Despite the fact that modern computer chips are extremely fast devices in comparison with neurons nevertheless even today’s fastest computers are much less effective in intelligent tasks than biological systems. The basic reason for this inequality stems from a difference in algorithmic approaches to information processing in computer science compared with that of living systems. The goal of our research is to discover basic computational principles of information processing in different living systems ranging from a simple organism’s nervous system to the human brain.

Current research projects in this field:

• Brain-Inspired Neuronal Model of Attention and Memory (EPSRC funded)

Collaboration with Dr. David Chik (UoP), Dr. Yakov Kazanovich (IMPB RAS, Pushchino, Russia). Selective visual attention is a cognitive process that allows a living organism to extract from the incoming visual information the part that is most important and should be processed in more detail. A large-scale brain-inspired model of hierarchically organised spiking neurons will be developed, that solves the problem of consecutive selection of objects by combining object oriented attention, memory, and novelty detection.

• CARMEN: Code analysis, repository, and modelling for e-Neuroscience, (EPSRC funded e-Science Pilot Project).

The project currently involves a consortium of academic investigators from 11 universities as well as commercial and international associates. Research in neurophysiology includes both analysis of data from neuronal systems and development of models to explain both the processes that form the character of data, and the high level behavioural functions. The project will provide integrated and co-ordinated services for the neuroscience data, enabling neuronal signal detection, sorting and analysis, as well as visualisation and modelling.

• Mathematical framework for modelling of memory.

Collaboration with Prof. Frank Hoppensteadt (Courant Institute, NYU). How can a brain maintain stable memories and behaviours when its underlying electrical and chemical structures are constantly changing? We investigate this stability problem thinking that the state variables (voltages, ionic currents, etc.) are governed by a complex system shaping the mnemonic landscape. We consider how it acts as a probability density function to guide slow parameter dynamics, and how the parameters shape the network output.

• Mathematical models of deep brain stimulation (supported by British Council).

In collaboration with Dr. Xuguang Liu and Dr. Nada Yousif (Imperial College London), Prof. Peter Ashwin (Exeter University), Prof. Tassilo Kuepper Cologne University, Germany). Deep Brain Stimulation (DBS) is the standard therapy for movement disorders such as Parkinson's disease, and in addition this therapy can be used to treat epilepsy, chronic pain and psychiatric diseases (e.g. depression). Although it is widely accepted that DBS is effective there remains a lack of understanding of the basic neuronal and biophysical mechanisms underlying DBS. It is extremely challenging to tackle this problem from mathematical and computational modelling.

• Modelling the anatomical structure and neural activity of tadpole spinal cord.

Collaboration with PhD student Tom Cooke (UoP), Prof. Alan Roberts (University of Bristol). How specific are the synaptic connections formed as neuronal networks develop and can simple rules account for the formation of functioning circuits? These questions are assessed in the spinal circuits controlling swimming in hatchling frog tadpoles. Experimental results suggest that synapse formation may not require axons to recognise specific, correct dendrites. To test the plausibility of hypotheses, we develop and study biologically realistic models of anatomy and electrophysiology of neural tadpole spinal cord.