Single Cortical Neurons as Deep Artificial Neural Networks

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 "depth" of a biological neurons, suggesting that cortical pyramidal neurons and the networks they form are computationally much more powerful than previously assumed.

David Beniaguev, Idan Segev, Michael London
Year of publication
bioRxiv preprint first posted online Apr. 18, 2019;