A single spiking neuron that can represent interval timing: analysis,plasticity and multi-stability

The ability to represent interval timing is crucial for many common behaviors, such as knowing whether to stop when the light turns from green to yellow. Neural representations of interval timing have been reported in the rat primary visual cortex and we have previously presented a computational framework describing how they can be learned by a network of neurons. Recent experimental and theoretical results in entorhinal cortex have shown that single neurons can exhibit persistent activity, previously thought to be generated by a network of neurons. Motivated by these single neuron results, we propose a single spiking neuron model that can learn to compute and represent interval timing. We show that a simple model, reduced analytically to a single dynamical equation, captures the average behavior of the complete high dimensional spiking model very well. Variants of this model can be used to produce bi-stable or multi-stable persistent activity. We also propose a plasticity rule by which this model can learn to represent different intervals and different levels of persistent activity.     

Spatial learning and action planning in a prefrontal cortical network model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive "insight" capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates.     

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.     

Time course of attention reveals different mechanisms for spatial and feature-based attention in area V4

Attention can facilitate visual processing, emphasizing specific locations and highlighting stimuli containing specific features. To dissociate the mechanisms of spatial and feature-based attention, we compared the time course of visually evoked responses under different attention conditions. We recorded from single neurons in area V4 during a delayed match-to-sample task that controlled both spatial and feature-based attention. Neuronal responses increased when spatial attention was directed toward the receptive field and were modulated by the identity of the target of feature-based attention. Modulation by spatial attention was weaker during the early portion of the visual response and stronger during the later portion of the response. In contrast, modulation by feature-based attention was relatively constant throughout the response. It appears that stimulus onset transients disrupt spatial attention, but not feature attention. We conclude that spatial attention reflects a combination of stimulus-driven and goal-driven processes, while feature-based attention is purely goal driven.     

Spatial constancy and the brain: insights from neural networks

To form an accurate internal representation of visual space, the brain must accurately account for movements of the eyes, head or body. Updating of internal representations in response to these movements is especially important when remembering spatial information, such as the location of an object, since the brain must rely on non-visual extra-retinal signals to compensate for self-generated movements. We investigated the computations underlying spatial updating by constructing a recurrent neural network model to store and update a spatial location based on a gaze shift signal, and to do so flexibly based on a contextual cue. We observed a striking similarity between the patterns of behaviour produced by the model and monkeys trained to perform the same task, as well as between the hidden units of the model and neurons in the lateral intraparietal area (LIP). In this report, we describe the similarities between the model and single unit physiology to illustrate the usefulness of neural networks as a tool for understanding specific computations performed by the brain.     

基于鼠脑海马位置细胞与Q学习面向目标导航

生理学实验表明在鼠脑海马结构中存在的一种具有特异性放电特征的细胞,它在大鼠空间导航和环境认知中起着关键性作用,该特异性神经元被称之为位置细胞。本文将基于位置细胞、运动神经元来构建一种前馈神经网络模型,采用Q学习算法实现大鼠面向目标导航任务。实验结果表明该前馈神经网络模型能快速实现大鼠面向目标导航任务。     

Attention to stimulus features shifts spectral tuning of V4 neurons during natural vision

Previous neurophysiological studies suggest that attention can alter the baseline or gain of neurons in extrastriate visual areas but that it cannot change tuning. This suggests that neurons in visual cortex function as labeled lines whose meaning does not depend on task demands. To test this common assumption, we used a system identification approach to measure spatial frequency and orientation tuning in area V4 during two attentionally demanding visual search tasks, one that required fixation and one that allowed free viewing during search. We found that spatial attention modulates response baseline and gain but does not alter tuning, consistent with previous reports. In contrast, feature-based attention often shifts neuronal tuning. These tuning shifts are inconsistent with the labeled-line model and tend to enhance responses to stimulus features that distinguish the search target. Our data suggest that V4 neurons behave as matched filters that are dynamically tuned to optimize visual search.     

Using neuronal populations to study the mechanisms underlying spatial and feature attention

Visual attention affects both perception and neuronal responses. Whether the same neuronal mechanisms mediate spatial attention, which improves perception of attended locations, and nonspatial forms of attention has been a subject of considerable debate. Spatial and feature attention have similar effects on individual neurons. Because visual cortex is retinotopically organized, however, spatial attention can comodulate local neuronal populations, whereas feature attention generally requires more selective modulation. We compared the effects of feature and spatial attention on local and spatially separated populations by recording simultaneously from dozens of neurons in both hemispheres of V4. Feature and spatial attention affect the activity of local populations similarly, modulating both firing rates and correlations between pairs of nearby neurons. However, whereas spatial attention appears to act on local populations, feature attention is coordinated across hemispheres. Our results are consistent with a unified attentional mechanism that can modulate the responses of arbitrary subgroups of neurons.     

Analysis of spiking synchrony in visual cortex reveals distinct types of top-down modulation signals for spatial and object-based attention

The activity of a border ownership selective (BOS) neuron indicates where a foreground object is located relative to its (classical) receptive field (RF). A population of BOS neurons thus provides an important component of perceptual grouping, the organization of the visual scene into objects. In previous theoretical work, it has been suggested that this grouping mechanism is implemented by a population of dedicated grouping (“G”) cells that integrate the activity of the distributed feature cells representing an object and, by feedback, modulate the same cells, thus making them border ownership selective. The feedback modulation by G cells is thought to also provide the mechanism for object-based attention. A recent modeling study showed that modulatory common feedback, implemented by synapses with N-methyl-D-aspartate (NMDA)-type glutamate receptors, accounts for the experimentally observed synchrony in spike trains of BOS neurons and the shape of cross-correlations between them, including its dependence on the attentional state. However, that study was limited to pairs of BOS neurons with consistent border ownership preferences, defined as two neurons tuned to respond to the same visual object, in which attention decreases synchrony. But attention has also been shown to increase synchrony in neurons with inconsistent border ownership selectivity. Here we extend the computational model from the previous study to fully understand these effects of attention. We postulate the existence of a second type of G-cell that represents spatial attention by modulating the activity of all BOS cells in a spatially defined area. Simulations of this model show that a combination of spatial and object-based mechanisms fully accounts for the observed pattern of synchrony between BOS neurons. Our results suggest that modulatory feedback from G-cells may underlie both spatial and object-based attention.     

Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal''s spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.     

Neuronal population activity and functional imaging.

Human functional brain imaging detects blood flow changes that are thought to reflect the activity of neuronal populations and, thus, the responses of neurons that carry behaviourally relevant information. Since this relationship is poorly understood, we explored the link between the activity of single neurons and their neuronal population. The functional imaging results were in good agreement with levels of population activation predicted from the known effects of sensory stimulation, learning and attention on single cortical neurons. However, the nature of the relationship between population activation and single neuron firing was very surprising. Population activation was strongly influenced by those neurons firing at low rates and so was very sensitive to the baseline or 'spontaneous' firing rate. When neural representations were sparse and neurons were tuned to several stimulus dimensions, population activation was hardly influenced by the few neurons whose firing was most strongly modulated by the task or stimulus. Measures of population activation could miss changes in information processing given simultaneous changes in neurons' baseline firing, response modulation or tuning width. Factors that can modulate baseline firing, such as attention, may have a particularly large influence on population activation. The results have implications for the interpretation of functional imaging signals and for cross-calibration between different methods for measuring neuronal activity.     

Synchronized oscillations in the visual cortex — a synergetic model

We present an oscillator network model for the synchronization of oscillatory neuronal activity underlying visual processing. The single neuron is modeled by means of a limit cycle oscillator with an eigenfrequency corresponding to visual stimulation. The eigenfrequency may be time dependent. The mutual coupling strengths are unsymmetrical and activity dependent, and they scatter within the network. Synchronized clusters (groups) of neurons emerge in the network due to the visual stimulation. The different clusters correspond to different visual stimuli. There is no limitation of the number of stimuli. Distinct clusters do not perturb each other, although the coupling strength between all model neurons is of the same order of magnitude. Our analysis is not restricted to weak coupling strength. The scatter of the couplings causes shifts of the cluster frequencies. The model's behavior is compared with the experimental findings. The coupling mechanism is extended in order to model the influence of bicucullin upon the neural network. We additionally investigate repulsive couplings, which lead to constant phase differences between clusters of the same frequency. Finally, we consider the problem of selective attention from the viewpoint of our model.     

Density dependent neurodynamics

The dynamics of a neural network depends on density parameters at (at least) two different levels: the subcellular density of ion channels in single neurons, and the density of cells and synapses at a network level. For the Frankenhaeuser-Huxley (FH) neural model, the density of sodium (Na) and potassium (K) channels determines the behaviour of a single neuron when exposed to an external stimulus. The features of the onset of single neuron oscillations vary qualitatively among different regions in the channel density plane. At a network level, the density of neurons is reflected in the global connectivity. We study the relation between the two density levels in a network of oscillatory FH neurons, by qualitatively distinguishing between three regions, where the mean network activity is (1) spiking, (2) oscillating with enveloped frequencies, and (3) bursting, respectively. We demonstrate that the global activity can be shifted between regions by changing either the density of ion channels at the subcellular level, or the connectivity at the network level, suggesting that different underlying mechanisms can explain similar global phenomena. Finally, we model a possible effect of anaesthesia by blocking specific inhibitory ion channels.     

Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment

It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through “neural sampling”, i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operations on rapidly varying probability distributions, such as the odds of getting rewarded for a specific behavior. We demonstrate the viability of this new approach towards neural coding and computation, which makes use of the inherent parallelism of generic neural circuits, by showing that this model can explain experimentally observed firing activity of cortical neurons for a variety of tasks that require rapid temporal integration of sensory information.     

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

How is cognitive task behavior generated by brain network interactions? This study describes a novel network modeling approach and applies it to source electroencephalography data. The model accurately predicts future information dynamics underlying behavior and (via simulated lesioning) suggests a role for cognitive control networks as key drivers of response information flow.     

Increased attention to spatial context increases both place field stability and spatial memory

The hippocampal formation is critical for the acquisition and consolidation of memories. When recorded in freely moving animals, hippocampal pyramidal neurons fire in a location-specific manner: they are "place" cells, comprising a hippocampal representation of the animal's environment. To explore the relationship between place cells and spatial memory, we recorded from mice in several behavioral contexts. We found that long-term stability of place cell firing fields correlates with the degree of attentional demands and that successful spatial task performance was associated with stable place fields. Furthermore, conditions that maximize place field stability greatly increase orientation to novel cues. This suggests that storage and retrieval of place cells is modulated by a top-down cognitive process resembling attention and that place cells are neural correlates of spatial memory. We propose a model whereby attention provides the requisite neuromodulatation to switch short-term homosynaptic plasticity to long-term heterosynaptic plasticity, and we implicate dopamine in this process.     

Orienting attention based on long-term memory experience

Attentional orienting and memory are intrinsically bound, but their interaction has rarely been investigated. Here we introduce an experimental paradigm using naturalistic scenes to investigate how long-term memory can guide spatial attention and thereby enhance identification of events in the perceptual domain. In the task, stable memories of objects embedded within complex scenes guide spatial orienting. We compared the behavioral effects and neural systems of memory-guided orienting with those in a more traditional attention-orienting task in which transient spatial cues guide attention. Memory-guided attention operated within surprisingly short intervals and conferred reliable and sizeable advantages for detection of objects embedded in scenes. Event-related functional magnetic resonance imaging showed that memory-guided attention involves the interaction between brain areas participating in retrieval of memories for spatial context with the parietal-frontal network for visual spatial orienting. Activity in the hippocampus was specifically engaged in memory-guided spatial attention and correlated with the ensuing behavioral advantage.     

AIM: A network model of attention in auditory cortex

Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.     

Population approach to a neural discrimination task

This article gives insights into the possible neuronal processes involved in visual discrimination. We study the performance of a spiking network of Integrate-and-Fire (IF) neurons when performing a benchmark discrimination task. The task we adopted consists of determining the direction of moving dots in a noisy context using similar stimuli to those in the experiments of Newsome and colleagues. We present a neural model that performs the discrimination involved in this task. By varying the synaptic parameters of the IF neurons, we illustrate the counter-intuitive importance of the second-order statistics (input noise) in improving the discrimination accuracy of the model. We show that measuring the Firing Rate (FR) over a population enables the model to discriminate in realistic times, and even surprisingly significantly increases its discrimination accuracy over the single neuron case, despite the faster processing. We also show that increasing the input noise increases the discrimination accuracy but only at the expense of the speed at which we can read out the FR.     

Synchronized oscillations in the visual cortex — a synergetic model

 We present an oscillator network model for the synchronization of oscillatory neuronal activity underlying visual processing. The single neuron is modeled by means of a limit cycle oscillator with an eigenfrequency corresponding to visual stimulation. The eigenfrequency may be time dependent. The mutual coupling strengths are unsymmetrical and activity dependent, and they scatter within the network. Synchronized clusters (groups) of neurons emerge in the network due to the visual stimulation. The different clusters correspond to different visual stimuli. There is no limitation of the number of stimuli. Distinct clusters do not perturb each other, although the coupling strength between all model neurons is of the same order of magnitude. Our analysis is not restricted to weak coupling strength. The scatter of the couplings causes shifts of the cluster frequencies. The model’s behavior is compared with the experimental findings. The coupling mechanism is extended in order to model the influence of bicucullin upon the neural network. We additionally investigate repulsive couplings, which lead to constant phase differences between clusters of the same frequency. Finally, we consider the problem of selective attention from the viewpoint of our model. Received: 15 February 1995/Accepted in revised form: 18 July 1995