Our approach is to use minimum probability flow (MPF) parameter estimation to deterministically fit very large Hopfield networks on windowed spike trains obtained from recordings of spontaneous activity of neurons in cat visual cortex. Examining the structure of the network memories over the spiking ...
举例来说,Elman神经网络(ENN)和它的变式,在隐藏层中添加了一个有关上下文的层,作为一种延迟记忆算子,因此能够对时变特性保持自适应。Hopfield是一种非线性的单层反馈网络,每个节点的输出都会反馈到别的节点的输入位置,常常用在控制系统的约束优化。NARX-Type RNN是一种带反馈的时延RNN,会根据外部输入提供更灵活的结...
for example, the Hopfield networks, dealt with in Chap. 4, which respond to the initial conditions by evolving towards a fixed point: they may be considered as cognitive systems capable of learning, and their memory is coded in the weights of the interconnections between...
N. Estimating memory deterioration rates following neurodegeneration and traumatic brain injuries in a Hopfield network model. Front. Neurosci 11, 623 (2017). Article Google Scholar Burak, Y. & Fiete, I. R. Accurate path integration in continuous attractor network models of grid cells. PLoS ...
The connection patterns of neural circuits in the brain form a complex network. Collective signalling within the network manifests as patterned neural activity and is thought to support human cognition and adaptive behaviour. Recent technological advance
Given a sufficiently large dataset of input–output pairs, a training algorithm can be used to automatically learn the mapping from inputs to outputs by tuning a set of parameters at each layer in the network. While in many cases the elementary building blocks of a Deep Learning system are ...
The latter uses the predictions of the learned neural network model to generate candidate microstructures that are supposed to (i) yield properties inside a defined target region and (ii) are reachable by the underlying manufacturing process, see Fig. 3. The details of the SMTLO approach is ...
Active modules mark regions of a network that are most active during a given cellular or disease response and can identify important biomarkers, disease mechanisms and therapeutic targets. Conserved modules are revealed through the alignment or comparison of networks across multiple species. Such modules...
A new particle swarm optimization (PSO) algorithm having a chaotic Hopfield Neural Network (HNN) structure is proposed. Particles exhibit chaotic behaviour before converging to a stable fixed point which is determined by the best points found by the individual particles and the swarm. During the ev...
- 《Zeitschrift Fur Naturforschung C A Journal of Biosciences》 被引量: 6发表: 1998年 Modular architecture for Hopfield network and distance based training algorithm for pattern association In this paper, a two-dimensional modular architecture for Hopfield neural network and a distance based training ...