Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Neuronal spikes are referred to as the electric activity of neurons (in terms of voltage) in response to various biological events such as the sodium and calcium ionic current channels in the brain. Currently, both biological models as well as mathem...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Connectivity is key to understanding neural circuit computations. However, estimating in vivo connectivity using recording of activity alone is challenging. Issues include common input and bias errors in inference, and limited temporal resolution due...
Chaos (Woodbury, N.Y.)
Jul 1, 2022
Due to the potential difference between two neurons and that between the inner and outer membranes of an individual neuron, the neural network is always exposed to complex electromagnetic environments. In this paper, we utilize a hyperbolic-type memr...
Optics express
Jun 6, 2022
We propose an approach to generate neuron-like spikes of vertical-cavity surface-emitting laser (VCSEL) by multi-frequency switching. A stable temporal spiking sequence has been realized both by numerical simulations and experiments with a pulse widt...
Chaos (Woodbury, N.Y.)
Jun 1, 2022
Physical neural networks are promising candidates for next generation artificial intelligence hardware. In such architectures, neurons and connections are physically realized and do not leverage digital concepts with their practically infinite signal...
Optics express
May 23, 2022
Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput and energy efficiency compared to their electronic neuromorphic counterparts while maintaining their benefits in terms of event-driven computing capability. Whil...
Neural computation
May 19, 2022
Backpropagation of error (backprop) is a powerful algorithm for training machine learning architectures through end-to-end differentiation. Recently it has been shown that backprop in multilayer perceptrons (MLPs) can be approximated using predictive...
Neural computation
May 19, 2022
Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requi...
Neural computation
Apr 15, 2022
With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationa...
Neural computation
Apr 15, 2022
Recent work on spiking neural networks (SNNs) has focused on achieving deep architectures. They commonly use backpropagation (BP) to train SNNs directly, which allows SNNs to go deeper and achieve higher performance. However, the BP training procedur...