Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System

Sorrell, Ethan; Rule, Michael E; O’Leary, Timothy

doi:10.1146/annurev-control-061720-012348

Cited by 15 publications

(14 citation statements)

References 167 publications

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“…Notably, brain–machine interface decoders routinely confront this and apply online recalibration and transfer learning to track drift in (e.g., see refs. 45 and 46 for review). We argue that neural circuits may do something similar to maintain “calibration” between relatively stable circuits and highly plastic circuits.…”

Section: Introductionmentioning

confidence: 99%

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

Rule

O’Leary

2022

Proc. Natl. Acad. Sci. U.S.A.

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As an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such “representational drift” raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations.

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Section: Introductionmentioning

confidence: 99%

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

Rule

O’Leary

2022

Proc. Natl. Acad. Sci. U.S.A.

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“…Ensuring a large κ 1 is therefore important for the parameter estimates to closely track the true time-varying parameters. Given the expression for κ 1 in (50), this shows the importance of ensuring a persistent excitation through a large δ and a small T . It also shows that one should avoid too large of a forgetting rate α. ii) A large γ decreases the radius of B ρ (t).…”

Section: Time-varying Parametersmentioning

confidence: 96%

“…Any system designed to interface with the brain should take into account the adaptive nature of biological neural networks [50]. However, there are currently no established adaptive estimation or control paradigms for exploiting high-dimensional single-neuron intracellular recordings.…”

Section: Introductionmentioning

confidence: 99%

Online estimation of biophysical neural networks

Burghi¹,

Sepulchre²

2021

Preprint

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This paper presents an adaptive observer for online state and parameter estimation of a broad class of biophysical models of neuronal networks. The design closely resembles classical solutions of adaptive control, and the convergence proof is based on contraction analysis. Our results include robustness guarantees with respect to unknown parameter dynamics. We discuss the potential of the approach in neurophysiological applications.

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“…Engineers have applied online recalibration and transfer learning and to track drift in brain-machine interface decoders (e.g. 41; 42 for review). Could neural circuits in the brain do something similar?…”

Section: Introductionmentioning

confidence: 99%

“…Notably, brain-machine interface decoders routinely confront this, and apply online recalibration and transfer learning to track drift in (e.g. 45; 46 for review). We argue that neural circuits may do something similar to maintain ‘calibration’ between relatively stable circuits and highly plastic circuits.…”

Section: Introductionmentioning

confidence: 99%

Self-healing codes: how stable neural populations can track continually reconfiguring neural representations

Rule

O’Leary

2021

Preprint

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Neural representations change, even in the absence of overt learning. To preserve stable behavior and memories, the brain must track these changes. Here, we explore homeostatic mechanisms that could allow neural populations to track drift in continuous representations without external error feedback. We build on existing models of Hebbian homeostasis, which have been shown to stabilize representations against synaptic turnover and allow discrete neuronal assemblies to track representational drift. We show that a downstream readout can use its own activity to detect and correct drift, and that such a self-healing code could be implemented by plausible synaptic rules. Population response normalization and recurrent dynamics could stabilize codes further. Our model reproduces aspects of drift observed in experiments, and posits neurally plausible mechanisms for long-term stable readouts from drifting population codes.

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Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System

Cited by 15 publications

References 167 publications

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

Online estimation of biophysical neural networks

Self-healing codes: how stable neural populations can track continually reconfiguring neural representations

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