# Synaptic Time-Dependent Plasticity Leads to Efficient Coding of Predictions

## TL;DR

This paper demonstrates that Synaptic Time-Dependent Plasticity enhances neural coding efficiency by reducing postsynaptic spike latency and number, thereby improving signal-to-noise ratio, lowering metabolic costs, and enabling predictive capabilities.

## Contribution

It extends understanding of STDP effects to long spike trains and shows how it leads to more efficient coding and prediction in neural systems.

## Key findings

- STDP reduces postsynaptic spike count and latency.
- Improves neural coding by increasing SNR and reducing energy use.
- Facilitates emergence of predictions from spike timing.

## Abstract

Latency reduction of postsynaptic spikes is a well-known effect of Synaptic Time-Dependent Plasticity. We expand this notion for long postsynaptic spike trains, showing that, for a fixed input spike train, STDP reduces the number of postsynaptic spikes and concentrates the remaining ones. Then we study the consequences of this phenomena in terms of coding, finding that this mechanism improves the neural code by increasing the signal-to-noise ratio and lowering the metabolic costs of frequent stimuli. Finally, we illustrate that the reduction of postsynaptic latencies can lead to the emergence of predictions.