Technical integration of hippocampus, basal ganglia and physical models for spatial navigation

Charles Fox

doi:10.3389/neuro.11.006.2009

Abstract

Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large-scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings.

Highlights

As computational resources inexorably grow, computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups
We report on our preliminary integration of recent hippocampal formation and basal ganglia models, both proposed components of the neural system for spatial navigation (Redish and Touretzky, 1997)
We have found Python’s default and named arguments to be especially useful in this type of modeling

Summary

Introduction

As computational resources inexorably grow, computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. Requiring the neural models to generate appropriate behavioural output using only inputs available in the environment is a strong test of the proposed computations of that neural system (Humphries et al, 2005; Prescott et al, 2006). In such large simulations, development time is as much an issue as computation time – to implement and test the models, construct simulated environments, implement realistic sensors, and so on. This paper shows how Python provides an excellent solution to both development and computation time problems; we discuss how Python can work with platforms designed for such large-scale integration (Mitchinson et al, 2008)

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Conclusion