Developmental Neural Networks

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This project models the rules of biological self-organisation that can generate neural circuits. Rules governing development are used to "grow" the inter-connections between the neurons. Currently this development model "grows" 3 D layers of Neural Networks using local chemical gradients emitted by neurons that influence both the growth direction of other neurites (axons and dendrites) and their propensity to split. It has been used to generate a large-scale structure based on a biologically plausible model of a retina that does edge detection and, for individual neurons, to produce the appropriate spiking behaviour when presented with a stimulus. A genetic algorithm is used to search the parameter space to determine the best parameter values for the development rules.

A multi-compartmental neuron simulator (called NEURON), that used partial differential equations, was used when investigating the neuron firing patterns. This model is too slow for networks of neurons and a faster simulation, that uses an adaption of a finite state machine to tree structures, has been partially developed.

The PhD programme will either:

1. Complete the new multi-compartmental neuron simulator and integrate it with the developmental model. The final combined models will be used to look at the effect of dendritic geometry on function, and possibly to define classes of dendritic geometries.
or

2. The model for growing neural networks will be extended to include the concept of genetic regulatory networks. A genetic regulatory network is the intricate and recursive network of activation and repression mechanisms formed by proteins that act on genes to determine whether and when they should generate other proteins. As such it can be used to model interactions between the parameters that control the developing neural network over time.

An interest in neuroscience and biology would be beneficial for this project.

To discuss this project please contact Rod Adams.