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Network reduction techniques for models in computational neuroscience


This workshop will focus on the reduced and mathematical descriptions for the activity of neural networks. As opposed to computationally expensive simulations, these reduced models give a concise description of neural activity across large networks. In this form, the neural activity can be fully characterised using a variety of mathematical and computational methods.

The primary objective of the workshop is to introduce participants to a variety of novel techniques in mathematical neuroscience. In order to be accessible for an audience with broad backgrounds, all presentations will have a strong didactic component.

In the first session, we will focus on model formulation and mathematical techniques for analysis. Subjects to be covered will include, spiking network reduction, equation free modeling and stochastic extensions to contemporary models.

In the second session, we will address practical aspects of reduced models, such as overcoming computational challenges and the application of these models to concrete problems in neuroscience.

With imaging techniques becoming able to image network activity at meso-scale networks, new mathematical methods are required to interpret the results. The Bernstein conference is an ideal mechanism to facilitate this knowledge exchange and foster stronger links between mathematical, computational and experimental neuroscience.


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