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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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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Free-space optical spiking neural network.

Reyhane Ahmadi¹, Amirreza Ahmadnejad², Somayyeh Koohi¹

¹Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

|January 8, 2025

Summary

This summary is machine-generated.

This study introduces the Optical Deep Spiking Convolutional Neural Network (OSCNN), a novel brain-inspired optical processor. The OSCNN achieves high accuracy with significantly lower power consumption and faster speeds than electronic alternatives, advancing neuromorphic engineering.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Neuromorphic Engineering
Optical Computing
Artificial Intelligence

Background:

Conventional electronic AI processors face limitations in speed and thermal dissipation.
Optical Neural Networks (ONNs) offer an alternative by utilizing light's processing capabilities.
Spiking Neural Networks (SNNs) within ONNs show promise for emulating brain computation efficiently.

Purpose of the Study:

To introduce a pioneering Free-space Optical Deep Spiking Convolutional Neural Network (OSCNN).
To leverage free-space optics for enhanced power efficiency and processing speed in AI.
To demonstrate competitive accuracy in pattern detection using a brain-inspired model.

Main Methods:

Developed the OSCNN inspired by the human eye's computational model.
Employed Gabor filters for initial feature extraction and optical components like Intensity-to-Delay conversion.
Utilized free-space optics, micro-positioning systems, and advanced optical/electronic integration techniques.

Main Results:

OSCNN achieved competitive classification accuracy on benchmark datasets (MNIST, ETH80, Caltech).
Demonstrated significantly lower power consumption (1.6 W) and faster processing (2.44 ms) compared to electronic CNNs.
Outperformed conventional GPUs in power efficiency while matching processing speeds.

Conclusions:

The OSCNN represents a significant advancement in low-power, high-speed optical neuromorphic computing.
The proposed methods address key challenges in optical neural network implementation, including alignment and signal integrity.
This work paves the way for more efficient and powerful brain-inspired computational systems.