# Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks

arXiv: https://arxiv.org/abs/1611.01557 · HTML: https://mlsanity.com/paper/1611.01557/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2016.

## Built on by (most cited first, 2 of 2)

- [Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles](https://mlsanity.com/paper/2302.11618/) (2023)
- [Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition](https://mlsanity.com/paper/2503.13492/) (2025)

## Abstract (arXiv)

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations.