Plasticity in memristive devices for spiking neural networks
Memristive devices present a new device technology allowing for the realization of compact
non-volatile memories. Some of them are already in the process of industrialization …
non-volatile memories. Some of them are already in the process of industrialization …
Plasticity and adaptation in neuromorphic biohybrid systems
Neuromorphic systems take inspiration from the principles of biological information
processing to form hardware platforms that enable the large-scale implementation of neural …
processing to form hardware platforms that enable the large-scale implementation of neural …
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-based synaptic plasticity in silicon: design, implementation, application, and challenges
The ability to carry out signal processing, classification, recognition, and computation in
artificial spiking neural networks (SNNs) is mediated by their synapses. In particular, through …
artificial spiking neural networks (SNNs) is mediated by their synapses. In particular, through …
A biological-realtime neuromorphic system in 28 nm CMOS using low-leakage switched capacitor circuits
C Mayr, J Partzsch, M Noack… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
A switched-capacitor (SC) neuromorphic system for closed-loop neural coupling in 28 nm
CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (ie …
CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (ie …
[图书][B] Neuromorphic Engineering: The Scientist's, Algorithms Designer's and Computer Architect's Perspectives on Brain-Inspired Computing
EE Tsur - 2021 - taylorfrancis.com
The brain is not a glorified digital computer. It does not store information in registers, and it
does not mathematically transform mental representations to establish perception or …
does not mathematically transform mental representations to establish perception or …
Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs
Memristive devices are popular among neuromorphic engineers for their ability to emulate
forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms …
forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms …
A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent “Heterosynaptic” LTD in the Dentate Gyrus In Vivo
P Jedlicka, L Benuskova… - PLoS computational …, 2015 - journals.plos.org
Long-term potentiation (LTP) and long-term depression (LTD) are widely accepted to be
synaptic mechanisms involved in learning and memory. It remains uncertain, however …
synaptic mechanisms involved in learning and memory. It remains uncertain, however …
Implementation of a spike-based perceptron learning rule using TiO2−x memristors
Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to
various input environments. Neuromorphic systems need to implement plastic synapses to …
various input environments. Neuromorphic systems need to implement plastic synapses to …
A neuromorphic VLSI design for spike timing and rate based synaptic plasticity
Abstract Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic
plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is …
plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is …