Chaotic dynamics with neuromorphic networks of spiking neurons

Context and motivation
The Institute of Neuroinformatics (INI) is a joint institute of the University of Zürich and the Swiss Federal Institute of Zürich (ETH Zürich). INI carries out experimental, theoretical, and applied research with the aim of discovering key principles by which the brain is built and works, and using the knowledge in practical applications where possible.
The Neuromorphic Cognitive Systems group at INI has been developing neuromorphic processing platforms that enable event-driven computing using algorithms and primitives inspired by biology. In this project, we aim to explore the dynamics of hardware implementations of spiking neural networks, built using analog circuits to faithfully emulate the behaviour of real synapses and neurons in cortical circuits. The project is broadly about leveraging the nonlinear nature of the neuromorphic circuits to obtain chaotic dynamics. This represents a new usage paradigm for these types of neuromorphic processors, and is particularly interesting because chaotic dynamics are observed pervasively in nature, from pendula and weather to isolated axons through entire brains. The synchronization of chaotic systems allows generating rich patterns from the interactions of relatively simple network configurations (e.g., communities, modules, and remote interactions can arise spontaneously among neurons interlinked as a ring).
Goals
The project is conceived around two related but complementary strands, which could be pursued in parallel or sequentially. On the one hand, the possibility of designing a network that can spread the information from a small number of high-rate synaptic inputs over a large number of neurons, with each neuron firing at much lower rates (i.e., to convert the temporal information present in high frequency signals to a spatio-temporal one distributed among multiple low-frequency nodes). On the other hand, the possibility of replicating on the neuromorphic platform results obtained in rings of single-transistor oscillators. Namely, diverse designs of a "node" will be explored, including one or multiple neurons, having excitatory and inhibitory connections within each node and to the adjacent ones. The aim is to find a valid configuration alongside "critical" values of the control parameters, such that the symmetry of the ring is spontaneously broken, and communities of nodes firing in sync emerge spontaneously emerge. It is expected that, based on the settings, the size and distributions of these communities should be easily controllable, mimicking some observations of multiscale modularity in the above figure.

Requirements

Python programming, Basic understanding of spiking neural networks.

Contact

Elisa Donati (elisa (at) ini.uzh.ch); Prof. Giacomo Indiveri (giacomo (at) ini.uzh.ch),
Ludovico Minati (lminati (at) ini.uzh.ch).

© 2021 Institut für Neuroinformatik