The head-direction (HD) cells found in the limbic system in freely moving rats represent the instantaneous head direction of the animal in the horizontal plane regardless of the location of the animal. The internal direction represented by these cells uses both self-motion information for inet-tially based updating and familiar visual landmarks for calibration. Here, a model of the dynamics of the HD cell ensemble is presented. The sta-bility of a localized static activity profile in the network and a dynamic shift mechanism are explained naturally by synaptic weight distribution components with even and odd symmetry, respectively. Under symmetric weights or symmetric reciprocal connections, a stable activity profile close to the known direc-tional tuning curves will emerge. By adding a slight asymmetry to the weights, the activity profile will shift continuously without 1

such as the orientation of a line in the visual field or the location of Two main goals for reconstruction are approached in this the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which paper. The first goal is technical and is exemplified by the the physical variables are estimated from observed neural activity. population vector method applied to motor cortical activities Reconstruction is useful first in quantifying how much information during various reaching tasks (Georgopoulos et al. 1986, 1989; about the physical variables is present in the population and, second, Schwartz 1994) and the template matching method applied to in providing insight into how the brain might use distributed represen- disparity selective cells in the visual cortex (Lehky and Sejnowtations in solving related computational problems such as visual ob- ski 1990) and hippocampal place cells during rapid learning of ject recognition and spatial navigation. Two classes of reconstruction place fields in a novel environment (Wilson and McNaughton methods, namely, probabilistic or Bayesian methods and basis func- 1993). In these examples, reconstruction extracts information tion methods, are discussed. They include important existing methods from noisy neuronal population activity and transforms it to a

We analyze a model of navigational map formation based on correlation-based, temporally asymmetric potentiation and depression of synapses between hippocampal place cells. We show that synaptic modification during random exploration of an environment shifts the location encoded by place cell activity in such a way that it indicates the direction from any location to a fixed target avoiding walls and other obstacles. Multiple maps to different targets can be simultaneously stored if we introduce target-dependent modulation of place cell activity. Once maps to a number of target locations in a given environment have been stored, novel maps to previously unknown target locations are automatically constructed by interpolation between existing maps.

this article. Because there is no visible cue in the hidden-platform water maze task, it would not help the animal find the platform. 3. Route system. Routes stored in the hippocampus can be written out to the cortex, so that directions necessary to reach a goal are associated with local views. This is the system detailed in section 2.5 (see also section 4.3). This system requires training for each step the animal must take; it cannot learn to associate local views with directions to distant goals without hippocampal help (through route replay). The Role of the Hippocampus 97 If there were a way to show the animal the route to the goal, it might be possible to train the route system even without a hippocampus. Whishaw, Cassell, and Jarrard (1995) and Schallert, Day, Weisend, and Sutherland (1996) both showed ways to train the route system directly and found that animals could learn to solve the water maze even with hippocampal lesions. Whishaw et al. (1995) trained animals with fimbria/fornix lesions to find a visible platform and then removed the visible platform. These animals concentrated their search where the platform had been. Schallert et al. (1996) used animals with kainate-colchicine hippocampal lesions. The animals were first trained with a large platform that filled almost the entire maze. Once the animals could reach that platform reliably, it was shrunk trial by trial until it was the same size as a typical platform in a water maze task. Again, the animals could learn to solve the water maze without a hippocampus. 4.3 Where Is the Route System? Although the data are not yet conclusive, we suggest that the most likely candidate for anatomical instantiation of the route system is from posterior parietal to posterior cingulate cortex. There is a lot of evide...

We propose a computational model of the CA3 region of the rat hippocampus that is able to reproduce the available experimental data concerning the dependence of directional selectivity of the place cell discharge on the environment and on the spatial task. The main feature of our model is a continuous, unsupervised Hebbian learning dynamics of recurrent connections, which is driven by the neuronal activities imposed upon the network by the environment-dependent external input. In our simulations, the environment and the movements of the rat are chosen to mimic those commonly observed in neurophysiological experiments. The environment is represented as local views that depend on both the position and the heading direction of the rat. We hypothesize that place cells are intrinsically directional, that is, they respond to local views. We show that the synaptic dynamics in the recurrent neural network rapidly modify the discharge correlates of the place cells: cells tend to be...

Recent experiments indicate that the shape of maps of preferred orientation in the primary visual cortex does not depend on visual experience. We propose a network model which demonstrates that the orientation and direction selectivity of individual units and the structure of the corresponding angle maps could emerge from local recurrent connections. Our model reproduces the structure of the maps of preferred orientation and preferred direction and explains the origin of their interrelation. The model also provides a novel explanation for the correlation between position shifts of receptive fields and changes of preferred orientations of single neurons across the surface of the cortex.

The ability to associate some stimuli while differentiating between others is an essential characteristic of biological memory. Theoretical models identify memories as attractors of neural network activity, with learning based on Hebb-like synaptic modifications. Our analysis shows that when network inputs are correlated, this mechanism results in overassociations, or up to several memories ‘‘merging’ ’ into one. To counteract this tendency, we introduce a learning mechanism that involves novelty-facilitated modifications, accentuating synaptic changes proportionally to the difference between network input and stored memories. This mechanism introduces a dependency of synaptic modifications on previously acquired memories, enabling a wide spectrum of memory associations, ranging from absolute discrimination to complete merging. The model predicts that memory representations should be sensitive to learning order, consistent with recent psychophysical studies of face recognition and electrophysiological experiments on hippocampal place cells. The proposed mechanism is compatible with a recent biological model of novelty-facilitated learning in hippocampal circuitry.

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Attractor networks have been one of the most successful paradigms in neural computation, and have been used as models of computation in the nervous system. Recently, we proposed a paradigm called ‘latent attractors’ where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus—a brain region of fundamental significance for memory and spatial learning. Latent attractor networks are a special case of associative memory networks. The model studied here consists of a two-layer recurrent network with attractors stored in the recurrent connections using a clipped Hebbian learning rule. The firing in both layers is competitive—K winners take all firing. The number of neurons allowed to fire, K,issmaller than the size of the

Many theories of neural networks assume rules of connection between pairs of neurons that are based on their cell types or functional properties. It is finally becoming feasible to test such pairwise models of connectivity, due to emerging advances in neuroanatomical techniques. One method will be to measure the functional properties of connected pairs of neurons, sparsely sampling pairs from many specimens. Another method will be to find a ‘‘connectome,’ ’ a dense map of all connections in a single specimen, and infer functional properties of neurons through computational analysis. For the latter method, the most exciting prospect would be to decode the memories that are hypothesized to be stored in connectomes. In constructing a neural network model of brain function, it is standard to start from a mathematical description of spiking and synaptic transmission, make assumptions about how neurons are connected by synapses and then numerically simulate or analytically derive the activity patterns of the network. Success is declared if the model’s activity patterns reproduce those measured by neurophysiologists. Initially, the model neurons used in such networks were highly