Deep learning in spiking neural networks

A Tavanaei, M Ghodrati, SR Kheradpisheh… - Neural networks, 2019 - Elsevier
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

[HTML][HTML] Breathing rhythm and pattern and their influence on emotion

S Ashhad, K Kam, CA Del Negro… - Annual review of …, 2022 - annualreviews.org
Breathing is a vital rhythmic motor behavior with a surprisingly broad influence on the brain
and body. The apparent simplicity of breathing belies a complex neural control system, the …

[HTML][HTML] Integrated information theory (IIT) 4.0: formulating the properties of phenomenal existence in physical terms

L Albantakis, L Barbosa, G Findlay… - PLoS computational …, 2023 - journals.plos.org
This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the
properties of experience in physical (operational) terms. It identifies the essential properties …

Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization

N Rathi, K Roy - IEEE Transactions on Neural Networks and …, 2021 - ieeexplore.ieee.org
Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …

Spiking-yolo: spiking neural network for energy-efficient object detection

S Kim, S Park, B Na, S Yoon - Proceedings of the AAAI conference on …, 2020 - ojs.aaai.org
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable
performance in a variety of applications. As we try to solve more advanced problems …

Optimizing deeper spiking neural networks for dynamic vision sensing

Y Kim, P Panda - Neural Networks, 2021 - Elsevier
Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

The role of variability in motor learning

AK Dhawale, MA Smith… - Annual review of …, 2017 - annualreviews.org
Trial-to-trial variability in the execution of movements and motor skills is ubiquitous and
widely considered to be the unwanted consequence of a noisy nervous system. However …

[图书][B] Neuronal dynamics: From single neurons to networks and models of cognition

W Gerstner, WM Kistler, R Naud, L Paninski - 2014 - books.google.com
What happens in our brain when we make a decision? What triggers a neuron to send out a
signal? What is the neural code? This textbook for advanced undergraduate and beginning …

Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks

N Rathi, K Roy - arXiv preprint arXiv:2008.03658, 2020 - arxiv.org
Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …

Robust neuronal dynamics in premotor cortex during motor planning

N Li, K Daie, K Svoboda, S Druckmann - Nature, 2016 - nature.com
Neural activity maintains representations that bridge past and future events, often over many
seconds. Network models can produce persistent and ramping activity, but the positive …