# The Neuron as a Direct Data-Driven Controller

arXiv: https://arxiv.org/abs/2401.01489 · HTML: https://mlsanity.com/paper/2401.01489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 96 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Formulas for Data-driven Control: Stabilization, Optimality and Robustness](https://mlsanity.com/paper/1903.06842/) (2019)
- [Data-Enabled Predictive Control: In the Shallows of the DeePC](https://mlsanity.com/paper/1811.05890/) (2018)
- [Predictive information in a sensory population](https://mlsanity.com/paper/1307.0225/) (2013)
- [Neuroscience-inspired online unsupervised learning algorithms](https://mlsanity.com/paper/1908.01867/) (2019)
- [Data-Driven Control of Complex Networks](https://mlsanity.com/paper/2003.12189/) (2020)
- [Linear tracking MPC for nonlinear systems Part II: The data-driven case](https://mlsanity.com/paper/2105.08567/) (2021)
- [Internal feedback in the cortical perception-action loop enables fast and accurate behavior](https://mlsanity.com/paper/2211.05922/) (2022)
- [Online learning of data-driven controllers for unknown switched linear systems](https://mlsanity.com/paper/2105.11523/) (2021)

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

- [What should a neuron aim for? Designing local objective functions based on information theory](https://mlsanity.com/paper/2412.02482/) (2024)
- [Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization](https://mlsanity.com/paper/2604.05042/) (2026)

## Abstract (arXiv)

In the quest to model neuronal function amidst gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends the current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, act as controllers, steering their environment towards a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. Utilizing the novel Direct Data-Driven Control (DD-DC) framework, we model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states and optimize control. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in Spike-Timing-Dependent Plasticity (STDP) with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a novel and biologically-informed fundamental unit for constructing neural networks.