this paper are applicable to a wide variety of experimental paradigms. However, it may help to conceptualise the stimulus as for example which object, of some set, is being viewed by the experimental subject; indeed, data from such an experiment are examined later in this paper. Consider the stimuli to be taken from a discrete set S with S elements, each occurring with probability P (s). The probability of events with response r is denoted as P (r), and the joint probability distribution as P (s; r).

The effectiveness of various stimulus identification (decoding) procedures for extracting the information carried by the responses of a population of neurons to a set of repeatedly presented stimuli is studied analytically, in the limit of short time windows. It is shown that in this limit, the entire information content of the responses can sometimes be decoded, and when this is not the case, the lost information is quantified. In particular, the mutual information extracted by taking into account only the most likely stimulus in each trial turns out to be, if not equal, much closer to the true value than that calculated from all the probabilities that each of the possible stimuli in the set was the actual one. The relation between the mutual information extracted by decoding and the percentage of correct stimulus decodings is also derived analytically in the same limit, showing that the metric content index can be estimated reliably from a few cells recorded from brief periods. Computer simulations as well as the activity of real neurons recorded in the primate hippocampus serve to confirm these results and illustrate the utility and limitations of the approach.

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff formulated four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard ’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. 2 Content

Recent neurophysiological experiments have led to a promising “biased competition hypothesis” of the neural basis of attention. According to this hypothesis, attention appears as a sometimes non-linear property that results from a top-down biassing effect that influences the competitive and cooperative interactions that work both within cortical areas and between cortical areas. In this paper we describe a detailed dynamical analysis of the synaptic and neuronal spiking mechanisms underlying biased competition. We perform a detailed analysis of the dynamical capabilities of the system by exploring the stationary attractors in the parameter space via a mean field reduction consistent with the underlying synaptic and spiking dynamics. The nonstationary dynamical behaviour, as measured in neuronal recording experiments, is studied via an integrate-and-fire model with realistic dynamics. This elucidates the role of cooperation and competition in the dynamics of biased competition; and shows why feedback connections between cortical areas need optimally to be weaker by a factor of approximately 2.5 than the feedforward connections in an attentional network. We modelled the interaction between top-down attention and bottom up stimulus contrast effects

switching Cognitive behaviour requires complex context-dependent mapping between sensory stimuli and actions. The same stimulus can lead to different behaviours depending on the situation, or the same behaviour may be elicited by different cueing stimuli. Neurons in the primate prefrontal cortex show task-speci®c ®ring activity during working memory delay periods. These neurons provide a neural substrate for mapping stimulus and response in a ¯exible, context- or rule-dependent, fashion. We describe here an integrate-and-®re network model to explain and investigate the different types of working-memory-related neuronal activity observed. The model contains different populations (or pools) of neurons (as found neurophysiologically) in attractor networks which respond in the delay period to the stimulus object, the stimulus position (`sensory pools'), to combinations of the stimulus sensory properties (e.g. the object identity or object location) and the response (`intermediate pools'), and to the response required (left or right) (`premotor pools'). The pools are arranged hierarchically, are linked by associative synaptic connections, and have global inhibition through inhibitory interneurons to implement competition. It is shown that a biasing attentional input to de®ne the current rule applied to the intermediate pools enables the system to select the correct response in what is a biased competition model of attention. The integrate-and-®re model not only produces realistic spiking dynamicals very similar to the neuronal data but also shows how dopamine could weaken and shorten the persistent neuronal activity in the delay period; and allows us to predict more response errors when dopamine is elevated because there

n Backward masking can potentially provide evidence of the time needed for visual processing, a fundamental constraint that must be incorporated into computational models of vision. Although backward masking has been extensively used psychophysically, there is little direct evidence for the effects of visual masking on neuronal responses. To investigate the effects of a backward masking paradigm on the responses of neurons in the temporal visual cortex, we have shown that the response of the neurons is interrupted by the mask. Under conditions when humans can just identify the stimulus, with stimulus onset asynchronies (SOA) of 20 msec, neurons in macaques respond to their best stimulus for approximately 30 msec. We now quantify the information that is available from the responses of single neurons under backward masking conditions when two to six faces were shown. We show that the information available is greatly decreased as the mask is brought closer to the stimulus. The decrease is more marked than the decrease in #ring rate because it is the selective part of the #ring that is especially attenuated by the mask, not the spontaneous #ring, and also because the neuronal response is more variable at short SOAs. However, even at the shortest SOA of 20 msec, the information available is on average 0.1 bits. This compares to 0.3 bits with only the 16-msec target stimulus shown and a typical value for such neurons of 0.4 to 0.5 bits with a 500msec stimulus. The results thus show that considerable information is available from neuronal responses even under backward masking conditions that allow the neurons to have their main response in 30 msec. This provides evidence for how rapid the processing of visual information is in a cortical area and provides a fundamental constra...

The information represented in the primate hippocampus is being analysed by making recordings in monkeys actively walking in the laboratory. In a sample of 352 cells recorded in this situation, no "place" cells have so far been found. Instead, we have found a considerable population of "spatial view" cells tuned to respond when the monkey looks at small parts of the environment. We have been able to demonstrate (1) that these hippocampal neurons respond to a view of space "out there," not to the place where the monkey is; (2) that the responses depend on where the monkey is looking, by measuring eye position; (3) that the responses in some cases (e.g., CA1 but not CA3) still occur if the view details are obscured with curtains; (4) that the cells (in, e.g., CA1) retain part of their "space" tuning even in complete darkness, for several minutes; and (5) that the spatial representation is allocentric. The spatial representation is, thus, different from that in the rat hippocampus, in which place cells respond based on where the rat is located. The representation is also different from that described in the parietal cortex, where neurons respond in egocentric coordinates. This representation of space "out there" provided by primate spatial view cells would be an appropriate part of a memory system involved in memories of particular events or episodes, for example, of where in an environment an object was seen. Spatial view cells (in conjunction with whole body motion cells in the primate hippocampus, and head direction cells in the primate presubiculum) would also be useful as part of a spatial navigation system, for which they would provide a memory component. Hippocampus 1999;9:467--480. # 1999 Wiley-Liss, Inc.

Objects can be recognised independently of the view they present, of their position on the retina, or their scale. It has been suggested that one basic mechanism that makes this possible is a memory effect, or a trace, that allows associations to be made between consecutive views of one object. In this work we explore the possibility that this memory trace is provided by sustained activity of neurons in layers of the visual pathway produced by an extensive recurrent connectivity. We describe a model that contains this high recurrent connectivity and synaptic efficacies built with contributions from associations between pairs of views that is simple enough to be treated analytically. The main result is that there is a change of behavior Permanent address: Departamento de F'isica Te'orica C-XI, Ciudad Universitaria de Cantoblanco, Universidad Aut'onoma de Madrid, 28049 Madrid, Spain as the strength of the association between views of the same object, relative to the association with...

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The existence of recurrent collateral connections between pyramidal cells within a cortical area and, in addition, reciprocal connections between connected cortical areas, is well established. In this work we analyse the properties of a tri-modular architecture of this type in which two input modules have convergent connections to a third module (which in the brain might be the next module in cortical processing or a bi-modal area receiving connections from two different processing pathways). Memory retrieval is analysed in this system which has Hebb-like synaptic modifiability in the connections and attractor states. Local activity features are stored in the intra-modular connections while the associations between corresponding features in different modules present during training are stored in the inter-modular connections. The response of the network when tested with corresponding and contradictory stimuli to the two input pathways is studied in detail. The model is solved quantitatively using techniques of statistical physics. In one type of test, a sequence of stimuli is applied, with a delay between them. It is found that if the coupling between the modules is low a regime exists in which they retain the capability to retrieve any of their stored features independently of the features being retrieved by the other modules. Although independent in this sense, the modules still influence each other in this regime through persistent modulatory currents which are strong enough to initiate recall in the whole network when only a single module is stimulated, and to raise the mean firing rates of the neurons in the attractors if the features in the different modules are corresponding. Some of these mechanisms might be useful for the description of many phenomena observe...