Abstract
Spatial navigation requires the processing of complex, disparate and often ambiguous sensory data. The neurocomputations underpinning this vital ability remain poorly understood. Controversy remains as to whether multimodal sensory information must be combined into a unified representation, consistent with Tolman's “cognitive map”, or whether differential activation of independent navigation modules suffice to explain observed navigation behaviour. Here we demonstrate that key neural correlates of spatial navigation in darkness cannot be explained if the path integration system acted independently of boundary (landmark) information. In vivo recordings demonstrate that the rodent head direction (HD) system becomes unstable within three minutes without vision. In contrast, rodents maintain stable place fields and grid fields for over half an hour without vision. Using a simple HD error model, we show analytically that idiothetic path integration (iPI) alone cannot be used to maintain any stable place representation beyond two to three minutes. We then use a measure of place stability based on information theoretic principles to prove that featureless boundaries alone cannot be used to improve localization above chance level. Having shown that neither iPI nor boundaries alone are sufficient, we then address the question of whether their combination is sufficient and – we conjecture – necessary to maintain place stability for prolonged periods without vision. We addressed this question in simulations and robot experiments using a navigation model comprising of a particle filter and boundary map. The model replicates published experimental results on place field and grid field stability without vision, and makes testable predictions including place field splitting and grid field rescaling if the true arena geometry differs from the acquired boundary map. We discuss our findings in light of current theories of animal navigation and neuronal computation, and elaborate on their implications and significance for the design, analysis and interpretation of experiments.
Highlights
A ‘‘Cognitive Map’’ Is Multimodal but Not Modular In 1948, Tolman employed two analogies to describe the prevailing classes of models used to explain the experimental data on maze navigation and learning obtained from rats [1]
We first characterized the performance of idiothetic path integration (iPI) using the head direction (HD) error model developed from empirical data as described in Methods
A series of unimodal and polymodal firing fields were simulated using a particle filter to mimic place fields and grid fields under various experimental conditions. These simulations tested whether it is computationally plausible for observed place and grid field stability to be maintained for 30 minutes or more using only iPI and a featureless boundary map, given an erroneous HD system
Summary
A ‘‘Cognitive Map’’ Is Multimodal but Not Modular In 1948, Tolman employed two analogies to describe the prevailing classes of models used to explain the experimental data on maze navigation and learning obtained from rats [1]. Tolman likened the stimulus-response class of models to an old fashioned telephone exchange, where incoming calls are linked via connecting switches to outgoing messages. Tolman was a proponent of the field theoretic or cognitive map class of models, in which the telephone switchboard was replaced by a ‘‘map control room’’. Tolman asserted that sensory inputs ‘‘are usually worked over and elaborated in the central control room into a tentative, cognitive-like map of the environment’’. Can a model without a cognitive-like map of the environment explain animal navigation data?
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