Results 71 to 80 of about 16,068 (283)
Frontiers in Neuroscience, 2020 Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence.
S. Lobov +4 more
semanticscholar +1 more source
Combined effects of STDP and homeostatic structural plasticity on coherence resonance [PDF]
, 2023 Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus, this paper investigates
arxiv +1 more source
Dendritic-Inspired Processing Enables Bio-Plausible STDP in Compound Binary Synapses
, 2018 Brain-inspired learning mechanisms, e.g. spike timing dependent plasticity (STDP), enable agile and fast on-the-fly adaptation capability in a spiking neural network. When incorporating emerging nanoscale resistive non-volatile memory (NVM) devices, with
Saxena, Vishal, Wu, Xinyu
core +1 more source
Interplay between dendritic non-linearities and STDP [PDF]
BMC Neuroscience, 2011 Recent results about dendritic computation of responses to presynaptic stimulations have raised a lot of interest. In particular, the integration of postsynaptic potentials (PSPs) exhibits non-linearities depending on their location on dendrites, even before it reaches the soma [1].
Taro Toyoizumi +2 more
openaire +3 more sources
STDP-based Associative Memory Formation and Retrieval [PDF]
arXiv, 2021 Spike-timing-dependent plasticity(STDP) is a biological process in which the precise order and timing of neuronal spikes affect the degree of synaptic modification. While there have been numerous research focusing on the role of STDP in neural coding, the functional implications of STDP at the macroscopic level in the brain have not been fully explored
arxiv
Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP
, 2019 Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware. However, the performance of these models is currently behind traditional methods. Introducing multi-layered SNNs is a promising way to reduce this gap.
Bilasco, Ioan Marius +4 more
core +1 more source
A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing [PDF]
, 2015 Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor ...
Saxena, Vishal, Wu, Xinyu, Zhu, Kehan
core +3 more sources
IEEE Access, 2023 In designing neuromorphic circuits and systems, developing compact and energy-efficient neuron and synapse circuits is essential for high-performance on-chip neural architectures. Toward that end, this work utilizes the advanced low-power and compact 7nm
Mohammad Khaleqi Qaleh Jooq +4 more
doaj +1 more source
Modulating STDP Balance Impacts the Dendritic Mosaic [PDF]
Frontiers in Computational Neuroscience, 2017 The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to ...
Thomas Launey +2 more
openaire +5 more sources
Advanced Functional Materials, EarlyView.Heterojunctions combining halide perovskites with low‐dimensional materials enhance optoelectronic devices by enabling precise charge control and improving efficiency, stability, and speed. These synergies advance flexible electronics, wearable sensors, and neuromorphic computing, mimicking biological vision for real‐time image analysis and intelligent
Yu‐Jin Du +11 more
wiley +1 more source