A minimal synaptic architecture is proposed for how the brain might perform path integration by computing the next internal representation of self-location from the current representation and from the perceived velocity of motion. In the model, a place-cell assembly called a “chart ” contains a twodimensional attractor set called an “attractor map ” that can be used to represent coordinates in any arbitrary environment, once associative binding has occurred between chart locations and sensory inputs. In hippocampus, there are different spatial relations among place fields in different environments and behavioral contexts. Thus, the same units may participate in many charts, and it is shown that the number of uncorrelated charts that can be encoded in the same recurrent network is potentially quite large. According to this theory, the firing of a given place cell is primarily a cooperative effect of the activity of its

Previous studies have shown that hippocampal place fields are controlled by the salient sensory cues in the environ-ment, in that rotation of the cues causes an equal rotation of the place fields. We trained rats to forage for food pellets in a gray cylinder with a single salient directional cue, a white card covering 90 ” of the cylinder wall. Half of the rats were disoriented before being placed in the cylinder, in or-der to disrupt their internal sense of direction. The other half were not disoriented before being placed in the cylin-der; for these rats, there was presumably a consistent re-lationship between the cue card and their internal direction sense. We subsequently recorded hippocampal place cells and thalamic head direction cells from both groups of rats as they moved in the cylinder; between some sessions the cylinder and cue card were rotated to a new direction. All

Our model of the spatial and temporal aspects of place cell firing, and their role in rat navigation is reviewed. The model provides a can- didate mechanism, at the level of individual cells, by which place cell information concerning self-localization could be used to guide navi- gation to previously visited reward sites. The model embodies specific predictions regarding the formation of place fields, the phase coding of place cell firing with respect to the hippocampal theta rhythm, and the formation of neuronal population vectors downstream from the place cells that code for the directions of goals during navigation. Re- cent experiments regarding the spatial distribution of place cell firing have confirmed our initial modeling hypothesis, that place fields are formed from Gaussian tuning curve inputs coding for the distances from environmental features, and enabled us to further specify the functional form of these inputs. Other recent experiments regarding the temporal distribution of place cell firing in 2-dimensional environ- ments have confirmed our predictions based on the temporal aspects of place cell firing on linear tracks. Directions for further experiments and refinements to the model are outlined for the future.

this paper) whenever the eyes were still (to within typically 1) during the record, and calculated the firing rate together with where the monkey was looking during that record. The next record was taken immediately after the preceding one, if there was no eye movement. (The findings described in this paper were unaffected if alternatively a new record was taken only when a new eye movement was made.) The algorithm could lag its neuronal data collection a short latency later than the eye position data. (If the neuron started to respond 100 ms after the monkey moved his eyes to an effective location in space, this lag could be set to 100 ms. In practice, the lag was set for all neurons to a value of 50 ms.) From the records containing a firing rate and the place of the monkey, the head direction and the eye position, it was possible to plot diagrams and perform statistical and information theoretic analyses of the firing rate of the cell when different locations in the room were being viewed, and also in relation to eye position, place and head direction. For allocentric position, the records were binned typically into 64 bins horizontally (16 for each wall) and 16 vertically. For the information analysis, the data were further quantized into typically 16 bins, in order to provide the numbers of samples needed for the information analyses. With an average recording time for any given statistical analysis of 2.76 min, the average total number of spikes recorded from a spatial view cell was 465 spikes, leading to the average rate of a spatial view cell during these experiments of 2.8 spikes/s. (For comparison, it is of interest to note that the average firing rate of the spatial view cells described here during locomotion when the monkey is visually exploring all parts of ...

During slow wave sleep (SWS), traces of neuronal activity patterns from preceding behavior can be observed in rat hippocampus and neocortex. The spontaneous reactivation of these patterns is manifested as the reinstatement of the distribution of pairwise firing-rate correlations within a population of simultaneously recorded neurons. The effects of behavioral state [quiet wakefulness, SWS, and rapid eye movement (REM)], interactions between two successive spatial experiences, and global modulation during 200 Hz electroencephalographic (EEG) “ripples ” on pattern reinstatement were studied in CA1 pyramidal cell population recordings. Pairwise firing-rate correlations during often repeated experiences accounted for a significant proportion of the variance in these interactions in subsequent SWS or quiet wakefulness and, to a lesser degree, during SWS before the experience on a given day. The latter effect was absent for novel experiences, suggesting that a persistent memory trace develops with experience. Pattern The hippocampus is thought to play an important role in the acquisition and consolidation of certain forms of memory. Lesions of the hippocampus lead to a temporally graded retrograde amnesia, suggesting that the hippocampus plays a role in the initial encoding of a memory but that, with time, the memory becomes independent of the hippocampus (Scoville and Milner,

The rat hippocampus contains cells that are characterized by location-specific firing. Previous work has shown that the angular position of hippocampal place cell firing fields is accurately controlled by the position of visual cues, suggesting that vision plays a important role in triggering place cell activity. However, a role for other types of information has also been suggested because place cell activity can be recorded while animals are moving in the darkness. In this study, we asked whether place fields can get established in rats that have never One of the most intriguing features of the rat hippocampus is the existence of place cells. First discovered by O’Keefe and Dostrovsky (1971), such cells, when recorded extracellularly from a freely moving rat, have the remarkable characteristic of being active only when the animal is in a specific region of its environment. Thus, a given place cell fires in a spatially delimited area

A number of computational models of hippocampal place cells incorporate attractor neural network architecture to simulate key findings in the place cell literature, including the properties of pattern completion, firing in the absence of visual input, and nonlinear responses to environmental manipulations. To test for evidence of attractor dynamics, ensembles of place cells were recorded using multiple-tetrode techniques. After many days of experience in an environment with salient local surface cues on a circular track and salient distal landmarks on the wall, the local surface cues were rotated as a set in opposition to the distal landmarks. The amount of mismatch between the local and distal sets of cues varied from 45 to 180°. If place cells were parts of strong attractors, then their place fields should Principal neurons of the rat hippocampus fire selectively in restricted locations of an environment (O’Keefe and Dostrovsky, 1971; Muller et al., 1987). Debate continues over whether these place cells are best described as the neural substrate of a cognitive map of the environment (O’Keefe and Nadel, 1978) or as the components of a more general relational learning system (Cohen and Eichenbaum, 1993). One reason for the continued debate is that few rules have been defined that describe precisely the nature of the interactions between the myriad sources of input onto place cells. Although place cells can be controlled by visual landmarks (O’Keefe and Conway, 1978; Muller and Kubie, 1987), this control is not absolute, and idiothetic cues and local surface cues can exert control over the cells in nonlinear ways (Young et

Whether hippocampal map realignment is coupled more strongly to position or time was studied in rats trained to shuttle on a linear track. The rats were required to run from a start box and to pause at a goal location at a fixed location relative to stable distal cues (room-aligned coordinate frame). The origin of each lap was varied by shifting the start box and track as a unit (box-aligned coordinate frame) along the direction of travel. As observed by Gothard et al. (1996a), on each lap the hippocampal activity realigned from a representation that was box-aligned to one that was room-aligned. We studied the dynamics of this transition using a measure of how well the moment-by-moment ensemble activity matched the expected activity given the location of the animal in each coordinate frame. The coherency ratio, defined as the ratio of the matches for the two coordinate systems, provides a quantitative measure of the ensemble activity alignment and was used to compare four possible descriptions of the realignment process. The elapsed time since leaving the box provided a better predictor of the occurrence of the transition than any of the three spatial parameters investigated, suggesting that the shift between coordinate systems is at least partially governed by a stochastic, time-dependent process. Key words: place cell; hippocampus; tetrode; spatial navigation; attractor map; coherency ratio Hippocampal pyramidal cells (“place cells”) display remarkable correlations with the position of an animal within an environment (the “place field ” of the cell; O’Keefe and Dostrovsky, 1971) (for review, see Redish, 1999). Both internal (e.g., vestibular and proprioceptive) and external (e.g., exterosensory) cues contribute to the generation of location-specific activity of hippocampal pyramidal

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

We present a model of the firing of place and head-direction cells in rat hippocampus. The model can predict the response of individual cells and populations to parametric manipulations of both geometric (e.g. O'Keefe & Burgess, 1996) and orientational (Fenton et al., 2000a) cues, extending a previous geometric model (Hartley et al., 2000). It provides a functional description of how these cells' spatial responses are derived from the rat's environment and makes easily testable quantitative predictions. Consideration of the phenomenon of remapping (Muller & Kubie, 1987; Bostock et al., 1991) indicates that the model may also be consistent with nonparametric changes in firing, and provides constraints for its future development.