.png){kind=icon}
Return
Delay learning and polychronization for reservoir computing
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2007.12.027.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random topology and driven by STDP (spike-time-dependent plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that affects the synaptic delays linking the network to the readout neurons, with classification as a goal task. The network processing and the resulting performance can be explained by the concept of polychronization, proposed by Izhikevich [Polychronization: computation with spikes, Neural Comput. 18(2) (2006) 245-282], on physiological grounds. The model emphasizes that polychronization can be used as a tool for exploiting the computational power of synaptic delays and for monitoring the topology and activity of a spiking neuron network. (c) 2008 Elsevier B.V. All rights reserved.
Keywords:
reservoir computing
spiking neuron network
synaptic plasticity
STDP
polychronization
programmable delay
margin criterion
classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSaveCoincident pre- and postsynaptic activity modifies GABAergic synapses by postsynaptic changes in Cl- transporter activity.png)
NEURONOAAI
NEURONIF15
OAAIShareSaveShareSave
View more