PT  - JOURNAL ARTICLE
AU  - David Beniaguev
AU  - Idan Segev
AU  - Michael London
TI  - Single Cortical Neurons as Deep Artificial Neural Networks
AID  - 10.1101/613141
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 613141
4099  - http://biorxiv.org/content/early/2020/03/19/613141.short
4100  - http://biorxiv.org/content/early/2020/03/19/613141.full
AB  - We introduce a novel approach to study neurons as sophisticated I/O information processing units by utilizing recent advances in the field of machine learning. We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. A Temporally Convolutional DNN (TCN) with seven layers was required to accurately, and very efficiently, capture the I/O of this neuron at the millisecond resolution. This complexity primarily arises from local NMDA-based nonlinear dendritic conductances. The weight matrices of the DNN provide new insights into the I/O function of cortical pyramidal neurons, and the approach presented can provide a systematic characterization of the functional complexity of different neuron types. Our results demonstrate that cortical neurons can be conceptualized as multi-layered “deep” processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed.