24 min
Understanding the role of neural heterogeneity in learning
Computational Neuroscience, Machine Learning, Mathematical Modeling and seminar by Dr Nicolas Perez-Nieves, Imperial College London
Hosted by SNUFA
Tuesday 18:10–19:20 Berlin (GMT+1)
Recording available
Recording
Abstract
The brain has a hugely diverse and heterogeneous nature. The exact role of heterogeneity has been relatively little explored as most neural models tend to be largely homogeneous. We trained spiking neural networks with varying degrees of heterogeneity on complex real-world tasks and found that heterogeneity resulted in more stable and robust training and improved training performance, especially for tasks with a higher temporal structure. Moreover, the optimal distribution of parameters found by training was found to be similar to experimental observations. These findings suggest that heterogeneity is not simply a result of noisy biological processes, but it may play a crucial role for learning in complex, changing environments.
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