Gandolfi, Daniela;
Puglisi, Francesco Maria;
Boiani, Giulia Maria;
Pagnoni, Giuseppe;
Friston, Karl J;
D'Angelo, Egidio Ugo;
Mapelli, Jonathan;
(2022)
Emergence of associative learning in a neuromorphic inference network.
Journal of Neural Engineering
, 19
(3)
, Article 036022. 10.1088/1741-2552/ac6ca7.
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Abstract
OBJECTIVE: In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH: We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS: Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE: These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.
| Type: | Article |
|---|---|
| Title: | Emergence of associative learning in a neuromorphic inference network |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1741-2552/ac6ca7 |
| Publisher version: | https://doi.org/10.1088/1741-2552/ac6ca7 |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Active inference, Predictive coding, brain-inspired computing, neuromorphic electronics, unsupervised learning |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10148425 |
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