PT  - JOURNAL ARTICLE
AU  - Benjamin James Lansdell
AU  - Konrad Paul Kording
TI  - Neural spiking for causal inference
AID  - 10.1101/253351
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 253351
4099  - http://biorxiv.org/content/early/2019/10/15/253351.short
4100  - http://biorxiv.org/content/early/2019/10/15/253351.full
AB  - When a neuron is driven beyond its threshold it spikes, and the fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. By introducing a local discontinuity with respect to their input drive, we show how spiking enables neurons to solve causal estimation and learning problems.