# New preprint: Neural Spiking for Causal Inference

How do neurons learn their effect on downstream reward, so that they can update their synaptic weights? An important problem known as the credit assignment problem. Konrad and I propose the novel idea that neurons exploit their spiking discontinuity in order to solve this problem.

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.

**Lansdell B**, Kording K, bioRxiv 2019