TY - JOUR
T1 - Evolution of neural activity in circuits bridging sensory and abstract knowledge
AU - Mastrogiuseppe, Francesca
AU - Hiratani, Naoki
AU - Latham, Peter
N1 - Publisher Copyright:
© 2023, eLife Sciences Publications Ltd. All rights reserved.
PY - 2023
Y1 - 2023
N2 - The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory input to abstract classes via gradient-descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments, we analyze activity via standard measures such as selectivity, correla-tions, and tuning symmetry. We find that the model is able to recapitulate experimental observa-tions, including seemingly disparate ones. We determine how, in the model, the behaviour of these measures depends on details of the circuit and the task. These dependencies make experimentally testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.
AB - The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory input to abstract classes via gradient-descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments, we analyze activity via standard measures such as selectivity, correla-tions, and tuning symmetry. We find that the model is able to recapitulate experimental observa-tions, including seemingly disparate ones. We determine how, in the model, the behaviour of these measures depends on details of the circuit and the task. These dependencies make experimentally testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.
UR - http://www.scopus.com/inward/record.url?scp=85149569401&partnerID=8YFLogxK
U2 - 10.7554/elife.79908
DO - 10.7554/elife.79908
M3 - Article
C2 - 36881019
AN - SCOPUS:85149569401
SN - 2050-084X
VL - 12
JO - eLife
JF - eLife
M1 - e79908
ER -