A computational model of the interaction between external and internal cues for the control of hippocampal place cells (original) (raw)
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Latent attractors: a model for context-dependent place representations in the hippocampus
Neural Computation, 2000
Cells throughout the rodent hippocampal system show place-speci c patterns of ring called place elds, creating a coarse-coded representation of location. The dependencie s of this place code-or cognitive map-on sensory cues have been investigated extensively, and several computational models have been developed to explain them. However, place representations also exhibit strong dependenc e on spatial and behavioral context, and identical sensory environments can produce very different place codes in different situations. Several recent studies have proposed models for the computational basis of this phenomenon , but it is still not completely understood. In this article, we present a very simple connectionist model for producing context-depende nt place representations in the hippocampus. We propose that context dependenc e arises in the dentate gyrus-hilus (DGH) system, which functions as a dynamic selector, disposing a small group of granule and pyramidal cells to re in response to afferent stimulus while depressing the rest. It is hypothesized that the DGH system dynamics has "latent attractors," which are unmasked by the afferent input and channel system activity into subpopulations of cells in the DG, CA3, and other hippocampal regions as observed experimentally. The proposed model shows that a minimally structured hippocampus-like system can robustly produce context-dependent place codes with realistic attributes.
Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity
Biological Cybernetics, 2000
A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On the level of the hippocampus, allothetic and idiothetic representations are integrated to yield a stable representation of the environment by a population of localized overlapping CA3-CA1 place fields. The hippocampal spatial representation is used as a basis for goal-oriented spatial behavior. We focus on the neural pathway connecting the hippocampus to the nucleus accumbens. Place cells drive a population of locomotor action neurons in the nucleus accumbens. Reward-based learning is applied to map place cell activity into action cell activity. The ensemble action cell activity provides navigational maps to support spatial behavior. We present experimental results obtained with a mobile Khepera robot.
A feed-forward model of spatial and directional selectivity of hippocampal place cells
In recent years a wealth of studies have focused on the role of the Hippocampus in spatial learning and navigation, triggered by the finding of place sensitive cells in this area. These cells have been interpreted as being responsible for coding a representation of space [9, 10]. Even though the term place cell suggests that location is the unique determinant of firing of hippocampal cells, there exist several other factors which influence hippocampal activity.
An attractor model for hippocampal place cell hysteresis
Neurocomputing, 2001
It is well-known that identical sensory input under di!erent perceptual, behavioral or contextual conditions can produce distinct patterns of activity in the place cells of the rodent hippocampus. However, the mechanisms underlying this have not been completely clari"ed. A recent experiment has shown that place cell activity on a 3-arm maze exhibits hysteresis as the maze is rotated with respect to distal cues. The apparent angular extent of a place "eld is greater when a maze arm rotates out of it than when it rotates back into the "eld. In this report, we present a simple attractor-based model of the hippocampus that reproduces this hysteresis phenomenon. The model allows us to make predictions about changes in the hysteresis e!ect as the animal becomes more familiar with the maze in several orientations. It also has implications for the place "eld remapping phenomenon seen in many hippocampal experiments.
Modeling Place Fields in Terms of the Cortical Inputs to the Hippocampus
2000
A model of place-cell firing is presented that makes quantitative predictions about specific place cells' spatial receptive fields following changes to the rat's environment. A place cell's firing rate is modeled as a function of the rat's location by the thresholded sum of the firing rates of a number of putative cortical inputs. These inputs are tuned to respond whenever an environmental boundary is at a particular distance and allocentric direction from the rat. The initial behavior of a place cell in any environment is simply determined by its set of inputs and its threshold; learning is not necessary. The model is shown to produce a good fit to the firing of individual place cells, and populations of place cells across environments of differing shape. The cells' behavior can be predicted for novel environments of arbitrary size and shape, or for manipulations such as introducing a barrier. The model can be extended to make behavioral predictions regarding spatial memory. Hippocampus 2000;10:369 -379.
A Simulation of Parahippocampal and Hippocampal Structures Guiding Spatial Navigation of
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Behavioural Brain Research, 2001
Rats learned to find the baited corner of a box surrounded by a curtain, regardless of whether they had a fixed or random point of entry (POE) through the curtain. On probe trials, rats used an internal direction sense carried from outside the curtain to solve the problem, and only used the visual cue inside the curtain if disoriented and denied access to a view of the room en route. Similar disorientation procedures were required to obtain cue control of hippocampal place fields. The results suggest that: (1) POE effects previously found in the water maze may be task-specific; (2) an undisrupted internal sense of direction carried from one environment to another may provide the preferred solution to spatial problems in the second environment, even when this second environment is a familiar one with stable visual cues; and (3) choice behaviour is sometimes, but not always, representative of the hippocampal representation of space.
Episodes in Space: A Modeling Study of Hippocampal Place Representation
Lecture Notes in Computer Science, 2008
A computer model of learning and representing spatial locations is studied. The model builds on biological constraints and assumptions drawn from the anatomy and physiology of the hippocampal formation of the rat. The emphasis of the presented research is on the usability of a computer model originally proposed to describe episodic memory capabilities of the hippocampus in a spatial task. In the present model two modalities -vision and path integration -are contributing to the recognition of a given place. We study how place cell activity emerges due to Hebbian learning in the model hippocampus as a result of random exploration of the environment. The model is implemented in the Webots mobile robotics simulation software. Our results show that the location of the robot is well predictable from the activity of a population of model place cells, thus the model is suitable to be used as a basic building block of location-based navigation strategies. However, some properties of the stored memories strongly resembles that of episodic memories, which do not match special spatial requirements.