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Emergence of associative learning in a neuromorphic inference network

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 10.1088/1741-2552/ac6ca7. (In press). Green open access

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Gandolfi+et+al_2022_J._Neural_Eng._10.1088_1741-2552_ac6ca7.pdf - Accepted Version

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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: As the Version of Record of this article is going to be/has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately. Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permission may be required. All third party content is fully copyright protected, and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record.
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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