Cell-type-specific neuromodulation guides synaptic credit assignment in a spiking neural network.
Proc Natl Acad Sci U S A
; 118(51)2021 12 21.
Article
em En
| MEDLINE
| ID: mdl-34916291
ABSTRACT
Brains learn tasks via experience-driven differential adjustment of their myriad individual synaptic connections, but the mechanisms that target appropriate adjustment to particular connections remain deeply enigmatic. While Hebbian synaptic plasticity, synaptic eligibility traces, and top-down feedback signals surely contribute to solving this synaptic credit-assignment problem, alone, they appear to be insufficient. Inspired by new genetic perspectives on neuronal signaling architectures, here, we present a normative theory for synaptic learning, where we predict that neurons communicate their contribution to the learning outcome to nearby neurons via cell-type-specific local neuromodulation. Computational tests suggest that neuron-type diversity and neuron-type-specific local neuromodulation may be critical pieces of the biological credit-assignment puzzle. They also suggest algorithms for improved artificial neural network learning efficiency.
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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Sinapses
/
Rede Nervosa
/
Neurônios
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article