Davey, Neil

Kadir, Shabnam

Katakura, Ohki

Maex, Reinoud

Steuber, Volker

English

University of Hertfordshire Research Archive

16/10/2019 Bernstein Conference 2019https://abstracts.g-node.org/abstracts/c6e6253d-9037-4b94-8ce0-d6c9d29cb83f 1/42660d6c9-4f37-4c90-94b7-72c0701539e3c6e6253d-9037-4b94-8ce0-d6c9d29cb83ffalsefalseUsing cerebellar Purkinje cell models atdi erent levels of abstraction as a read-outmechanism for pattern recognition in abiologically detailed granular layer model.Ohki KatakuraReinoud MaexNeil DaveyShabnam KadirVolker Steuber1. Centre for Computer Science and Informatics Research, University of Hertfordshire, CollegeLane, Hat eld, Hertfordshire, AL10 9AB, United KingdomThe cerebellum is thought to be essential for our adaptive control. Its anatomy, physiology, circuitryand functionality have been well-studied [1--3], and several biologically detailed models of cerebellarneurons [4--9] and microcircuits [10,11] have been proposed. However, most existing networkmodels that include the input layer (granular layer) and the output cell (Purkinje cell, PC) are basedon simpli ed neuronal models [12--15]. Here, hybrid network models of cerebellar cortex wereconstructed based on a realistic granular layer model [11] and one of two kinds of abridged PCmodels: an inner product of synaptic weights and parallel  bre (pf) inputs [16,17], and an integrate-and- re unit [13,14]. The granular layer model consisted of detailed granule and Golgi cell (GC andGoC) models, mossy  bres (mfs) and glomeruli (glom) (panel A). The PC received synapses from pfs(GC axons) originating within a 4,000 m transversal  250 m sagittal  100 m vertical volume ofgranular layer comprising about 192,000 GCs. The models were implemented and simulated withNEURON (version 7.4) and Python (version 3.7.0) and studied in the context of a pattern recognitiontasks. During this task, mfs were spontaneously active at 5 Hz and, in addition, a mf patch of 100 mradius was activated at 200 Hz for 50 ms (panels B,C). At the o set of the mf patch stimulus, climbing bre (cf) input induced long-term depression (LTD) of synapses from active pfs to the PC. Aftertraining, the two types of PC models showed distinct responses to learnt and novel patterns ofstimulated mfs, both in their inner product and in their number of spikes during the stimulated 50ms (red and green traces in panels D,E). The increase in inner product and  ring rate in response toburst activity of mfs were absent in response to input to the learnt patch, but were still present inresponse to novel input, which implies that the transformation of stimuli in the granular layer doesnot prevent learning with pfs-PC LTD. As a next step, more realistic PC models [4,5,9] will be used asread-out neurons, and the e ect of sparseness and clustering on pattern recognition will be studied.11111μ × μ × μμ16/10/2019 Bernstein Conference 2019https://abstracts.g-node.org/abstracts/c6e6253d-9037-4b94-8ce0-d6c9d29cb83f 2/4AcknowledgementsThe  rst author appreciates the help of Dr. Tadashi Yamazaki at University of Electro-Communications, Tokyo and fruitful discussions with Dr. Sergio Solinas at the Institute ofNeuroinformatics, Zurich. This work made use of the University of Hertfordshire's high-performancecomputing facility.References1. Eccles, J.C., Ito, M. & Szentágothai, J. (1967) The cerebellum as a neuronal machine. New York:Springer-Verlag. (https://doi.org/10.1007%2F978-3-662-13147-3), 10.1007/978-3-662-13147-3(http://dx.doi.org/10.1007/978-3-662-13147-3)2. Palay, S.L. & Chan-Palay, V. (1974) Cerebellar cortex: Cytology and organizaion. New York:Springer-Verlag. 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(https://doi.org/10.1038%2Fsrep46550), 10.1038/srep46550(http://dx.doi.org/10.1038/srep46550)Copyright: © (2019) Katakura O, Maex R, Davey N, Kadir S, Steuber VCitation: Katakura O, Maex R, Davey N, Kadir S, Steuber V (2019) Using cerebellar Purkinje cellmodels at di erent levels of abstraction as a read-out mechanism for pattern recognition in abiologically detailed granular layer model.. Bernstein Conference 2019. doi:10.12751/nncn.bc2019.0043 (http://doi.org/10.12751/nncn.bc2019.0043)16/10/2019 Bernstein Conference 2019https://abstracts.g-node.org/abstracts/c6e6253d-9037-4b94-8ce0-d6c9d29cb83f 4/4

Using cerebellar Purkinje cell models at different levels of abstraction as a read-out mechanism for pattern recognition in a biologically detailed granular layer model

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Using cerebellar Purkinje cell models at different levels of abstraction as a read-out mechanism for pattern recognition in a biologically detailed granular layer model

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