@article {David613141, author = {Beniaguev David and Segev Idan and London Michael}, title = {Single Cortical Neurons as Deep Artificial Neural Networks}, elocation-id = {613141}, year = {2019}, doi = {10.1101/613141}, publisher = {Cold Spring Harbor Laboratory}, abstract = {We propose a novel approach based on modern deep artificial neural networks (DNNs) for understanding how the morpho-electrical complexity of neurons shapes their input/output (I/O) properties at the millisecond resolution in response to massive synaptic input. The I/O of integrate and fire point neuron is accurately captured by a DNN with a single unit and one hidden layer. A fully connected DNN with one hidden layer faithfully replicated the I/O relationship of a detailed model of Layer 5 cortical pyramidal cell (L5PC) receiving AMPA and GABAA synapses. However, when adding voltage-gated NMDA-conductances, a temporally-convolutional DNN with seven layers was required. Analysis of the DNN filters provides new insights into dendritic processing shaping the I/O properties of neurons. This work proposes a systematic approach for characterizing the functional {\textquotedblleft}depth{\textquotedblright} of a biological neurons, suggesting that cortical pyramidal neurons and the networks they form are computationally much more powerful than previously assumed.}, URL = {https://www.biorxiv.org/content/early/2019/04/18/613141}, eprint = {https://www.biorxiv.org/content/early/2019/04/18/613141.full.pdf}, journal = {bioRxiv} }