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Related Experiment Videos

Digital Implementation of the Two-Compartmental Pinsky-Rinzel Pyramidal Neuron Model.

Elahe Rahimian, Soheil Zabihi, Mahmood Amiri

    IEEE Transactions on Biomedical Circuits and Systems
    |October 14, 2017
    PubMed
    Summary

    Researchers simplified the Pinsky-Rinzel pyramidal neuron model using piecewise linear approximation. A new digital circuit for this model enables neuromorphic applications and replicates bursting and spiking activities on field-programmable gate arrays (FPGAs).

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    Area of Science:

    • Neuroscience
    • Computer Engineering
    • Neuromorphic Engineering

    Background:

    • Brain-like computing systems merge electronics and neuroscience.
    • Optimized digital hardware implementation of neurons is crucial for neuromorphic applications.
    • Pyramidal neuron bursting activity is key to synaptic plasticity.

    Purpose of the Study:

    • To propose a modified Pinsky-Rinzel pyramidal neuron model using piecewise linear approximation.
    • To design a digital circuit for the simplified model suitable for low-cost hardware like FPGAs.
    • To demonstrate the circuit's ability to replicate essential neuronal firing characteristics.

    Main Methods:

    • Modified the Pinsky-Rinzel model by replacing complex nonlinear equations with piecewise linear approximation.

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  • Designed a digital circuit for the simplified model.
  • Simulated both original and proposed models in MATLAB and the digital circuit in Vivado.
  • Physically implemented the circuit on an FPGA.

    • Main Results:

    • The simplified model and its digital circuit showed good agreement with original model simulations.
    • The FPGA implementation successfully replicated essential firing responses, including bursting and spiking.
    • The new circuit advances existing designs in replicating complex neuronal behaviors.

    Conclusions:

    • The piecewise linear approximation offers an efficient method for implementing pyramidal neuron models in digital hardware.
    • The developed FPGA circuit provides a viable platform for neuromorphic engineering applications.
    • This work contributes to the development of neuroinspired chips capable of mimicking biological neural functions.