Neuromorphic algorithms for brain implants: a review

Abstract

Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neuromorphic hardware, could match or surpass traditional methods in efficiency. Our aim is to inspire the creation and deployment of models that not only enhance computational performance for implants but also serve broader fields like medical diagnostics and robotics inspiring next generations of neural implants.

Keywords: biohybrid interfaces; brain implants; brain-computer interfaces (BCIs); data compression; mixed-signal design; neurocomputational models; neuromorphic computing; spiking neural networks (SNNs).

Figure 1

Illustrates the signal processing workflow for brain implants across the three tiers: external (on-workstation), on-node, and fully on-implant processing. Each tier highlights differences in hardware, bandwidth, tasks, and trade-offs between latency, power, and computational efficiency.

Figure 2

An example of a neuromorphic pipeline for brain implants, starting with neural recording and progressing through event-driven processing, spiking neural network computation, and adaptive neuromodulation for closed-loop control.