At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters

By Christian A. Rössert, Paul Dean, John Porrill. 2015

62 citations recorded by the source corpus.

Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations. Details of this process are however unclear. While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets, the more general application for temporally varying signals has yet to be demonstrated. Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants. The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior. We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells. Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells, variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway. Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “The presence of such mechanisms could contribute to separating neural trajectories by exploiting the rich dynamics of edge-of-chaos networks (Rossert et al., 2015), in analogy with reservoir computing (Yamazaki and Tanaka, 2007; Buonomano and Maass, 2009; Sussillo and Abbott, 2009).”
Computational Principles of Supervised Learning in the Cerebellum (2018, unknown): “…dampening the gain of the system based on the current response (Gao et al. 2016, Lisberger & Sejnowski 1992), and (d ) implementing an architecture for reservoir computing capable of generating dynamically rich input signals in the granule cell layer of the cerebellar cortex (Rossert et al. 2015).”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “…achieve their extraordinary performance, and (ii) from the empirical side, recent work has revealed that a mechanism akin to reservoir computing enables neuronal networks of the cerebellum to perform highly complex tasks in an efficient way by operating at criticality (Rössert et al., 2015).”