Title
Structural Plasticity Denoises Responses and Improves Learning Speed.
Abstract
Despite an abundance of computational models for learning of synaptic weights, there has been relatively little research on structural plasticity, i.e., the creation and elimination of synapses. Especially, it is not clear how structural plasticity works in concert with spike-timing-dependent plasticity (STDP) and what advantages their combination offers. Here we present a fairly large-scale functional model that uses leaky integrate-and-fire neurons, STDP, homeostasis, recurrent connections, and structural plasticity to learn the input encoding, the relation between inputs, and to infer missing inputs. Using this model, we compare the error and the amount of noise in the network's responses with and without structural plasticity and the influence of structural plasticity on the learning speed of the network. Using structural plasticity during learning shows good results for learning the representation of input values, i.e., structural plasticity strongly reduces the noise of the response by preventing spikes with a high error. For inferring missing inputs we see similar results, with responses having less noise if the network was trained using structural plasticity. Additionally, using structural plasticity with pruning significantly decreased the time to learn weights suitable for inference. Presumably, this is due to the clearer signal containing less spikes that misrepresent the desired value. Therefore, this work shows that structural plasticity is not only able to improve upon the performance using STDP without structural plasticity but also speeds up learning. Additionally, it addresses the practical problem of limited resources for connectivity that is not only apparent in the mammalian neocortex but also in computer hardware or neuromorphic (brain-inspired) hardware by efficiently pruning synapses without losing performance.
Year
DOI
Venue
2016
10.3389/fncom.2016.00093
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
structural plasticity,STDP,learning,spiking neural network,homoeostasis
Neuroscience,Computer science,Inference,Neuromorphic engineering,Computational model,Artificial intelligence,Spike-timing-dependent plasticity,Spiking neural network,Machine learning,Encoding (memory),Plasticity
Journal
Volume
ISSN
Citations 
10
1662-5188
1
PageRank 
References 
Authors
0.35
13
4
Name
Order
Citations
PageRank
Robin Spiess110.69
Richard George210.35
Matthew Cook328818.19
Peter U. Diehl42339.75