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Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Updated: Jan 1, 2026

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Real-time cortical simulation on neuromorphic hardware.

Oliver Rhodes¹, Luca Peres¹, Andrew G D Rowley¹

¹Department of Computer Science, University of Manchester, Manchester, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|December 24, 2019

Summary

This summary is machine-generated.

This study demonstrates real-time simulation of large spiking neural networks using SpiNNaker hardware, achieving faster speeds and lower energy consumption than traditional supercomputers and GPUs for neuroscience research.

Keywords:

SpiNNaker cortical microcircuit low-power neuromorphic parallel programming real time

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Area of Science:

Computational Neuroscience
Neuromorphic Engineering
Artificial Intelligence

Background:

Spiking neural networks (SNNs) are crucial for understanding brain function.
Simulating large-scale SNNs in real-time presents significant computational challenges.
Existing high-performance computing (HPC) and GPU approaches face limitations in speed and energy efficiency.

Purpose of the Study:

To present a real-time simulation of a large-scale, biologically representative SNN.
To evaluate the performance of SpiNNaker neuromorphic hardware for SNN simulations.
To demonstrate the scalability and energy efficiency of neuromorphic computing for neuroscience.

Main Methods:

Utilized a heterogeneous parallelization scheme on SpiNNaker neuromorphic hardware.
Employed a published cortical microcircuit model (77k neurons, 0.3 billion synapses) as a benchmark.
Compared simulation results against established HPC simulator baselines for correctness.

Main Results:

Achieved the first hard real-time simulation of the cortical microcircuit model (10s biological time in 10s wall-clock time).
Outperformed HPC simulators (3x slowdown) and GPUs (2x slowdown) in simulation speed.
Demonstrated 10x energy reduction compared to HPC systems and comparable energy use to GPUs.
Confirmed simulation correctness through statistical measures against HPC baselines.
Showcased system robustness with multiple 12-hour simulations.

Conclusions:

SpiNNaker hardware enables efficient real-time simulation of large-scale SNNs.
Neuromorphic hardware overcomes communication barriers of traditional computing for SNNs.
This approach offers a powerful neuroscience research tool for extended SNN studies.
Real-time processing is achievable and scalable with increasing SNN size on neuromorphic platforms.