Brain-i-Nets Research -- Summary
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 are 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, are employed to carry out these investigations. Open questions that arise in these modelling studies are addressed by changes in experimental protocols of the neuroscientists, building on long standing interdisciplinary collaborations among the partners.
Long-term Vision
The overall long-term vision of this project is
to develop new design principles for adaptive, reconfigurable very-large-scale hardware systems implementing novel learning rules inspired by biological neural networks in vivo.
Learning mechanisms implemented in the brain appear to be much more robust and flexible than those currently used in neurally inspired computing systems. To confer the superior adaptive and computational capabilities of biological neural systems to large-scale recurrent neural hardware systems and other novel massively parallel computing devices, new and more sophisticated learning rules are needed.
Our long-term vision is that the learning rules for global gating of local learning, identified and explored in this project, will become ideal candidates for implementation in hardware. Conceptually the interaction of local factors that can be monitored and stored at the site of each connection with one or a few global factors is very attractive for hardware implementation. Previous collaborations of several partners of the project have shown that networks of spiking neurons can be implemented in a truly large-scale, parallel, mixed analog-digital hardware system. The inclusion of learning rules that go beyond the classical Hebbian or STDP rules for unsupervised learning, by including a third factor representing for example information on saliency or reward, will advance the hardware into a regime where a much broader class of learning tasks can be solved by these ultra-rapid machines.
