# Temporal Basis Function Models for Closed-Loop Neural Stimulation

arXiv: https://arxiv.org/abs/2507.15274 · HTML: https://mlsanity.com/paper/2507.15274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 65 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Brain Co-Processors: Using AI to Restore and Augment Brain Function](https://mlsanity.com/paper/2012.03378/) (2020)
- [Learning to Correspond Dynamical Systems](https://mlsanity.com/paper/1912.03015/) (2019)
- [Neural Co-Processors for Restoring Brain Function: Results from a Cortical Model of Grasping](https://mlsanity.com/paper/2210.11478/) (2022)
- [Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation](https://mlsanity.com/paper/2406.06714/) (2024)

## Built on by (most cited first, 1 of 1)

- [Real-time closed-loop protocol to assess neural variability in temporal coding](https://mlsanity.com/paper/2608.24895/) (2026)

## Abstract (arXiv)

Closed-loop neural stimulation provides novel therapies for neurological diseases such as Parkinson's disease (PD), but it is not yet clear whether artificial intelligence (AI) techniques can tailor closed-loop stimulation to individual patients or identify new therapies. Progress requires us to address a number of translational issues, including sample efficiency, training time, and minimizing loop latency such that stimulation may be shaped in response to changing brain activity. We propose temporal basis function models (TBFMs) to address these difficulties, and explore this approach in the context of excitatory optogenetic stimulation. We demonstrate the ability of TBF models to provide a single-trial, spatiotemporal forward prediction of the effect of optogenetic stimulation on local field potentials (LFPs) measured in two non-human primates. We further use simulations to demonstrate the use of TBF models for closed-loop stimulation, driving neural activity towards target patterns. The simplicity of TBF models allow them to be sample efficient, rapid to train (2-4min), and low latency (0.2ms) on desktop CPUs. We demonstrate the model on 40 sessions of previously published excitatory optogenetic stimulation data. For each session, the model required 15-20min of data collection to successfully model the remainder of the session. It achieved a prediction accuracy comparable to a baseline nonlinear dynamical systems model that requires hours to train, and superior accuracy to a linear state-space model. In our simulations, it also successfully allowed a closed-loop stimulator to control a neural circuit. Our approach begins to bridge the translational gap between complex AI-based approaches to modeling dynamical systems and the vision of using such forward prediction models to develop novel, clinically useful closed-loop stimulation protocols.