Results 261 to 270 of about 16,068 (283)

STDP within NDS Neurons [PDF]

, 2010
We investigate the use of Spike Time Dependent Plasticity (STDP) in a network of Nonlinear Dynamic State (NDS) Neurons We find out that NDS Neurons can implement a form of STDP; a biological phenomenon that neocortical neurons own, and would preserve their temporal asymmetric windows of firing activity, while stabilizing to Unstable Periodic Orbits ...
openaire   +1 more source

Some of the next articles are maybe not open access.

Related searches:

CORDIC-SNN: On-FPGA STDP Learning With Izhikevich Neurons

IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2019
This paper proposes a neuromorphic platform for on-FPGA online spike timing dependant plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) algorithms. The implemented platform comprises two main components.
Moslem Heidarpur, A. Ahmadi, M. Ahmadi, Mostafa Rahimi Azghadi   +3 more
semanticscholar   +1 more source

Analog Neurons with Dopamine-Modulated STDP

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2019
Neuron circuits embedded with dopamine-modulated spike-timing-dependent plasticity (STDP) are described in this paper. The circuit functions are discussed in detail with HSPICE simulations. This work explores a possible learning process including short-term STDP and longer-term dopamine reward in neuromorphic systems including a noisy synapse that ...
Alice C. Parker, Kun Yue
openaire   +2 more sources

STDP-Based Unsupervised Spike Pattern Learning in a Photonic Spiking Neural Network With VCSELs and VCSOAs

IEEE Journal of Selected Topics in Quantum Electronics, 2019
We propose a photonic spiking neural network (SNN) consisting of photonic spiking neurons based on vertical-cavity surface-emitting lasers (VCSELs). The photonic spike timing dependent plasticity (STDP) is implemented in a vertical-cavity semiconductor ...
S. Xiang, Yahui Zhang, Junkai Gong, Xingxing Guo, Lin Lin, Y. Hao   +5 more
semanticscholar   +1 more source

An event-driven Spike-DBN model for fault diagnosis using reward-STDP.

ISA transactions, 2023
Y. Liu, Xiuqing Wang, ZiHang Zeng, Wei Zhang, Hong Qu   +4 more
semanticscholar   +1 more source

Synaptic regulation on various STDP rules

Neurocomputing, 2004
Abstract An additive rule of spike-timing-dependent synaptic plasticity (STDP) automatically achieves synaptic competition and activity regulation, where synaptic balance is moderately regulated to control the post synaptic activity (Song et al., Nature Neurosci. 3 (2000) 919).
Kaoru Nakano, Yutaka Sakai, Shuji Yoshizawa   +2 more
openaire   +2 more sources

Influence of the Endogenous Acetylcholine on STDP Induction

2013
Cholinergic inputs from the medial septum are projected to pyramidal neurons in hippocampal CA1 and release acetylcholine (ACh) from their terminals. The cholinergic inputs are considered to be integrated with sensory inputs and to play a crucial role in learning and memory. Meanwhile, it has been reported that the relative timing between pre- and post-
Minoru Tsukada, Takeshi Aihara, Eriko Sugisaki, Yasuhiro Fukushima   +3 more
openaire   +2 more sources

STDP-based behavior learning on the TriBot robot

SPIE Proceedings, 2009
This paper describes a correlation-based navigation algorithm, based on an unsupervised learning paradigm for spiking neural networks, called Spike Timing Dependent Plasticity (STDP). This algorithm was implemented on a new bio-inspired hybrid mini-robot called TriBot to learn and increase its behavioral capabilities.
ARENA, Paolo Pietro, S. DE FIORE, L. PATANÈ, M. POLLINO, C. VENTURA   +4 more
openaire   +3 more sources

Competitive STDP Learning of Overlapping Spatial Patterns

Neural Computation, 2015
Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple ...
openaire   +2 more sources

Emergence of Optimal Decoding of Population Codes Through STDP

Neural Computation, 2013
The brain faces the problem of inferring reliable hidden causes from large populations of noisy neurons, for example, the direction of a moving object from spikes in area MT. It is known that a theoretically optimal likelihood decoding could be carried out by simple linear readout neurons if weights of synaptic connections were set to certain values ...
Habenschuss S., Puhr H., Maass W.
openaire   +2 more sources