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Mixed-Mode Response of Nigral Dopaminergic Neurons: An in Silico Study on SpiNNaker

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14258))

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Abstract

We present a work-in-progress on the mixed-mode (burst, non-burst) spiking response of the Substantia-Nigra-pars-compacta (SNc) using a conductance-based Izhikevich’s spiking neuron (IZK) model on SpiNNaker. The SNc is a primary source of Dopamine (DA) that is essential for reward-based learning and prediction in the brain and forms a part of the Basal Ganglia (BG). The bursting phases of the mixed-mode facilitate reward-related DA release whereas the non-burst phases maintain the base-levels of DA in the extracellular space. Previously, we have implemented a BG model where the modulatory effects of DA on the network synapses were simulated using static conductances. Recently, we have implemented the time-varying effects of reward-based DA release in a balanced-random-network. However, both these works did not include the SNc population. Here, we present an SNc population simulated on SpiNNaker and parameterised to display mixed-mode response; our goal is to integrate it into the existing BG model. We observe that the IZK model parameter d is crucial for model response transition between the burst and non-burst modes. Furthermore, inhibition play a pivotal role in transition from burst to mixed-mode response as reported in physiological studies. In addition, we have identified the constant current inputs in the model that facilitate mixed-mode response. With appropriate parameterisation of the efferents from the existing BG model to the SNc population, the burst to non-burst ratio in the mixed-mode response conforms to physiological observations. Continuing research is looking into using the SNc population to model reward-based learning and decision-making by the brain.

This research was supported by the Science and Engineering Research Board of India (SERB) Grant no. CRG/2019/003534. Access to SpiNNaker server was via the Human Brain Project; support for all SpiNNaker-based work was provided by the SpiNNaker team, the University of Manchester.

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Authors and Affiliations

  1. BITS Pilani, K K Birla Goa Campus, Goa, India

    Pavan Kumar Enuganti & Basabdatta Sen Bhattacharya

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  1. Pavan Kumar Enuganti
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  2. Basabdatta Sen Bhattacharya
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Correspondence to Basabdatta Sen Bhattacharya .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Enuganti, P.K., Sen Bhattacharya, B. (2023). Mixed-Mode Response of Nigral Dopaminergic Neurons: An in Silico Study on SpiNNaker. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14258. Springer, Cham. https://doi.org/10.1007/978-3-031-44192-9_29

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  • DOI: https://doi.org/10.1007/978-3-031-44192-9_29

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  • Print ISBN: 978-3-031-44191-2

  • Online ISBN: 978-3-031-44192-9

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