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How many neurons does Lstm have?

Computer science

Word (computer architecture)

Process (computing)

Recurrent neural network

Prefrontal cortex

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Open access•Journal Article•DOI ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit Qicheng Tang, Mengning Yang, Yang Ying - Show less +2 more 06 Feb 2019-Journal of Advanced Transportation 43 Citations	Compared with other conventional models, ST-LSTM network can achieve a better performance in experiments.
Book Chapter•DOI Memory networks in the prefrontal cortex. Joaquin M. Fuster 01 Jan 2000-Progress in Brain Research 45 Citations	Thus, one neuron or group of neurons anywhere in the cortex can be a part of many networks and thus many memories.
Open access•Journal Article•DOI A quantitative description of dendritic conductances and its application to dendritic excitation in layer 5 pyramidal neurons. Mara Almog, Alon Korngreen - Show less +1 more 01 Jan 2014-The Journal of Neuroscience 71 Citations	Moreover, in addition to faithfully predicting many experimental results recorded from the apical dendrite of L5 pyramidal neurons, the model validates a new methodology for mechanistic modeling of neurons in the CNS.
Open access•Proceedings Article•DOI Advanced LSTM: A Study About Better Time Dependency Modeling in Emotion Recognition Fei Tao, Gang Liu - Show less +1 more 15 Apr 2018 40 Citations	The A-LSTM outperforms the conventional LSTM by 5.5% relatively.
Journal Article•DOI The crayfish lateral giants as command neurons for escape behavior Gene C. Olson, Franklin B. Krasne - Show less +1 more 09 Jun 1981-Brain Research 47 Citations	We conclude that the LGs have most of the features of command neurons.
Journal Article•DOI Novel Efficient RNN and LSTM-Like Architectures: Recurrent and Gated Broad Learning Systems and Their Applications for Text Classification Jie Du, Chi-Man Vong, C. L. Philip Chen - Show less +2 more 17 Feb 2021-IEEE Transactions on Systems, Man, and Cybernetics 93 Citations	From the experimental results, the proposed methods achieve higher accuracies than LSTM while taking significantly less training time on most evaluated datasets, especially when the LSTM is in deep architecture.
Open access•Proceedings Article•DOI Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition Luc Mioulet, Gautier Bideault, Clément Chatelain, Thierry Paquet, Stephan Brunessaux - Show less +4 more 08 Feb 2015 11 Citations	Our results show that the low level combination works best, thanks to the powerful data modeling of the LSTM neurons.

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