Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges

J Tang, F Yuan, X Shen, Z Wang, M Rao… - Advanced …, 2019 - Wiley Online Library
As the research on artificial intelligence booms, there is broad interest in brain‐inspired
computing using novel neuromorphic devices. The potential of various emerging materials …

[HTML][HTML] Deep learning with spiking neurons: opportunities and challenges

M Pfeiffer, T Pfeil - Frontiers in neuroscience, 2018 - frontiersin.org
Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

The impact of studying brain plasticity

P Mateos-Aparicio… - Frontiers in cellular …, 2019 - frontiersin.org
Neural plasticity, also known as neuroplasticity or brain plasticity, can be defined as the
ability of the nervous system to change its activity in response to intrinsic or extrinsic stimuli …

Random synaptic feedback weights support error backpropagation for deep learning

TP Lillicrap, D Cownden, DB Tweed… - Nature …, 2016 - nature.com
The brain processes information through multiple layers of neurons. This deep architecture
is representationally powerful, but complicates learning because it is difficult to identify the …

Superspike: Supervised learning in multilayer spiking neural networks

F Zenke, S Ganguli - Neural computation, 2018 - direct.mit.edu
A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration

G Tononi, C Cirelli - Neuron, 2014 - cell.com
Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain
need to disconnect from the environment for hours every day? The synaptic homeostasis …

[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 …

Norepinephrine ignites local hotspots of neuronal excitation: How arousal amplifies selectivity in perception and memory

M Mather, D Clewett, M Sakaki… - Behavioral and Brain …, 2016 - cambridge.org
Emotional arousal enhances perception and memory of high-priority information but impairs
processing of other information. Here, we propose that, under arousal, local glutamate levels …

Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules

W Gerstner, M Lehmann, V Liakoni… - Frontiers in neural …, 2018 - frontiersin.org
Most elementary behaviors such as moving the arm to grasp an object or walking into the
next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal …

Synergistic gating of electro‐iono‐photoactive 2D chalcogenide neuristors: coexistence of hebbian and homeostatic synaptic metaplasticity

RA John, F Liu, NA Chien, MR Kulkarni… - Advanced …, 2018 - Wiley Online Library
Emulation of brain‐like signal processing with thin‐film devices can lay the foundation for
building artificially intelligent learning circuitry in future. Encompassing higher functionalities …