Learning Universal Computations with Spikes.
Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.
Donders Institute, Department of Biophysics, Radboud University, Nijmegen, Netherlands.
Dominik
D
Thalmeier
Max Planck Institute for Psycholinguistics, Department for Neurobiology of Language, Nijmegen, Netherlands.|Donders Institute, Department for Neuroinformatics, Radboud University, Nijmegen, Netherlands.
Marvin
M
Uhlmann
Donders Institute, Department of Biophysics, Radboud University, Nijmegen, Netherlands.
Hilbert J
HJ
Kappen
Donders Institute, Department for Neuroinformatics, Radboud University, Nijmegen, Netherlands.|Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
Raoul-Martin
RM
Memmesheimer
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D019295:Computational Biology; D006801:Humans; D007858:Learning / Q000502:physiology*; D057567:Memory, Long-Term / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission / Q000502:physiology