Sussex Neuroscience

Professor Phil Husbands

Phil HusbandsMeta plasticity and reconfiguration in motor circuits: embodied models

The ways in which multiple adaptive processes, operating at different temporal and spatial scales, interact in the nervous system are not well understand but have a crucial role in the generation of behaviour.  Modulatory mechanisms play an important part in such interactions, e.g. in meta-plasticity (the plasticity of plasticity) and in the operation of multi-functional networks – networks capable of generating multiple and distinct behavioural patterns as they reconfigure into different functional circuits under modulatory influences.  Recent work has shown how complex network dynamics, including oscillatory and chaotic dynamics, can both give rise to and arise from such interactions. Understanding such dynamics is an important part of the puzzle.

Such interactions are widespread in invertebrate and vertebrate species and are often at the heart of sophisticated decision making and behavioural switching. A better characterisation of how they work is fundamental to advancing knowledge of the nervous system, but will also provide important new principles for the design of complex artificial systems.

The aim of this project is to develop embodied models of some of these mechanisms: models of whole behaviour generating systems in which simulated neuronal networks act as the nervous system for robotic or simulated agents engaged in sensorimotor behaviours. The project will (at least initially) concentrate on motor behaviours and will make use of relevant empirical studies in invertebrate neuroscience. 

This project would suit students with a strong computational/mathematical background and an interest in biorobotics. Training in robotic and simulation techniques will be provided.

Relevant recent publications

(For full list of publications and more details about the lab, please visit: http://www.sussex.ac.uk/Users/philh/index.html)

Moioli, R. and Husbands, P. (2013)  Neuronal Assembly Dynamics in Supervised and Unsupervised Learning Scenarios, Neural Computation .

Yoonsik Shim and  Phil Husbands (2012) Chaotic Exploration and Learning of Locomotion Behaviours. Neural Computation 24(8):2185-2222.  

Moioli, R., Vargas, P. and Husbands, P. (2012) Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent, Biological Cybernetics 106(6-7):407–427.

McGregor, S., Vasas, V., Husbands, P. and  Fernando, C. (2012) Evolution of associative learning in chemical networks, PLoS Computational Biology  8(11): e1002739. doi:10.1371/journal.pcbi.1002739