrumental conditioned reflexes on light (CS+) were formed in six cats by the method of "active choice" of quality of reinforcement; bread-meat mixture was given after short-delay conditioned bar-press responses, and the delayed responses were rewarded by meat. The animals differed in choice behavior strategy: "self-control", "ambivalent", "impulsive". The multiunit activity in the frontal cortex and hippocampus (CA3) was recorded. Cross-correlation analysis was used for estimation of correlation of activities in neuronal pairs in the frontal cortex and hippocampus (distributed frontal-hippocampal networks) and pairs within the same structure (frontal and hippocampal local neuronal networks). It was shown that the number of cross-correlations between the discharges of neurons both in the local and distributed networks was significantly higher in "self-control" cats. Under conditions of systemic administration of antagonists of muscarinic central cholinoreceptors (trihexyphenidyl and scopolamine), the bar-press conditioning impaired, the number of direct interneuronal connections decreased, and the number of externally synchronized correlations ("common input") significantly increased. The results suggest that the local and distributed neural networks of the frontal cortex and hippocampus are involved in the system of brain structures that determine the behavioral strategy of "self control".     

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.     

A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons

We propose a model for the neuronal implementation of selective visual attention based on temporal correlation among groups of neurons. Neurons in primary visual cortex respond to visual stimuli with a Poisson distributed spike train with an appropriate, stimulus-dependent mean firing rate. The spike trains of neurons whose receptive fields donot overlap with the focus of attention are distributed according to homogeneous (time-independent) Poisson process with no correlation between action potentials of different neurons. In contrast, spike trains of neurons with receptive fields within the focus of attention are distributed according to non-homogeneous (time-dependent) Poisson processes. Since the short-term average spike rates of all neurons with receptive fields in the focus of attention covary, correlations between these spike trains are introduced which are detected by inhibitory interneurons in V4. These cells, modeled as modified integrate-and-fire neurons, function as coincidence detectors and suppress the response of V4 cells associated with non-attended visual stimuli. The model reproduces quantitatively experimental data obtained in cortical area V4 of monkey by Moran and Desimone (1985).     

Cortical transients preceding voluntary movement

The process of initiating a voluntary muscular movement evidently involves a focusing of diffuse brain activity onto a highly specific location in the primary motor cortex. Even the very simple stereotypic movements used to study the ‘contingent negative variation’ and the ‘readiness potential’ begin with EEG indicative of widely distributed brain activity. In natural settings the involvement of diffuse cortical networks is undoubtedly even more important. Eventually, however, activity must coalesce onto specific neurons for the intended movement to ensue. Here we examine that focusing process from a mathematical point of view. Using a digital simulation, we solve the global equations for cortical dynamics and model the flow from diffuse onset to localized spike. From this perspective the interplay between global and local effects is seen as a necessary consequence of a basic cortical architecture which supports wave propagation. Watching the process evolve over time allows us to estimate some characteristic amplitudes and delays.     

Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies

Dynamical behavior of a biological neuronal network depends significantly on the spatial pattern of synaptic connections among neurons. While neuronal network dynamics has extensively been studied with simple wiring patterns, such as all-to-all or random synaptic connections, not much is known about the activity of networks with more complicated wiring topologies. Here, we examined how different wiring topologies may influence the response properties of neuronal networks, paying attention to irregular spike firing, which is known as a characteristic of in vivo cortical neurons, and spike synchronicity. We constructed a recurrent network model of realistic neurons and systematically rewired the recurrent synapses to change the network topology, from a localized regular and a “small-world” network topology to a distributed random network topology. Regular and small-world wiring patterns greatly increased the irregularity or the coefficient of variation (Cv) of output spike trains, whereas such an increase was small in random connectivity patterns. For given strength of recurrent synapses, the firing irregularity exhibited monotonous decreases from the regular to the random network topology. By contrast, the spike coherence between an arbitrary neuron pair exhibited a non-monotonous dependence on the topological wiring pattern. More precisely, the wiring pattern to maximize the spike coherence varied with the strength of recurrent synapses. In a certain range of the synaptic strength, the spike coherence was maximal in the small-world network topology, and the long-range connections introduced in this wiring changed the dependence of spike synchrony on the synaptic strength moderately. However, the effects of this network topology were not really special in other properties of network activity. Action Editor: Xiao-Jing Wang     

Lateral spike conduction velocity in the visual cortex affects spatial range of synchronization and receptive field size without visual experience: a learning model with spiking neurons

Classical receptive fields (cRF) increase in size from the retina to higher visual centers. The present work shows how temporal properties, in particular lateral spike velocity and spike input correlation, can affect cRF size and position without visual experience. We demonstrate how these properties are related to the spatial range of cortical synchronization if Hebbian learning dominates early development. For this, a largely reduced model of two successive levels of the visual cortex is developed (e.g., areas V1 and V2). It consists of retinotopic networks of spiking neurons with constant spike velocity in lateral connections. Feedforward connections between level 1 and 2 are additive and determine cRF size and shape, while lateral connections within level 1 are modulatory and affect the cortical range of synchronization. Input during development is mimicked by spike trains with spatially homogeneous properties and a confined temporal correlation width. During learning, the homogeneous lateral coupling shrinks to limited coupling structures defining synchronization and related association fields (AF). The size of level-1 synchronization fields determines the lateral coupling range of developing level-1-to-2 connections and, thus, the size of level-2 cRFs, even if the feedforward connections have distance-independent delays. AFs and cRFs increase with spike velocity in the lateral network and temporal correlation width of the input. Our results suggest that AF size of V1 and cRF size of V2 neurons are confined during learning by the temporal width of input correlations and the spike velocity in lateral connections without the need of visual experience. During learning from visual experience, a similar influence of AF size on the cRF size may be operative at successive levels of processing, including other parts of the visual system.     

The temporal organization of the functional connection of the motor cortex neurons in the cat]

Conditioned food-procuring response to time (2 minutes interval) was elaborated in cats, multiunit activity of the motor cortex being recorded. On the basis of single spike trains discriminated from the multiunit activity the cross-correlation histograms were built and the spikes composing their peaks were analysed in real time. This secondary analysis of the histograms allowed to ascertain the dynamics of functional connections between the neurons during the phase of active waiting according to the distribution of coincident impulses. A concentration of coincident impulses of simultaneously recorded cells was observed in different moment of time. In some neuronal pairs the concentration of coincident impulses was revealed to the end of the conditioned interval. The data obtained are considered as a manifestation of the conditioned reaction at the level of neuronal interaction.     

Temporal organization of interneuronal relations in the cerebral cortex of the cat in relation to the level of alimentary motivation

By means of records of multicellular activity, interneuronal relations and their modifications in two cortical zones (Visual and motor) were studied in cats at different levels of alimentary motivation. For quantitative evaluation of interneuronal relations the statistic method of cross-correlation analysis of impulse trains was used in determining the probability of the appearance of the discharge of one neurone after the impulse of the other one. For groups of neurones in both investigated cortical areas, three-neurones microsystems were singled out and their activity was analyzed by temporal parameters of interaction between neurones at the interval of 120 ms, both within one microarea (intraanalyzer connections) and between microareas of two cortical zones. The correlation of temporal parameters of interneuronal connections (temporal delays in the activity of neuronal pairs) changed depending on spatial localization of neurones and functional condition of the animals. The existence is suggested of "informational" (1-30 ms) and "motivational" (90-120 ms) values of interneuronal relations for interanalyser connections.     

Spatio-temporal firing patterns in the frontal cortex of behaving monkeys

Recording the activity of several neurons in parallel in the frontal cortex of behaving monkeys reveals that firing times of neurons can maintain ± 1 ms accuracy even after delays of over 400 ms. The accurate firing structures were associated with behavior. Neural networks that can sustain such accuracy can learn ‘learn’ to bind with each other and thus may serve as building blocks for cognitive processes.     

Multiple origins of the cortical γ rhythm

Gamma rhythms (30-80 Hz) are a near-ubiquitous feature of neuronal population activity in mammalian cortices. Their dynamic properties permit the synchronization of neuronal responses to sensory input within spatially distributed networks, transient formation of local neuronal "cell assemblies," and coherent response patterns essential for intercortical regional communication. Each of these phenomena form part of a working hypothesis for cognitive function in cortex. All forms of physiological gamma rhythm are inhibition based, being characterized by rhythmic trains of inhibitory postsynaptic potentials in populations of principal neurons. It is these repeating periods of relative enhancement and attenuation of the responsivity of major cell groups in cortex that provides a temporal structure shared across many millions of neurons. However, when considering the origins of these repeating trains of inhibitory events considerable divergence is seen depending on cortical region studied and mode of activation of gamma rhythm generating networks. Here, we review the evidence for involvement of multiple subtypes of interneuron and focus on different modes of activation of these cells. We conclude that most massively parallel brain regions have different mechanisms of gamma rhythm generation, that different mechanisms have distinct functional correlates, and that switching between different local modes of gamma generation may be an effective way to direct cortical communication streams. Finally, we suggest that developmental disruption of the endophenotype for certain subsets of gamma-generating interneuron may underlie cognitive deficit in psychiatric illness.     

The correlation grid: analysis of synchronous spiking in multi-dimensional spike train data and identification of feasible connection architectures

This paper presents a visualization technique specifically designed to support the analysis of synchronous firings in multiple, simultaneously recorded, spike trains. This technique, called the correlation grid, enables investigators to identify groups of spike trains, where each pair of spike trains has a high probability of generating spikes approximately simultaneously or within a constant time shift. Moreover, the correlation grid was developed to help solve the following reverse problem: identification of the connection architecture between spike train generating units, which may produce a spike train dataset similar to the one under analysis. To demonstrate the efficacy of this approach, results are presented from a study of three simulated, noisy, spike train datasets. The parameters of the simulated neurons were chosen to reflect the typical characteristics of cortical pyramidal neurons. The schemes of neuronal connections were not known to the analysts. Nevertheless, the correlation grid enabled the analysts to find the correct connection architecture for each of these three data sets.     

Spike propagation synchronized by temporally asymmetric Hebbian learning

 Synchronously spiking neurons have been observed in the cerebral cortex and the hippocampus. In computer models, synchronous spike volleys may be propagated across appropriately connected neuron populations. However, it is unclear how the appropriate synaptic connectivity is set up during development and maintained during adult learning. We performed computer simulations to investigate the influence of temporally asymmetric Hebbian synaptic plasticity on the propagation of spike volleys. In addition to feedforward connections, recurrent connections were included between and within neuron populations and spike transmission delays varied due to axonal, synaptic and dendritic transmission. We found that repeated presentations of input volleys decreased the synaptic conductances of intragroup and feedback connections while synaptic conductances of feedforward connections with short delays became stronger than those of connections with longer delays. These adaptations led to the synchronization of spike volleys as they propagated across neuron populations. The findings suggests that temporally asymmetric Hebbian learning may enhance synchronized spiking within small populations of neurons in cortical and hippocampal areas and familiar stimuli may produce synchronized spike volleys that are rapidly propagated across neural tissue. Received: 28 May 2002 / Accepted: 3 June 2002 RID="*" ID="*" Correspondence to: R. E. Suri Intelligent Optical Systems (IOS), 2520 W 237th St, Torrance, CA 90505-5217, USA (e-mail: rsuri@intopsys.com, Tel.: +1-310-5307130 ext. 108, Fax: +1-210-5307417)     

Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.     

The outlook for the experimental study of the network functions of cortical cells during learning]

The main outcome of the experiments described in the paper is an idea on the gnostic cortical microset. Multineuronal activity recorded from the motor cortex of cats with a conditioned response to time and the following cross-correlation analysis revealed a strict distribution of interneuronal connections within the microsystems (between the adjacent neurons) and variable connections between the remote neurons during the active waiting stage of two minute interval. Additional analysis of the narrow (0.5 ms) peaks of the histograms allowed to form a view on the synaptic interaction in time. It was found that there was different temporal distribution of the spikes in the peak obtained due to correlograms of neuronal pairs. Some cortical neurons demonstrated a visible synaptic activation at the end of the waiting period when other signs of the temporary behaviour were absent. Pharmacological testing functional interneuronal connections with acetylcholine and Ca(2+)-suppressing drug EGTA have raised a question on the neurochemical specificity of the intra- and extracortical synapses.     

Regulation of spike timing in visual cortical circuits

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.     

Multiplying with neurons: Compensation for irregular input spike trains by using time-dependent synaptic efficiencies

A leaky integrate-and-fire (LIF) neurons can act as multipliers by detecting coincidences of input spikes. However, in case of input spike trains with irregular interspike delays, false coincidences are also detected and the operation as a multiplier is degraded. This problem can be solved by using time dependent synaptic weights which are set to zero after each input spike and recover with the same time constant as the decay time of the corresponding excitatory postsynaptic potentials (EPSP). Such a mechanism results in EPSP's with amplitudes independent on the input interspike delays. Neuronal computation is then performed without frequency decoding.