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
in 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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Synapses
/
Nerve Net
/
Neurons
Type of study:
Prognostic_studies
Language:
En
Journal:
Proc Natl Acad Sci U S A
Year:
2021
Document type:
Article