Memory Recall and Spike Frequency Adaptation

TL;DR

This paper demonstrates that spike frequency adaptation (SFA) enables controlled, selective memory retrieval and attractor switching in neural networks, providing a biologically plausible mechanism for memory recall dynamics.

Contribution

It introduces SFA into auto-associative networks, showing how it enables state-dependent control and dynamic switching of memory patterns, unlike traditional models.

Findings

SFA stabilizes specific attractors in neural networks.

SFA allows for temporal switching between memory states.

Models replicate plausible memory retrieval behaviors.

Abstract

The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using auto-associative networks such as the Hopfield model. This kind of model reliably converges to stored patterns which contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard auto-associative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.