This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Borzello, M. Assessments of dentate gyrus function: discoveries and debates. Nat. Rev. Neurosci. 24,502–517(2023).BorzelloM.Assessments of dentate gyrus function: discoveries and debates. Nat. Rev. Neurosci. 24, 502–517 (2023).Search in Google Scholar
Asutay, E. Affective calculus: the construction of affect through information integration over time. Emotion 21,159–174 (2019).AsutayE.Affective calculus: the construction of affect through information integration over time. Emotion21, 159–174 (2019).Search in Google Scholar
Herweg, N. A., Solomon, E. A. & Kahana, M. J. Theta oscillations in human memory. Trends in Cognitive Sciences 24,208–227 (2020).HerwegN. A.SolomonE. A.KahanaM. J.,Theta oscillations in human memory. Trends in Cognitive Sciences24, 208–227 (2020).Search in Google Scholar
L. Kang and T. Toyoizumi. Hopfield-like network with complementary encodings of memories. Phys. Rev. E, 108(5):054410, 2023.KangL.ToyoizumiT..Hopfield-like network with complementary encodings of memories. Phys. Rev. E, 108(5):054410, 2023.Search in Google Scholar
Zheng, J. Multiplexing of theta and alpha rhythms in the amygdala-hippocampal circuit supports pattern separation of emotional information. Neuron 102,887–898 (2019).ZhengJ.Multiplexing of theta and alpha rhythms in the amygdala-hippocampal circuit supports pattern separation of emotional information. Neuron102, 887–898 (2019).Search in Google Scholar
Barry, D. N. & Love, B. C. A neural network account of memory replay and knowledge consolidation. Cereb. Cortex. 33, 83–95(2022).BarryD. N.LoveB. C.A neural network account of memory replay and knowledge consolidation. Cereb. Cortex. 33, 83–95(2022).Search in Google Scholar
Xiao, H., Rasul, K., & Vollgraf, R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms arXiv 1708.07747 (2017).XiaoH.RasulK.VollgrafR.,Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms arXiv 1708.07747 (2017).Search in Google Scholar
Qasim, S. E., Fried, I. & Jacobs, J. Phaseprecessioninthe human hippocampus and entorhinal cortex. Cell 184,3242–3255 (2021).QasimS. E.FriedI.JacobsJ.Phaseprecessioninthe human hippocampus and entorhinal cortex. Cell184, 3242–3255 (2021).Search in Google Scholar
Vertes, E., and Sahani, M. (2019). A neurally plausible model learns successor representations in partially observable environments. Adv. Neural Inf. Process. Syst. 32, 13714–13724.VertesE.SahaniM., (2019). A neurally plausible model learns successor representations in partially observable environments. Adv. Neural Inf. Process. Syst. 32, 13714–13724.Search in Google Scholar
Sun, C., Yang, W., Martin, J., and Tonegawa, S. (2020). Hippocampal neurons represent events as transferable units of experience. Nat. Neurosci. 23,651–663.SunC.YangW.MartinJ.TonegawaS., (2020). Hippocampal neurons represent events as transferable units of experience. Nat. Neurosci. 23, 651–663.Search in Google Scholar