A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task

By Robert A. Legenstein, Steven M. Chase, Andrew B. Schwartz, Wolfgang Maass. 2010

144 citations recorded by the source corpus.

It has recently been shown in a brain–computer interface experiment that motor cortical neurons change their tuning properties selectively to compensate for errors induced by displaced decoding parameters. In particular, it was shown that the three-dimensional tuning curves of neurons whose decoding parameters were reassigned changed more than those of neurons whose decoding parameters had not been reassigned. In this article, we propose a simple learning rule that can reproduce this effect. Our learning rule uses Hebbian weight updates driven by a global reward signal and neuronal noise. In contrast to most previously proposed learning rules, this approach does not require extrinsic information to separate noise from signal. The learning rule is able to optimize the performance of a model system within biologically realistic periods of time under high noise levels. Furthermore, when the model parameters are matched to data recorded during the brain–computer interface learning experiments described above, the model produces learning effects strikingly similar to those found in the experiments.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Motor primitives in space and time via targeted gain modulation in cortical networks (2018, unknown): “To control movements of different durations, motor networks must be able to slow down or speed up muscle outputs (i.e., change the duration of movements without affecting their shape).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “ration, as in Legenstein et al. (2010), but rather serves as”
Explicit and Implicit Contributions to Learning in a Sensorimotor Adaptation Task (2014, background): “Exploration of potential aiming directions may be a fundamental process for motor learning and, in fact, may also occur in nonhuman primates (Jarosiewicz et al., 2008; Legenstein et al., 2010).”
Reservoir Computing Trends (2012, background): “Recently he and his group have added reinforcement learning [27] and Bayesian inferencing [4] to the picture of microcir-”