Related Experiment Video
Updated: May 15, 2026
10:32
Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
Published on: April 15, 2015
An FPGA-Based Silicon Neuronal Network with Selectable Excitability Silicon Neurons
Jing Li1, Yuichi Katori, Takashi Kohno
1Graduate School of Engineering, The University of Tokyo Tokyo, Japan.
Frontiers in Neuroscience
|December 28, 2012
Summary
This study introduces a digital silicon neuronal network capable of associative memory. The efficient hardware design enables complex simulations, demonstrating pattern retrieval and neuron synchronization.
Area of Science:
- Neuroscience
- Computer Engineering
- Artificial Intelligence
Background:
- Biological nervous systems exhibit complex intelligent behaviors like associative memory.
- Simulating these systems in hardware offers efficient and scalable intelligent task execution.
Purpose of the Study:
- To present a digital silicon neuronal network capable of simulating biological neural systems.
- To implement intelligent tasks, specifically associative memory, using a novel hardware design.
Main Methods:
- Developed a digital spiking silicon neuron (DSSN) and a silicon synapse for tunable excitability and computational efficiency.
- Employed a mixed pipeline and parallel structure with shift operations for large-scale network implementation on an FPGA.
- Integrated memory and USB control blocks for host PC communication.
Main Results:
- Successfully built and tested a 256-neuron fully connected network on a Xilinx Spartan-6 FPGA.
- Demonstrated the network's capability for associative memory, retrieving stored patterns based on input similarity.
- Observed neuron synchronization correlating with successful pattern retrieval.
Conclusions:
- The digital silicon neuronal network effectively simulates neural functions and performs associative memory.
- The hardware-efficient design allows for complex and scalable neural network implementations.
- Neuron synchronization serves as an indicator of successful pattern retrieval in the network.
Related Concept Videos
01:25Neural Circuits
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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
01:19The Role of Ion Channels in Neuronal Computation
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.