TY - JOUR
T1 - A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex
AU - Pennington, Jacob R.
AU - David, Stephen V.
N1 - Publisher Copyright:
© 2023 Pennington, David. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/5
Y1 - 2023/5
N2 - Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently and substantially better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to data from novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a complete representational space across neurons in an auditory cortical field.
AB - Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently and substantially better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to data from novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a complete representational space across neurons in an auditory cortical field.
UR - http://www.scopus.com/inward/record.url?scp=85159762968&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159762968&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1011110
DO - 10.1371/journal.pcbi.1011110
M3 - Article
C2 - 37146065
AN - SCOPUS:85159762968
SN - 1553-734X
VL - 19
JO - PLoS computational biology
JF - PLoS computational biology
IS - 5
M1 - e1011110
ER -