University of Sussex
Browse

File(s) not publicly available

On the equivalence of Hebbian learning and the SVM formalism

presentation
posted on 2023-06-09, 01:57 authored by Thomas NowotnyThomas Nowotny, Ramón Huerta
We show that it is possible to relate the Support Vector Machine formalism to Hebbian Learning in the context of olfactory learning in the insect brain. Since neurons cannot have negative firing rates, two neurons and synaptic inhibition are required to encode a binary classification problem in a biologically realistic way. We show that the two neuron system with plausible Hebbian learning rules can be mapped to a large margin classifier. Two formalisms are analyzed: regular SVMs and the so-called inhibitory SVMs. The regularization term in regular SVMs brings the synaptic vectors of the two neurons close to each other, while the inhibitory SVM can bring them to 0 resembling the memory loss process in Hebbian learning. Based on the analogy to large margin classifiers we also predict the existence of a negative Hebbian leaning rule for negative reinforcement signals.

Funding

Olfactory Coding in the Insect Pheromone Pathway: Models and Experiments; G0299; BBSRC-BIOTECHNOLOGY & BIOLOGICAL SCIENCES RESEARCH COUNCIL; BB/F00513/1

History

Publication status

  • Published

Page range

1-4

Presentation Type

  • paper

Event name

Information Sciences and Systems (CISS), 2012 46th Annual Conference on

Event location

Princeton, NJ

Event type

conference

Event date

21-23 March 2012

Book title

2012 46th Annual Conference on Information Sciences and Systems (CISS)

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2016-06-30

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC