A review of learning in biologically plausible spiking neural networks
Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
Synaptic electronics: materials, devices and applications
In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological
synaptic plasticity and learning are described. The material properties and electrical …
synaptic plasticity and learning are described. The material properties and electrical …
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well
established that it depends on pre-and postsynaptic activity. However, models that rely …
established that it depends on pre-and postsynaptic activity. However, models that rely …
[图书][B] Neuronal dynamics: From single neurons to networks and models of cognition
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 …
signal? What is the neural code? This textbook for advanced undergraduate and beginning …
Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity
Memristors have been extensively studied for data storage and low-power computation
applications. In this study, we show that memristors offer more than simple resistance …
applications. In this study, we show that memristors offer more than simple resistance …
Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
Brain-inspired computing is an emerging field, which aims to extend the capabilities of
information technology beyond digital logic. A compact nanoscale device, emulating …
information technology beyond digital logic. A compact nanoscale device, emulating …
Plasticity of cortical excitatory-inhibitory balance
RC Froemke - Annual review of neuroscience, 2015 - annualreviews.org
Synapses are highly plastic and are modified by changes in patterns of neural activity or
sensory experience. Plasticity of cortical excitatory synapses is thought to be important for …
sensory experience. Plasticity of cortical excitatory synapses is thought to be important for …
Synaptic suppression triplet‐STDP learning rule realized in second‐order memristors
The synaptic weight modification depends not only on interval of the pre‐/postspike pairs
according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing …
according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing …
Spike timing–dependent plasticity: a Hebbian learning rule
N Caporale, Y Dan - Annu. Rev. Neurosci., 2008 - annualreviews.org
Spike timing–dependent plasticity (STDP) as a Hebbian synaptic learning rule has been
demonstrated in various neural circuits over a wide spectrum of species, from insects to …
demonstrated in various neural circuits over a wide spectrum of species, from insects to …
A history of spike-timing-dependent plasticity
How learning and memory is achieved in the brain is a central question in neuroscience.
Key to today's research into information storage in the brain is the concept of synaptic …
Key to today's research into information storage in the brain is the concept of synaptic …