Entorhinal/dentate excitation of CA3: A critical variable in hippocampal models

By Sean M. Polyn, Xiangbao Wu, William B. Levy. Neurocomputing, 2000

1,637 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/27fceb7f7342004dbbb5f987b77894e43ac4a19b

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

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Adaptive stochastic resonance (1998, background): “Adaptive fuzzy systems o er a balance between the structured and symbolic rulebased expert systems found in arti cial intelligence [221] and the unstructured but numeric approximators found in modern neural networks [100, 101, 132].”
A delay fractioning approach to global synchronization of delayed complex networks with stochastic disturbances (2008, background): “It is worth pointing out that, among most existing results, the network synchronization problem has been predominantly studied for deterministic complex networks with or without delays, see [2–5,9–14] and the references therein.”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, methodology): “Recently another elegant graph based PCA method [ Tang et al. , 2017 ] is proposed spectral clustering with out-of-sample case.”
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation (2019, methodology): “DeepTD is adapted from the DQN-based RL summariser [Yao et al., 2018] and is trained by taking U∗ as the rewards (see §4).”
Predicting protein-protein interactions from primary structure (2001, background): “This means that a conﬁdence level may be assigned to the prediction, and alleviates problems with overﬁtting inherent in neural network function approximation (Hecht-Nielsen, 1989).”
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images (2019, methodology): “This similarly applies to medical images in a clinical environment, wherein physicians’ specialty is critical [Han et al., 2018a; Frid-Adar et al., 2018].”
Error-backpropagation in temporally encoded networks of spiking neurons (2000, methodology): “(5) For error-backpropagation, we treat each synaptic terminal as a separate connection k with weight w k ij .”
Transition to chaos in random neuronal networks (2015, result): “We show that when synaptic time constants are slow relative to the mean inverse firing rates the network undergoes a transition from fast spiking fluctuations with constant rates to a state where the firing rates exhibit chaotic fluctuations, similar to the transition predicted by rate based…”
Learning in the presence of concept drift and hidden contexts (2004, methodology): “For instance, the momentum function in the deltarule used in neural networks (see, e.g., Hecht-Nielsen, 1990) essentially realizes aform of memory decay; recent experience can be made to have a stronger in uenceon the network's internal weight con guration than very old examples.”