NDLI: A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features

Content Provider	IEEE Xplore Digital Library
Author	Pehlevan, C. Chklovskii, D.B.
Copyright Year	2014
Description	Author affiliation: Howard Hughes Med. Inst., Howard, WI, USA (Pehlevan, C.) \|\| Simons Center for Data Anal., Simons Found., New York, NY, USA (Chklovskii, D.B.)
Abstract	Despite our extensive knowledge of biophysical properties of neurons, there is no commonly accepted algorithmic theory of neuronal function. Here we explore the hypothesis that single-layer neuronal networks perform online symmetric nonnegative matrix factorization (SNMF) of the similarity matrix of the streamed data. By starting with the SNMF cost function we derive an online algorithm, which can be implemented by a biologically plausible network with local learning rules. We demonstrate that such network performs soft clustering of the data as well as sparse feature discovery. The derived algorithm replicates many known aspects of sensory anatomy and biophysical properties of neurons including unipolar nature of neuronal activity and synaptic weights, local synaptic plasticity rules and the dependence of learning rate on cumulative neuronal activity. Thus, we make a step towards an algorithmic theory of neuronal function, which should facilitate large-scale neural circuit simulations and biologically inspired artificial intelligence.
Starting Page	769
Ending Page	775
File Size	458585
Page Count	7
File Format	PDF
e-ISBN	9781479982974
DOI	10.1109/ACSSC.2014.7094553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Neurons Cost function Gabor filters Sparse matrices Biological information theory Feedforward neural networks feature learning neuron non-negative matrix factorization online clustering learning rules
Content Type	Text
Resource Type	Article

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