Abstract:
:The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework (NEF) offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify-arbitrarily-neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.
journal_name
Front Neuroscijournal_title
Frontiers in neuroscienceauthors
Tapson JC,Cohen GK,Afshar S,Stiefel KM,Buskila Y,Wang RM,Hamilton TJ,van Schaik Adoi
10.3389/fnins.2013.00153subject
Has Abstractpub_date
2013-08-30 00:00:00pages
153eissn
1662-4548issn
1662-453Xjournal_volume
7pub_type
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