Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron
Abstract: Spike pattern classification is a key topic in machine learning, computational neuroscience, and electronic device design. Here, we offer a new supervised learning rule based on Support Vector Machines (SVM) to determine the synaptic weights of a leaky integrate-and-fire (LIF) neuron model for spike pattern classification. We compare classification performance between this algorithm and other methods sharing the same conceptual framework. We consider the effect of postsynaptic potential (PSP) kernel dynamics on patterns separability, and we propose an extension of the method to decrease computational load. The algorithm performs well in generalization tasks. We show that the peak value of spike patterns separability depends on a relation between PSP dynamics and spike pattern duration, and we propose a particular kernel that is well-suited for fast computations and electronic implementations
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Frontiers in Computational Neuroscience. 6 (2012), 78, DOI 10.3389/fncom.2012.00078, issn: 1662-5188
IN COPYRIGHT http://rightsstatements.org/page/InC/1.0 rs
- Klassifikation
-
Informatik
- Schlagwort
-
Lehrmaschine
- Ereignis
-
Veröffentlichung
- (wo)
-
Freiburg
- (wer)
-
Universität
- (wann)
-
2012
- Urheber
- Beteiligte Personen und Organisationen
-
Fakultät für Biologie
Bernstein Center Freiburg
Albert-Ludwigs-Universität Freiburg
- DOI
-
10.3389/fncom.2012.00078
- URN
-
urn:nbn:de:bsz:25-freidok-118974
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:46 MEZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Ambard, Maxime
- Rotter, Stefan
- Fakultät für Biologie
- Bernstein Center Freiburg
- Albert-Ludwigs-Universität Freiburg
- Universität
Entstanden
- 2012
Ähnliche Objekte (12)
Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons : comparison and implementation
Parallel Support Vector Machines
Support vector machines (8.6.2011)
Neuron firing in driven nonlinear integrate-and-fire models
Optimization of support vector machines
Spamerkennung mit Support Vector Machines
GPU-beschleunigte Support-Vector Machines
Multi-chimera States in the Leaky Integrate-and-Fire Model
Support vector machines in relational databases
Adaptive rates for support vector machines
Grundlagen von Support Vector Machines (SVM)
Incremental Learning with Support Vector Machines
Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons : comparison and implementation
Parallel Support Vector Machines
Support vector machines (8.6.2011)
Neuron firing in driven nonlinear integrate-and-fire models
Optimization of support vector machines
Spamerkennung mit Support Vector Machines
GPU-beschleunigte Support-Vector Machines
Multi-chimera States in the Leaky Integrate-and-Fire Model
Support vector machines in relational databases
Adaptive rates for support vector machines
Grundlagen von Support Vector Machines (SVM)
Incremental Learning with Support Vector Machines
Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons : comparison and implementation
Parallel Support Vector Machines
Support vector machines (8.6.2011)
Neuron firing in driven nonlinear integrate-and-fire models
Optimization of support vector machines
Spamerkennung mit Support Vector Machines
GPU-beschleunigte Support-Vector Machines
Multi-chimera States in the Leaky Integrate-and-Fire Model
Support vector machines in relational databases
Adaptive rates for support vector machines
Grundlagen von Support Vector Machines (SVM)