BRAIN-I-NETS · Novel Brain-Inspired Learning Paradigms for Large-Scale Neuronal Networks

Current designs of neurally inspired computing systems rely on learning rules that appear to be insufficient to port the superior adaptive and computational capabilities of biological neural systems into large-scale recurrent neural hardware system. This is not surprising, since most of these learning rules had to be extrapolated from results of neurobiological experiments in vitro. New experimental techniques in neurobiology - such as 2-photon laser-scanning microscopy, optogenetic cell activation, and dynamic clamp techniques - make it now possible to record the changes that really take place in the intact brain during learning. First results indicate that the rules for synaptic plasticity have in fact to be rewritten. In particular, it appears that local synaptic plasticity is gated in multiple ways by global factors such as neuromodulators and network states. One primary goal of this project is to apply and extend new cutting-edge experimental techniques to produce a set of rules for synaptic plasticity and network reorganisation that describe the actual adaptive processes that take place in the living brain during learning.These new rules will be analysed by computational neuroscience experts and their consequences for learning in simulated large-scale networks of neurons and neurally inspired computing systems will be ascertained. The goal of this project is to port essential aspects of learning in the intact brain into current and next-generation neuromorphic hardware. New interchangeable software tools, that have recently been developed in the FP6 project FACETS, will be employed to carry out these investigations. Open questions that arise in these modelling studies will be addressed by changes in experimental protocols of the neuroscientists, building on long standing interdisciplinary collaborations among the partners.