Description: Limits to the development of feed-forward structures in large recurrent neuronal networks

This title appears in the Scientific Report : 2013

Limits to the development of feed-forward structures in large recurrent neuronal networks

Spike-timing dependent plasticity (STDP) has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite this high level of interest, convincing demonstrati...

Personal Name(s):	Kunkel, Susanne (Corresponding author)
	Diesmann, Markus / Morrison, Abigail
Contributing Institute:	Theoretical Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6
Published in:	Frontiers in computational neuroscience, 4 (2010) 160, S. 1-15
Imprint:	Lausanne Frontiers Research Foundation 2010
DOI:	10.3389/fncom.2010.00160
PubMed ID:	21415913
Document Type:	Journal Article
Research Program:	Helmholtz Alliance on Systems Biology Brain-inspired multiscale computation in neuromorphic hybrid systems Signalling Pathways and Mechanisms in the Nervous System
Link:	OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/5348 in citations.
Please use the identifier: http://dx.doi.org/10.3389/fncom.2010.00160 in citations.

Spike-timing dependent plasticity (STDP) has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite this high level of interest, convincing demonstrations of this capacity in large, initially random networks have not been forthcoming. Such demonstrations as there are typically rely on constraining the problem artificially. Techniques include employing additional pruning mechanisms or STDP rules that enhance symmetry breaking, simulating networks with low connectivity that magnify competition between synapses, or combinations of the above. In this paper, we first review modeling choices that carry particularly high risks of producing non-generalizable results in the context of STDP in recurrent networks. We then develop a theory for the development of feed-forward structure in random networks and conclude that an unstable fixed point in the dynamics prevents the stable propagation of structure in recurrent networks with weight-dependent STDP. We demonstrate that the key predictions of the theory hold in large-scale simulations. The theory provides insight into the reasons why such development does not take place in unconstrained systems and enables us to identify biologically motivated candidate adaptations to the balanced random network model that might enable it.