The authors draw together the results of a series of detailed computational studies and show how they are contributing to the development of a theory of hippocampal function. A new part of the theory introduced here is a quantitative analysis of how backprojections from the hippocampus to the neocortex could lead to the recall of recent memories. The theory is then compared with other theories of hippocampal function. First, what is computed by the hippocampus is considered. The hypothesis the authors advocate, on the basis of the effects of damage to the hippocampus and neuronal activity recorded in it, is that it is involved in the formation of new memories by acting as an intermediate-term buffer store for information about episodes, particularly for spatial, but probably also for some nonspatial, information. The authors analyze how the hippocampus could perform this function, by producing a computational theory of how it operates, based on neuroanatomical and neurophysiological information about the different neuronal systems con-tained within the hippocampus. Key hypotheses are that the CA3 pyramidal cells operate as a single autoassociation network to store new episodic information as it arrives via a number of specialized preprocessing stages from many association areas of the cerebral cortex, and that the dentate

The hippocampus and related structures are thought to be capable of 1) representing cortical activity in a way that minimizes overlap of the representations assigned t ~ different cortical patterns (pattern separation); and 2) modifying synaptic connections so that these representations can later be reinstated from partial or noisy versions of the cortical activity pattern that was present at the time of storage (pattern completion). We point out that there is a trade-off between pattern separation and completion and propose that the unique anatomical and physiological properties of the hippocampus might serve to minimize this trade-off. We use analytical methods to determine quantitative estimates of both separation and completion for specified parameterized models of the hippocampus. These estimates are then used to evaluate the role of various properties and of the hippocampus, such as the activity levels seen in different hippocampal regions, synaptic potentiation and depression, the multi-layer connectivity of the system, and the relatively focused and strong mossy fiber projections. This analysis is focused on the feedforward pathways from the entorhinal cortex (EC) to the dentate gyrus (DG) and region CA3. Among our results are the following: 1) Hebbian synaptic modification (LTP) facilitates completion but reduces separation, unless the

The CA3 network in the hippocampus may operate as an autoassociator, in which declarative memories, known to be dependent on hippocampal processing, could be stored, and subsequently retrieved, using modifiable synaptic efficacies in the CA3 recurrent collateral system. On the basis of this hypothesis, the authors explore the computational relevance of the extrinsic afferents. to the CA3 network. A quantitative statistical analysis of the information that may be relayed by such afferent connections reveals the need for two distinct systems of input synapses. The synapses of the first system need to be strong (but not associatively modifiable) in order to force, during learning, the CA3 cells into a pattern of activity relatively independent of any inputs being received from the recurrent collaterals, and which thus reflects sizable amounts of new information. It is proposed that the mossy fiber system performs this function. A second system, with a large number of associatively modifiable synapses on each receiving cell, is needed in order to relay a signal specific enough to initiate the retrieval process. This may be identified, we propose, with the perforant path input to CA3. Key words: hippocampus, autoassociative memory, attractor neural networks, associative synapses, information storage

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. It has been proposed that the receptive fields of neurons in V1 are optimised to generate "sparse", Kurtotic, or "interesting" output probability distributions (Barlow & Tolhurst, 1992; Barlow, 1994; Field, 1994; Intrator & Cooper, 1991; Intrator, 1992). We investigate the empirical evidence for this further and argue that filters can produce "interesting" output distributions simply because natural images have variable local intensity variance. If the proposed filters have zero D.C., then the probability distribution of filter outputs (and hence the output Kurtosis) is well predicted simply from these effects of variable local variance. This suggests that finding filters with high output Kurtosis does not necessarily signal interesting image structure. It is then argued that finding filters that maximise output Kurtosis generates filters that are incompatible with observed physiology. In particular the optimal difference--of--Gaussian (DOG) filter should have the smallest possible s...

Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, temporary storage in the hippocampus, and a slow, longterm storage within the neocortex. This paper presents a neural network model of the hippocampal episodic memory inspired by Damasio's idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a binding layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial activation of the stored features activates the binding pattern, which in turn reactivates the entire stored pattern. For many configurations of the model, a theoretical lower bound for the memory capacity can be derived, and it can be an order of magnitude or higher than the number of all units in the model, and several orders of magnitude higher than the number of binding-layer units. Computational simulations further indicate that the average capacity is an order of magnitude larger than the theoretical lower bound, and making the connectivity between layers sparser causes an even further increase in capacity. Simulations also show that if more descriptive binding patterns are used, the errors tend to be more plausible (patterns are confused with other similar patterns), with a slight cost in capacity. The convergence-zone episodic memory therefore accounts for the immediate storage and associative retrieval capability and large capacity of the hippocampal memory, and shows why the memory encoding areas can be much smaller than the perceptual maps, consist of rather coarse computational units, and be only sparsely connected t...

The distribution of responses of sensory neurons to ecological stimulation has been proposed to be designed to maximize information transmission, which according to a simple model would imply an exponential distribution of spike counts in a given time window. We have used recordings from inferior temporal cortex neurons responding to quasi-natural visual stimulation (presented using a video of everyday lab scenes, and a large number of static images of faces and natural scenes) to assess the validity of this exponential model and to develop an alternative simple model of spike count distributions. We find that the exponential model has to be rejected in 84% of cases (at the P ! 0:01 level). A new model, which accounts for the firing rate distribution found in terms of slow and fast variability in the inputs which produce neuronal activation, is rejected statistically in only 16% of cases. Finally, we show that the neurons are moderately efficient at transmitting information, ...

stimuli in the responses of single olfactory neurons in the primate orbitofrontal area, neuronal responses were measured to a set of seven to nine odorants in macaques performing an olfactory dis-crimination task. The population of neurons analyzed had responses that were significantly differential to the odorants. 2. Information theoretic analyses were applied to the responses of the neurons, and information measures were calculated from the firing rate of the responses and from the principal components of the responses. The information reflected by the firing rate of the response accounted for the majority of the information present (86%) when compared with the information derived from the first three principal components of the spike train. This indicated that temporal encoding had a very minor role in the encoding of olfac-tory information by single orbitofrontal olfactory cells. 3. The average information about which odorant was presented, averaged across the 38 neurons, was 0.09 bits, a figure that is low when compared with the information values previously published for the responses of temporal lobe face-selective neurons. 4. Application of information theoretic analyses to the re-sponses of these neurons showed how much information about which stimulus was delivered was present in the responses of indi-vidual neurons. It was found that for the majority of the neurons significant amounts of information were reflected about one or two of the odorants presented. 5. For each neuron, the information reflected in the responses of that neuron about the reinforcement value and the information about the identity of the odorants were calculated. It is shown that many neurons carry information about which of the odorants was presented; in addition, some neurons reflect information only about the taste association of the stimuli and not about odorant identity. 6. Measurements of the sparseness of the representation indi-cated that a broadly distributed representation of the identity of odorants was present in this population of neurons.

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...

this paper with describing linked temporal sequences of events.) The hippocampus is also implicated in spatial memory. For example, damage to the hippocampal system in monkeys produces de# cits in learning about where objects are and where responses must be made (Rolls 1996; Gaffan 1998), and in rats produces spatial learning de# cits (Martin et al. 2000). Neurophysiologically, hippocampal neurons in rats respond to the place where the animal is located (O'Keefe 1990; Kubie & Muller 1991; Wilson & McNaughton 1993), and in primates to the place being viewed (Rolls et al. 1997; Rolls 1999). It has thus been a long-standing question about whether the hippocampus and nearby temporal lobe structures are involved in episodic memory or spatial function. In this paper we show that this question can be resolved by revealing that a single neural network can implement both episodic and spatial memory