A computational framework for cortical learning.
Biol Cybern
; 90(6): 400-9, 2004 Jun.
Article
en En
| MEDLINE
| ID: mdl-15316786
Recent physiological findings have revealed that long-term adaptation of the synaptic strengths between cortical pyramidal neurons depends on the temporal order of presynaptic and postsynaptic spikes, which is called spike-timing-dependent plasticity (STDP) or temporally asymmetric Hebbian (TAH) learning. Here I prove by analytical means that a physiologically plausible variant of STDP adapts synaptic strengths such that the presynaptic spikes predict the postsynaptic spikes with minimal error. This prediction error model of STDP implies a mechanism for cortical memory: cortical tissue learns temporal spike patterns if these spike patterns are repeatedly elicited in a set of pyramidal neurons. The trained network finishes these patterns if their beginnings are presented, thereby recalling the memory. Implementations of the proposed algorithms may be useful for applications in voice recognition and computer vision.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Potenciales de Acción
/
Redes Neurales de la Computación
/
Células Piramidales
/
Aprendizaje
/
Vías Nerviosas
/
Plasticidad Neuronal
Tipo de estudio:
Prognostic_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Biol Cybern
Año:
2004
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Alemania