Neuronal assembly dynamics in supervised and unsupervised learning scenarios

Moioli, Renan C and Husbands, Phil (2013) Neuronal assembly dynamics in supervised and unsupervised learning scenarios. Neural Computation, 25 (11). pp. 2934-2975. ISSN 0899-7667

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Abstract

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology > QP0361 Nervous system
Depositing User: Phil Husbands
Date Deposited: 19 Sep 2013 10:56
Last Modified: 07 Mar 2017 09:31
URI: http://sro.sussex.ac.uk/id/eprint/46378

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