Neural Comput 2013 Dec 18;25(12):3113-30. Epub 2013 Sep 18.
Google Switzerland GmbH, 8002 Zurich, Switzerland
Front Neurosci 2022 11;16:775457. Epub 2022 Apr 11.
Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
We present a processing-in-memory (PIM)-based hardware platform, referred to as MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking neural network. MONETAuses 8T static random-access memory (SRAM)-based PIM cores for vector matrix multiplication (VMM) augmented with spike-time-dependent-plasticity (STDP) based weight update. The spiking neural network (SNN)-focused data flow is presented to minimize data movement in MONETAwhile ensuring learning accuracy. Read More
Front Neurosci 2022 31;16:838832. Epub 2022 Mar 31.
FutureX LAB, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
Spiking neural network (SNN) is considered to be the brain-like model that best conforms to the biological mechanism of the brain. Due to the non-differentiability of the spike, the training method of SNNs is still incomplete. This paper proposes a supervised learning method for SNNs based on associative learning: ALSA. Read More
Front Neurosci 2021 4;15:756876. Epub 2021 Nov 4.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information processing capability and high biological plausibility. Although SNNs are currently more efficient than artificial neural networks (ANNs), they are not as accurate as ANNs. Error backpropagation is the most common method for directly training neural networks, promoting the prosperity of ANNs in various deep learning fields. Read More
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2021 Oct;38(5):986-994
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China.
Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Read More
Elife 2021 10 28;10. Epub 2021 Oct 28.
Department of Physiology, University of Bern, Bern, Switzerland.
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic coupling strength between neurons are essential for this capability, setting us apart from simpler, hard-wired organisms. How these changes can be mathematically described at the phenomenological level, as so-called 'plasticity rules', is essential both for understanding biological information processing and for developing cognitively performant artificial systems. Read More