Dear Colleagues,

Brain-inspired intelligence uses the principles of brain science to build algorithms and computational intelligent systems with human-like intelligence. Scientists are focused on the studies of brain-inspired computer chips for highly sophisticated neural models involving neuronal units and synaptic connections. These can be efficiently applied in not only real-time computational intelligence for large-scale neural circuits but also perceived intelligence with deep learning networks that are approaching state-of-the-art classification accuracy on several vision, speech, and tactility tasks. Engineering the brain by mimicking the structure and function of a neural network on a silicon integrated circuit is the goal of embedded computational intelligence. ARM and FPGA processors are the main flexible components in the embedded system design and achieve their reliability through hardware and software technology, which can provide a high-speed, real-time, and low-power embedded platform for brain-inspired computation that boosts the applications of neural computation in both the industrial and medical fields.

This Special Issue focuses on recent advances in modeling and embedded applications in brain-inspired computation, which might be used for different purposes such as simulation of neural circuits, neural control engineering, synaptic learning rules, perceived tasks in edge computing, etc. Reviews of state-of-the-art brain-inspired artificial intelligence in embedded systems are also welcome.

The topics of interest for this Special Issue include the following: 

1. Biophysical-based neuromorphic modeling from single neuron to large-scale neural network.
2. Hardware acceleration strategies of neural circuit simulation on embedded systems.
3. Closed-loop neuromodulation for neurological diseases such as epilepsy, Parkinson’s disease, depression, etc.
4. Hardware in-loop simulations for invasive or noninvasive brain stimulation technologies such as DBS, TMS, tDCS, tACS, etc.
5. Embedded implementation of deep neural networks, including CNN, SNN, RNN, etc.
6. Lightweight deep learning network for embedded platforms.
7. Perceived intelligence (vision, speech, tactile, etc.) applications in edge computing.
8. Communication architectures involved in multi-core embedded systems.
9. Synaptic learning rules in deep neural networks.

It should be pointed out that the topics are not limited to the above list and we will consider submission in any area of the application of brain-inspired artificial intelligence.

Prof. Dr. Xile Wei
Dr. Chia-Chu Chiang
Dr. Yazan M Dweiri
Guest Editors

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• neural circuit modeling
• neuromorphic simulation
• embedded application
• deep neural network
• synaptic learning
• neural control engineering
• closed-loop neuromodulation
• hardware in-loop simulation
• computer vision
• computer tactile