Efficient, continual, and generalized learning in the brain – neural mechanism of Mental Schema 2.0 –

References

Adamantidis, A.R., Gutierrez Herrera, C., and Gent, T.C. (2019). Oscillating circuitries in the sleeping brain. Nat. Rev. Neurosci. 20: 746–762, https://doi.org/10.1038/s41583-019-0223-4.Search in Google Scholar PubMed

Ambrose, R.E., Pfeiffer, B.E., and Foster, D.J. (2016). Reverse replay of hippocampal place cells is uniquely modulated by changing reward. Neuron 91: 1124–1136, https://doi.org/10.1016/j.neuron.2016.07.047.Search in Google Scholar PubMed PubMed Central

Asok, A., Leroy, F., Rayman, J.B., and Kandel, E.R. (2019). Molecular mechanisms of the memory trace. Trends Neurosci. 42: 14–22, https://doi.org/10.1016/j.tins.2018.10.005.Search in Google Scholar PubMed PubMed Central

Babichev, A. and Dabaghian, Y.A. (2018). Topological schemas of memory spaces. Front. Comput. Neurosci. 12: 27, https://doi.org/10.3389/fncom.2018.00027.Search in Google Scholar PubMed PubMed Central

Babichev, A., Morozov, D., and Dabaghian, Y. (2019). Replays of spatial memories suppress topological fluctuations in cognitive map. Netw. Neurosci. 3: 707–724, https://doi.org/10.1162/netn_a_00076.Search in Google Scholar PubMed PubMed Central

Bahtiyar, S., Gulmez Karaca, K., Henckens, M.J.A.G., and Roozendaal, B. (2020). Norepinephrine and glucocorticoid effects on the brain mechanisms underlying memory accuracy and generalization. Mol. Cell. Neurosci. 108: 103537, https://doi.org/10.1016/j.mcn.2020.103537.Search in Google Scholar PubMed

Baraduc, P., Duhamel, J.R., and Wirth, S. (2019). Schema cells in the macaque hippocampus. Science 363: 635–639, https://doi.org/10.1126/science.aav5404.Search in Google Scholar PubMed

Baram, A.B., Muller, T.H., Nili, H., Garvert, M.M., and Behrens, T.E.J. (2021). Entorhinal and ventromedial prefrontal cortices abstract and generalize the structure of reinforcement learning problems. Neuron 109: 713.e7–723.e7, https://doi.org/10.1016/j.neuron.2020.11.024.Search in Google Scholar PubMed PubMed Central

Bassett, D.S. and Sporns, O. (2017). Network neuroscience. Nat. Neurosci. 20: 353–364, https://doi.org/10.1038/nn.4502.Search in Google Scholar PubMed PubMed Central

Bassett, D.S., Xia, C.H., and Satterthwaite, T.D. (2018). Understanding the emergence of neuropsychiatric disorders with network neuroscience. Biol. Psychiatr. Cognit. Neurosci. Neuroimaging 3: 742–753, https://doi.org/10.1016/j.bpsc.2018.03.015.Search in Google Scholar PubMed PubMed Central

Behrens, T.E.J., Muller, T.H., Whittington, J.C.R., Mark, S., Baram, A.B., Stachenfeld, K.L., and Kurth-Nelson, Z. (2018). What is a cognitive map? Organizing knowledge for flexible behavior. Neuron 100: 490–509, https://doi.org/10.1016/j.neuron.2018.10.002.Search in Google Scholar PubMed

Bhattarai, B., Lee, J.W., and Jung, M.W. (2020). Distinct effects of reward and navigation history on hippocampal forward and reverse replays. Proc. Natl. Acad. Sci. U. S. A. 117: 689–697, https://doi.org/10.1073/pnas.1912533117.Search in Google Scholar PubMed PubMed Central

Bongard, S. and Nieder, A. (2010). Basic mathematical rules are encoded by primate prefrontal cortex neurons. Proc. Natl. Acad. Sci. U. S. A. 107: 2277–2282, https://doi.org/10.1073/pnas.0909180107.Search in Google Scholar PubMed PubMed Central

Bonifazi, P., Goldin, M., Picardo, M.A., Jorquera, I., Cattani, A., Bianconi, G., Represa, A., Ben-Ari, Y., and Cossart, R. (2009). GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326: 1419–1424, https://doi.org/10.1126/science.1175509.Search in Google Scholar PubMed

Bowman, C.R. and Zeithamova, D. (2018). Abstract memory representations in the ventromedial prefrontal cortex and hippocampus support concept generalization. J. Neurosci. 38: 2605–2614, https://doi.org/10.1523/jneurosci.2811-17.2018.Search in Google Scholar

Broadbent, N.J., Squire, L.R., and Clark, R.E. (2004). Spatial memory, recognition memory, and the hippocampus. Proc. Natl. Acad. Sci. U. S. A. 101: 14515–14520, https://doi.org/10.1073/pnas.0406344101.Search in Google Scholar PubMed PubMed Central

Brunel, N., Hakim, V., Isope, P., Nadal, J.P., and Barbour, B. (2004). Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell. Neuron 43: 745–757, https://doi.org/10.1016/s0896-6273(04)00528-8.Search in Google Scholar

Buch, E.R., Claudino, L., Quentin, R., Bönstrup, M., and Cohen, L.G. (2021). Consolidation of human skill linked to waking hippocampo-neocortical replay. Cell Rep. 35: 109193, https://doi.org/10.1016/j.celrep.2021.109193.Search in Google Scholar PubMed PubMed Central

Bui, K., Park, F., Zhang, S., Qi, Y., and Xin, J. (2021). Structured sparsity of convolutional neural networks via nonconvex sparse group regularization. Front. Appl. Math. Stat. 6: 62, https://doi.org/10.3389/fams.2020.529564.Search in Google Scholar

Burak, Y. and Fiete, I.R. (2009). Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5: e1000291, https://doi.org/10.1371/journal.pcbi.1000291.Search in Google Scholar PubMed PubMed Central

Burgess, N. (2008). Grid cells and theta as oscillatory interference: theory and predictions. Hippocampus 18: 1157–1174, https://doi.org/10.1002/hipo.20518.Search in Google Scholar PubMed PubMed Central

Burgess, N. and O’Keefe, J. (2011). Models of place and grid cell firing and theta rhythmicity. Curr. Opin. Neurobiol. 21: 734–744, https://doi.org/10.1016/j.conb.2011.07.002.Search in Google Scholar PubMed PubMed Central

Buzsáki, G. (2015). Hippocampal sharp wave-ripple: a cognitive biomarker for episodic memory and planning. Hippocampus 25: 1073–1188, https://doi.org/10.1002/hipo.22488.Search in Google Scholar PubMed PubMed Central

Buzsáki, G. and Mizuseki, K. (2014). The log-dynamic brain: how skewed distributions affect network operations. Nat. Rev. Neurosci. 15: 264–278, https://doi.org/10.1038/nrn3687.Search in Google Scholar PubMed PubMed Central

Buzsáki, G. and Tingley, D. (2018). Space and time: the hippocampus as a sequence generator. Trends Cognit. Sci. 22: 853–869, https://doi.org/10.1016/j.tics.2018.07.006.Search in Google Scholar PubMed PubMed Central

Buzsáki, G., Leung, L.W., and Vanderwolf, C.H. (1983). Cellular bases of hippocampal EEG in the behaving rat. Brain Res. 287: 139–171, https://doi.org/10.1016/0165-0173(83)90037-1.Search in Google Scholar PubMed

Cang, Z. and Wei, G.W. (2017). TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput. Biol. 13: e1005690, https://doi.org/10.1371/journal.pcbi.1005690.Search in Google Scholar PubMed PubMed Central

Carlsson, G. (2009). Topology and data. Bull. Am. Math. Soc. 46: 255–308, https://doi.org/10.1090/s0273-0979-09-01249-x.Search in Google Scholar

Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., and Fiete, I. (2019). The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat. Neurosci. 22: 1512–1520, https://doi.org/10.1038/s41593-019-0460-x.Search in Google Scholar PubMed

Chen, G., Zou, X., Watanabe, H., van Deursen, J.M., and Shen, J. (2010). CREB binding protein is required for both short-term and long-term memory formation. J. Neurosci. 30: 13066–13077, https://doi.org/10.1523/jneurosci.2378-10.2010.Search in Google Scholar PubMed PubMed Central

Chen, Z., Gomperts, S.N., Yamamoto, J., and Wilson, M.A. (2014). Neural representation of spatial topology in the rodent hippocampus. Neural Comput. 26: 1–39, https://doi.org/10.1162/neco_a_00538.Search in Google Scholar PubMed PubMed Central

Chen, Z., Kloosterman, F., Brown, E.N., and Wilson, M.A. (2012). Uncovering spatial topology represented by rat hippocampal population neuronal codes. J. Comput. Neurosci. 33: 227–255, https://doi.org/10.1007/s10827-012-0384-x.Search in Google Scholar PubMed PubMed Central

Chen, Z. and Liu, B. (2018). Lifelong machine learning. In: Synthesis Lectures on artificial Intelligence and machine learning, 2nd ed. Cham: Springer, pp. 1–207.10.2200/S00832ED1V01Y201802AIM037Search in Google Scholar

Chung, M.K., Lee, H., DiChristofano, A., Ombao, H., and Solo, V. (2019). Exact topological inference of the resting-state brain networks in twins. Netw. Neurosci. 3: 674–694, https://doi.org/10.1162/netn_a_00091.Search in Google Scholar PubMed PubMed Central

Colbran, R.J. (2015). Thematic minireview series: molecular mechanisms of synaptic plasticity. J. Biol. Chem. 290: 28594–28595, https://doi.org/10.1074/jbc.r115.696468.Search in Google Scholar PubMed PubMed Central

Cossell, L., Iacaruso, M.F., Muir, D.R., Houlton, R., Sader, E.N., Ko, H., Hofer, S.B., and Mrsic-Flogel, T.D. (2015). Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518: 399–403, https://doi.org/10.1038/nature14182.Search in Google Scholar PubMed PubMed Central

Curtis, A., Silver, T., Tenenbaum, J.B., Lozano-Pérez, T., and Kaelbling, L. (2022). Discovering state and action abstractions for generalized task and motion planning. In: The 36th AAAI conference on artificial intelligence (AAAI-22), AAII, Vol. 36, pp. 5377–5384.10.1609/aaai.v36i5.20475Search in Google Scholar

Curto, C. (2017). What can topology tell us about the neural code? Bull. Am. Math. Soc. 54: 63–78, https://doi.org/10.1090/bull/1554.Search in Google Scholar

Dabaghian, Y. (2020). From topological analyses to functional modeling: the case of hippocampus. Front. Comput. Neurosci. 14: 593166, https://doi.org/10.3389/fncom.2020.593166.Search in Google Scholar PubMed PubMed Central

Dabaghian, Y., Brandt, V.L., and Frank, L.M. (2014). Reconceiving the hippocampal map as a topological template. Elife 3: e03476, https://doi.org/10.7554/elife.03476.Search in Google Scholar PubMed PubMed Central

Dabaghian, Y., Mémoli, F., Frank, L., and Carlsson, G. (2012). A topological paradigm for hippocampal spatial map formation using persistent homology. PLoS Comput. Biol. 8: e1002581, https://doi.org/10.1371/journal.pcbi.1002581.Search in Google Scholar PubMed PubMed Central

Danjo, T., Toyoizumi, T., and Fujisawa, S. (2018). Spatial representations of self and other in the hippocampus. Science 359: 213–218, https://doi.org/10.1126/science.aao3898.Search in Google Scholar PubMed

Dehaene-Lambertz, G. and Spelke, E.S. (2015). The infancy of the human brain. Neuron 88: 93–109, https://doi.org/10.1016/j.neuron.2015.09.026.Search in Google Scholar PubMed

DiTullio, R.W. and Balasubramanian, V. (2021). Dynamical self-organization and efficient representation of space by grid cells. Curr. Opin. Neurobiol. 70: 206–213, https://doi.org/10.1016/j.conb.2021.11.007.Search in Google Scholar PubMed PubMed Central

Dragoi, G. and Buzsáki, G. (2006). Temporal encoding of place sequences by hippocampal cell assemblies. Neuron 50: 145–157, https://doi.org/10.1016/j.neuron.2006.02.023.Search in Google Scholar PubMed

Dragoi, G. and Tonegawa, S. (2011). Preplay of future place cell sequences by hippocampal cellular assemblies. Nature 469: 397–401, https://doi.org/10.1038/nature09633.Search in Google Scholar PubMed PubMed Central

Edwards, L.A., Wagner, J.B., Simon, C.E. and Hyde, D.C. (2016). Functional brain organization for number processing in pre-verbal infants. Dev. Sci. 19: 757–769, https://doi.org/10.1111/desc.12333.Search in Google Scholar PubMed

Ellis, C.T., Lesnick, M., Henselman-Petrusek, G., Keller, B., and Cohen, J.D. (2019). Feasibility of topological data analysis for event-related fMRI. Netw. Neurosci. 3: 695–706, https://doi.org/10.1162/netn_a_00095.Search in Google Scholar PubMed PubMed Central

Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature 542: 115–118, https://doi.org/10.1038/nature21056.Search in Google Scholar PubMed PubMed Central

Fanselow, M. and Poulos, A.M. (2005). The neuroscience of mammalian associative learning. Annu. Rev. Psychol. 56: 207–234, https://doi.org/10.1146/annurev.psych.56.091103.070213.Search in Google Scholar PubMed

FeldmanHall, O., Montez, D.F., Phelps, E.A., Davachi, L., and Murty, V.P. (2021). Hippocampus guides adaptive learning during dynamic social interactions. J. Neurosci. 41: 1340–1348, https://doi.org/10.1523/jneurosci.0873-20.2020.Search in Google Scholar PubMed PubMed Central

Feldmeyer, D., Egger, V., Lübke, J., and Sakmann, B. (1999). Reliable synaptic connections between pairs of excitatory layer 4 neurones within a single “barrel” of developing rat somatosensory cortex. J. Physiol. 521: 169–190, https://doi.org/10.1111/j.1469-7793.1999.00169.x.Search in Google Scholar PubMed PubMed Central

Fiete, I.R., Burak, Y., and Brookings, T. (2008). What grid cells convey about rat location. J. Neurosci. 28: 6858–6871, https://doi.org/10.1523/jneurosci.5684-07.2008.Search in Google Scholar

Flesch, T., Balaguer, J., Dekker, R., Nili, H., and Summerfield, C. (2018). Comparing continual task learning in minds and machines. Proc. Natl. Acad. Sci. U. S. A. 115: E10313–E10322, https://doi.org/10.1073/pnas.1800755115.Search in Google Scholar PubMed PubMed Central

Foster, D.J. and Wilson, M.A. (2006). Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440: 680–683, https://doi.org/10.1038/nature04587.Search in Google Scholar PubMed

Foster, D.J. and Wilson, M.A. (2007). Hippocampal theta sequences. Hippocampus 17: 1093–1099, https://doi.org/10.1002/hipo.20345.Search in Google Scholar PubMed

Galland, B.C., Taylor, B.J., Elder, D.E., and Herbison, P. (2012). Normal sleep patterns in infants and children: a systematic review of observational studies. Sleep Med. Rev. 16: 213–222, https://doi.org/10.1016/j.smrv.2011.06.001.Search in Google Scholar PubMed

Gardner, R.J., Hermansen, E., Pachitariu, M., Burak, Y., Baas, N.A., Dunn, B.A., Moser, M.B., and Moser, E.I. (2022). Toroidal topology of population activity in grid cells. Nature 602: 123–128, https://doi.org/10.1038/s41586-021-04268-7.Search in Google Scholar PubMed PubMed Central

Gauthier, J.L. and Tank, D.W. (2018). A dedicated population for reward coding in the hippocampus. Neuron 99: 179.e7–193.e7, https://doi.org/10.1016/j.neuron.2018.06.008.Search in Google Scholar PubMed PubMed Central

Ge, X., Zhang, K., Gribizis, A., Hamodi, A.S., Sabino, A.M., and Crair, M.C. (2021). Retinal waves prime visual motion detection by simulating future optic flow. Science 373, https://doi.org/10.1126/science.abd0830.Search in Google Scholar PubMed PubMed Central

Ghrist, R. (2008). Barcodes: the persistent topology of data. Bull. Amer. Math. Soc. 45: 61–75.10.1090/S0273-0979-07-01191-3Search in Google Scholar

Gillespie, A.K., Astudillo Maya, D.A., Denovellis, E.L., Liu, D.F., Kastner, D.B., Coulter, M.E., Roumis, D.K., Eden, U.T., and Frank, L.M. (2021). Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice. Neuron 109: 3149.e6–3163.e6, https://doi.org/10.1016/j.neuron.2021.07.029.Search in Google Scholar PubMed PubMed Central

Girardeau, G., Benchenane, K., Wiener, S.I., Buzsáki, G., and Zugaro, M.B. (2009). Selective suppression of hippocampal ripples impairs spatial memory. Nat. Neurosci. 12: 1222–1223, https://doi.org/10.1038/nn.2384.Search in Google Scholar PubMed

Girardeau, G. and Lopes-Dos-Santos, V. (2021). Brain neural patterns and the memory function of sleep. Science 374: 560–564, https://doi.org/10.1126/science.abi8370.Search in Google Scholar PubMed PubMed Central

Giusti, C., Ghrist, R., and Bassett, D.S. (2016). Two’s company, three (or more) is a simplex: algebraic-topological tools for understanding higher-order structure in neural data. J. Comput. Neurosci. 41: 1–14, https://doi.org/10.1007/s10827-016-0608-6.Search in Google Scholar PubMed PubMed Central

Giusti, C., Pastalkova, E., Curto, C., and Itskov, V. (2015). Clique topology reveals intrinsic geometric structure in neural correlations. Proc. Natl. Acad. Sci. U. S. A. 112: 13455–13460, https://doi.org/10.1073/pnas.1506407112.Search in Google Scholar PubMed PubMed Central

Golub, M.D., Sadtler, P.T., Oby, E.R., Quick, K.M., Ryu, S.I., Tyler-Kabara, E.C., Batista, A.P., Chase, S.M., and Yu, B.M. (2018). Learning by neural reassociation. Nat. Neurosci. 21: 607–616, https://doi.org/10.1038/s41593-018-0095-3.Search in Google Scholar PubMed PubMed Central

Gonzalez, C., Jiang, X., Gonzalez-Martinez, J., and Halgren, E. (2022). Human spindle variability. J. Neurosci. 42: 4517–4537, https://doi.org/10.1523/jneurosci.1786-21.2022.Search in Google Scholar

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2020). Generative adversarial networks. Commun. ACM 63: 139–144, https://doi.org/10.1145/3422622.Search in Google Scholar

Gothard, K.M., Skaggs, W.E., and Mcnaughton, B.L. (1996). Dynamics of mismatch correction in the hippocampal ensemble code for space: interaction between path integration and environmental cues. J. Neurosci. 16: 8027–8040, https://doi.org/10.1523/jneurosci.16-24-08027.1996.Search in Google Scholar PubMed PubMed Central

Gridchyn, I., Schoenenberger, P., O’Neill, J., and Csicsvari, J. (2020). Assembly-specific disruption of hippocampal replay leads to selective memory deficit. Neuron 106: 291.e6–300.e6, https://doi.org/10.1016/j.neuron.2020.01.021.Search in Google Scholar PubMed

Grosmark, A.D. and Buzsáki, G. (2016). Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences. Science 351: 1440–1443, https://doi.org/10.1126/science.aad1935.Search in Google Scholar PubMed PubMed Central

Gulledge, A. and Stuart, G. (2003). Action potential initiation and propagation in layer 5 pyramidal neurons of the rat prefrontal cortex: absence of dopamine modulation. J. Neurosci. 23: 11363–11372, https://doi.org/10.1523/JNEUROSCI.23-36-11363.2003.Search in Google Scholar PubMed PubMed Central

Hadsell, R., Rao, D., Rusu, A.A. and Pascanu, R. (2020). Embracing change: continual learning in deep neural networks. Trends Cognit. Sci. 24: 1028–1040, https://doi.org/10.1016/j.tics.2020.09.004.Search in Google Scholar PubMed

Hasselmo, M.E., Giocomo, L.M., and Zilli, E.A. (2007). Grid cell firing may arise from interference of theta frequency membrane potential oscillations in single neurons. Hippocampus 17: 1252–1271, https://doi.org/10.1002/hipo.20374.Search in Google Scholar PubMed PubMed Central

Hatcher, A. (2001). Algebraic topology. Cambridge Univeristy Press, New York.Search in Google Scholar

Helfrich, R.F., Mander, B.A., Jagust, W.J., Knight, R.T. and Walker, M.P. (2018). Old brains come uncoupled in sleep: slow wave-spindle synchrony, brain atrophy, and forgetting. Neuron 97: 221.e4–230.e4, https://doi.org/10.1016/j.neuron.2017.11.020.Search in Google Scholar PubMed PubMed Central

Hensel, F., Moor, M., and Rieck, B. (2021). A survey of topological machine learning methods. Front. Artif. Intell. 4: 681108, https://doi.org/10.3389/frai.2021.681108.Search in Google Scholar PubMed PubMed Central

Higgins, C., Liu, Y., Vidaurre, D., Kurth-Nelson, Z., Dolan, R., Behrens, T. and Woolrich, M. (2021). Replay bursts in humans coincide with activation of the default mode and parietal alpha networks. Neuron 109: 882.e7–893.e7, https://doi.org/10.1016/j.neuron.2020.12.007.Search in Google Scholar PubMed PubMed Central

Hofer, C., Kwitt, R., Niethammer, M., and Uhl, A. (2017). Deep learning with topological signatures. Adv. Neural Inf. Process. Syst. 30.Search in Google Scholar

Hosny, A., Parmar, C., Coroller, T.P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R.J., Mak, R.H., and Aerts, H.J.W.L. (2018). Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 15: e1002711, https://doi.org/10.1371/journal.pmed.1002711.Search in Google Scholar PubMed PubMed Central

Høydal, Ø.A., Skytøen, E.R., Andersson, S.O., Moser, M.B., and Moser, E.I. (2019). Object-vector coding in the medial entorhinal cortex. Nature 568: 400–404, https://doi.org/10.1038/s41586-019-1077-7.Search in Google Scholar PubMed

Huszár, R., Zhang, Y., Blockus, H., and Buzsáki, G. (2022). Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis. Nat. Neurosci. 25: 1201–1212, https://doi.org/10.1038/s41593-022-01138-x.Search in Google Scholar PubMed

Hyde, D.C., Boas, D.A., Blair, C., and Carey, S. (2010). Near-infrared spectroscopy shows right parietal specialization for number in pre-verbal infants. Neuroimage 53: 647–652, https://doi.org/10.1016/j.neuroimage.2010.06.030.Search in Google Scholar PubMed PubMed Central

Ikegaya, Y., Sasaki, T., Ishikawa, D., Honma, N., Tao, K., Takahashi, N., Minamisawa, G., Ujita, S., and Matsuki, N. (2013). Interpyramid spike transmission stabilizes the sparseness of recurrent network activity. Cerebr. Cortex 23: 293–304, https://doi.org/10.1093/cercor/bhs006.Search in Google Scholar PubMed

Ishikawa, T. and Ikegaya, Y. (2020). Locally sequential synaptic reactivation during hippocampal ripples. Sci. Adv. 6: eaay1492, https://doi.org/10.1126/sciadv.aay1492.Search in Google Scholar PubMed PubMed Central

Izard, V., Dehaene-Lambertz, G., and Dehaene, S. (2008). Distinct cerebral pathways for object identity and number in human infants. PLoS Biol. 6: e11, https://doi.org/10.1371/journal.pbio.0060011.Search in Google Scholar PubMed PubMed Central

Izard, V.R., Sann, C., Spelke, E.S., and Streri, A. (2009). Newborn infants perceive abstract numbers. Proc. Natl. Acad. Sci. U. S. A. 106: 10382–10385, https://doi.org/10.1073/pnas.0812142106.Search in Google Scholar PubMed PubMed Central

Jarrard, L.E. (1993). On the role of the hippocampus in learning and memory in the rat. Behav. Neural. Biol. 60: 9–26, https://doi.org/10.1016/0163-1047(93)90664-4.Search in Google Scholar PubMed

Jeffery, K.J., Gilbert, A., Burton, S., and Strudwick, A. (2003). Preserved performance in a hippocampal-dependent spatial task despite complete place cell remapping. Hippocampus 13: 175–189, https://doi.org/10.1002/hipo.10047.Search in Google Scholar PubMed

Julian, J.B. and Doeller, C.F. (2021). Remapping and realignment in the human hippocampal formation predict context-dependent spatial behavior. Nat. Neurosci. 24: 863–872, https://doi.org/10.1038/s41593-021-00835-3.Search in Google Scholar PubMed

Jung, H., Ju, J., Jung, M., and Kim, J. (2018). Less-forgetful learning for domain expansion in deep neural networks. AAAI 32: 3358–3365, https://doi.org/10.1609/aaai.v32i1.11769.Search in Google Scholar

Kaefer, K., Nardin, M., Blahna, K., and Csicsvari, J. (2020). Replay of behavioral sequences in the medial prefrontal cortex during rule switching. Neuron 106: 154.e6–165.e6, https://doi.org/10.1016/j.neuron.2020.01.015.Search in Google Scholar PubMed

Kanari, L., Dictus, H., Chalimourda, A., Arnaudon, A., Van Geit, W., Coste, B., Shillcock, J., Hess, K., and Markram, H. (2022). Computational synthesis of cortical dendritic morphologies. Cell Rep. 39: 110586, https://doi.org/10.1016/j.celrep.2022.110586.Search in Google Scholar PubMed

Kandel, E.R. (2001). The molecular biology of memory storage: a dialogue between genes and synapses. Science 294: 1030–1038, https://doi.org/10.1126/science.1067020.Search in Google Scholar PubMed

Kang, L. and Balasubramanian, V. (2019). A geometric attractor mechanism for self-organization of entorhinal grid modules. Elife 8, https://doi.org/10.7554/elife.46687.Search in Google Scholar PubMed PubMed Central

Kemker, R. and Christopher, K. (2017). Fearnet: brain-inspired model for incremental learning. arXiv Preprint, https://doi.org/10.48550/arXiv.1711.10563.Search in Google Scholar

Kepecs, A. and Fishell, G. (2014). Interneuron cell types are fit to function. Nature 505: 318–326, https://doi.org/10.1038/nature12983.Search in Google Scholar PubMed PubMed Central

Khazipov, R., Sirota, A., Leinekugel, X., Holmes, G.L., Ben-Ari, Y., and Buzsáki, G. (2004). Early motor activity drives spindle bursts in the developing somatosensory cortex. Nature 432: 758–761, https://doi.org/10.1038/nature03132.Search in Google Scholar PubMed

Khodagholy, D., Gelinas, J.N., and Buzsáki, G. (2017). Learning-enhanced coupling between ripple oscillations in association cortices and hippocampus. Science 358: 369–372, https://doi.org/10.1126/science.aan6203.Search in Google Scholar PubMed PubMed Central

Kim, G., Jang, J., Baek, S., Song, M., and Paik, S.B. (2021). Visual number sense in untrained deep neural networks. Sci. Adv. 7, https://doi.org/10.1126/sciadv.abd6127.Search in Google Scholar PubMed PubMed Central

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.. (2017). Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. U. S. A. 114: 3521–3526, https://doi.org/10.1073/pnas.1611835114.Search in Google Scholar PubMed PubMed Central

Knudsen, E.B. and Wallis, J.D. (2021). Hippocampal neurons construct a map of an abstract value space. Cell 184: 4640.e10–4650.e10, https://doi.org/10.1016/j.cell.2021.07.010.Search in Google Scholar PubMed PubMed Central

Konidaris, G. (2019). On the necessity of abstraction. Curr. Opin. Behav. Sci. 29: 1–7, https://doi.org/10.1016/j.cobeha.2018.11.005.Search in Google Scholar PubMed PubMed Central

Korngiebel, D.M. and Mooney, S.D. (2021). Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery. NPJ Digit. Med. 4: 93, https://doi.org/10.1038/s41746-021-00464-x.Search in Google Scholar PubMed PubMed Central

Krabbe, S., Paradiso, E., d’Aquin, S., Bitterman, Y., Courtin, J., Xu, C., Yonehara, K., Markovic, M., Müller, C., Eichlisberger, T., et al.. (2019). Adaptive disinhibitory gating by VIP interneurons permits associative learning. Nat. Neurosci. 22: 1834–1843, https://doi.org/10.1038/s41593-019-0508-y.Search in Google Scholar PubMed

Kumaran, D. (2012). What representations and computations underpin the contribution of the hippocampus to generalization and inference? Front. Hum. Neurosci. 6: 157, https://doi.org/10.3389/fnhum.2012.00157.Search in Google Scholar PubMed PubMed Central

Kumaran, D., Hassabis, D., and McClelland, J.L. (2016). What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends Cognit. Sci. 20: 512–534, https://doi.org/10.1016/j.tics.2016.05.004.Search in Google Scholar PubMed

Kutter, E.F., Bostroem, J., Elger, C.E., Mormann, F., and Nieder, A. (2018). Single neurons in the human brain encode numbers. Neuron 100: 753.e4–761.e4, https://doi.org/10.1016/j.neuron.2018.08.036.Search in Google Scholar PubMed

Langston, R.F., Ainge, J.A., Couey, J.J., Canto, C.B., Bjerknes, T.L., Witter, M.P., Moser, E.I., and Moser, M.B. (2010). Development of the spatial representation system in the rat. Science 328: 1576–1580, https://doi.org/10.1126/science.1188210.Search in Google Scholar PubMed

Lanore, F., Cayco-Gajic, N.A., Gurnani, H., Coyle, D., and Silver, R.A. (2021). Cerebellar granule cell axons support high-dimensional representations. Nat. Neurosci. 24: 1142–1150, https://doi.org/10.1038/s41593-021-00873-x.Search in Google Scholar PubMed PubMed Central

Lefort, S., Tomm, C., Floyd Sarria, J.C., and Petersen, C.C.H. (2009). The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61: 301–316, https://doi.org/10.1016/j.neuron.2008.12.020.Search in Google Scholar PubMed

Lehtelä, L., Salmelin, R., and Hari, R. (1997). Evidence for reactive magnetic 10-Hz rhythm in the human auditory cortex. Neurosci. Lett. 222: 111–114, https://doi.org/10.1016/s0304-3940(97)13361-4.Search in Google Scholar PubMed

Leutgeb, J.K., Leutgeb, S., Treves, A., Meyer, R., Barnes, C.A., McNaughton, B.L., Moser, M.B., and Moser, E.I. (2005). Progressive transformation of hippocampal neuronal representations in “morphed” environments. Neuron 48: 345–358, https://doi.org/10.1016/j.neuron.2005.09.007.Search in Google Scholar PubMed

Lever, C., Burton, S., Jeewajee, A., O’Keefe, J., and Burgess, N. (2009). Boundary vector cells in the subiculum of the hippocampal formation. J. Neurosci. 29: 9771–9777, https://doi.org/10.1523/jneurosci.1319-09.2009.Search in Google Scholar

Lever, C., Wills, T., Cacucci, F., Burgess, N., and O’Keefe, J. (2002). Long-term plasticity in hippocampal place-cell representation of environmental geometry. Nature 416: 90–94, https://doi.org/10.1038/416090a.Search in Google Scholar PubMed

Li, X., Ouyang, G., Usami, A., Ikegaya, Y., and Sik, A. (2010). Scale-free topology of the CA3 hippocampal network: a novel method to analyze functional neuronal assemblies. Biophys. J. 98: 1733–1741, https://doi.org/10.1016/j.bpj.2010.01.013.Search in Google Scholar PubMed PubMed Central

Li, Z. and Hoiem, D. (2018). Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40: 2935–2947, https://doi.org/10.1109/tpami.2017.2773081.Search in Google Scholar PubMed

Liu, Y., Mattar, M.G., Behrens, T.E.J., Daw, N.D., and Dolan, R.J. (2021). Experience replay is associated with efficient nonlocal learning. Science 372, https://doi.org/10.1126/science.abf1357.Search in Google Scholar PubMed PubMed Central

London, M. and Häusser, M. (2005). Dendritic computation. Annu. Rev. Neurosci. 28: 503–532, https://doi.org/10.1146/annurev.neuro.28.061604.135703.Search in Google Scholar PubMed

Latchoumane, C.F.V., Ngo, H.V., Born, J., and Shin, H.S. (2017). Thalamic spindles promote memory formation during sleep through triple phase-locking of cortical, thalamic, and hippocampal rhythms. Neuron 95: 424.e6–435.e6, https://doi.org/10.1016/j.neuron.2017.06.025.Search in Google Scholar PubMed

Ma, R., Miao, J., Niu, L., and Zhang, P. (2019). Transformed ℓ1 regularization for learning sparse deep neural networks. Neural Network 119: 286–298, https://doi.org/10.1016/j.neunet.2019.08.015.Search in Google Scholar PubMed

Mander, B.A., Winer, J.R., and Walker, M.P. (2017). Sleep and human aging. Neuron 94: 19–36, https://doi.org/10.1016/j.neuron.2017.02.004.Search in Google Scholar PubMed PubMed Central

Mandler, G. and Shebo, B.J. (1982). Subitizing: an analysis of its component processes. J. Exp. Psychol. Gen. 111: 1–22, https://doi.org/10.1037/0096-3445.111.1.1.Search in Google Scholar

Mathis, A., Herz, A.V.M., and Stemmler, M. (2012). Optimal population codes for space: grid cells outperform place cells. Neural Comput. 24: 2280–2317, https://doi.org/10.1162/neco_a_00319.Search in Google Scholar

McCloskey, M. and Cohen, N.J. (1989). Catastrophic interference in connectionist networks: the sequential learning problem. Psychol. Learn. Motiv. Adv. Res. Theor. 24: 109–165.10.1016/S0079-7421(08)60536-8Search in Google Scholar

Mccrink, K. and Wynn, K. (2004). Large-number addition and subtraction by 9-month-old infants. Psychol. Sci. 15: 776–781, https://doi.org/10.1111/j.0956-7976.2004.00755.x.Search in Google Scholar PubMed

Michon, F., Sun, J.J., Kim, C.Y., Ciliberti, D., and Kloosterman, F. (2019). Post-learning hippocampal replay selectively reinforces spatial memory for highly rewarded locations. Curr. Biol. 29: 1436.e5–1444.e5, https://doi.org/10.1016/j.cub.2019.03.048.Search in Google Scholar PubMed

Mikutta, C., Feige, B., Maier, J.G., Hertenstein, E., Holz, J., Riemann, D., and Nissen, C. (2019). Phase-amplitude coupling of sleep slow oscillatory and spindle activity correlates with overnight memory consolidation. J. Sleep Res. 28: e12835, https://doi.org/10.1111/jsr.12835.Search in Google Scholar PubMed

Mindell, J.A., Sadeh, A., Wiegand, B., How, T.H., and Goh, D.Y.T. (2010). Cross-cultural differences in infant and toddler sleep. Sleep Med. 11: 274–280, https://doi.org/10.1016/j.sleep.2009.04.012.Search in Google Scholar PubMed

Mitsuno, K., Miyao, J., and Kurita, T. (2020). Hierarchical group sparse regularization for deep convolutional neural networks; hierarchical group sparse regularization for deep convolutional neural networks. In: 2020 international joint conference on neural networks (IJCNN).10.1109/IJCNN48605.2020.9207531Search in Google Scholar

Miyawaki, H. and Mizuseki, K. (2022). De novo inter-regional coactivations of preconfigured local ensembles support memory. Nat. Commun. 11: 1272, https://doi.org/10.1038/s41467-022-28929-x.Search in Google Scholar PubMed PubMed Central

Mölle, M., Bergmann, T.O., Marshall, L., and Born, J. (2011). Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep 34: 1411–1421, https://doi.org/10.5665/sleep.1290.Search in Google Scholar PubMed PubMed Central

Moser, E.I., Kropff, E. and Moser, M.B. (2008). Place cells, grid cells, and the Brain’s spatial representation system. Annu. Rev. Neurosci. 31: 69–89, https://doi.org/10.1146/annurev.neuro.31.061307.090723.Search in Google Scholar PubMed

Muller, R.U. and Kubie, J.L. (1987). The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J. Neurosci. 7: 1951–1968, https://doi.org/10.1523/jneurosci.07-07-01951.1987.Search in Google Scholar PubMed PubMed Central

Nakazawa, K., McHugh, T.J., Wilson, M.A., and Tonegawa, S. (2004). NMDA receptors, place cells and hippocampal spatial memory. Nat. Rev. Neurosci. 5: 361–372, https://doi.org/10.1038/nrn1385.Search in Google Scholar PubMed

Nasr, K., Viswanathan, P., and Nieder, A. (2019). Number detectors spontaneously emerge in a deep neural network designed for visual object recognition. Sci. Adv. 5: eaav7903, https://doi.org/10.1126/sciadv.aav7903.Search in Google Scholar PubMed PubMed Central

Navarro-Lobato, I. and Genzel, L. (2019). The up and down of sleep: from molecules to electrophysiology. Neurobiol. Learn. Mem. 160: 3–10, https://doi.org/10.1016/j.nlm.2018.03.013.Search in Google Scholar PubMed

Ngo, C.T., Benear, S.L., Popal, H., Olson, I.R., and Newcombe, N.S. (2021). Contingency of semantic generalization on episodic specificity varies across development. Curr. Biol. 31: 2690.e5–2697.e5, https://doi.org/10.1016/j.cub.2021.03.088.Search in Google Scholar PubMed PubMed Central

Nieder, A. (2016). The neuronal code for number. Nat. Rev. Neurosci. 17: 366–382, https://doi.org/10.1038/nrn.2016.40.Search in Google Scholar PubMed

Nieder, A. (2021). Neuroethology of number sense across the animal kingdom. J. Exp. Biol. 224: 244764, https://doi.org/10.1242/jeb.218289.Search in Google Scholar PubMed

Nieder, A. and Dehaene, S. (2009). Representation of number in the brain. Annu. Rev. Neurosci. 32: 185–208, https://doi.org/10.1146/annurev.neuro.051508.135550.Search in Google Scholar PubMed

Nieder, A. and Miller, E.K. (2004). A parieto-frontal network for visual numerical information in the monkey. Proc. Natl. Acad. Sci. U. S. A. 101: 7457–7462, https://doi.org/10.1073/pnas.0402239101.Search in Google Scholar PubMed PubMed Central

Norimoto, H., Makino, K., Gao, M., Shikano, Y., Okamoto, K., Ishikawa, T., Sasaki, T., Hioki, H., Fujisawa, S., and Ikegaya, Y. (2018). Hippocampal ripples down-regulate synapses. Science 359: 1524–1527, https://doi.org/10.1126/science.aao0702.Search in Google Scholar PubMed

O’Keefe, J. and Burgess, N. (1996). Geometric determinants of the place fields of hippocampal neurons. Nature 381: 425–428, https://doi.org/10.1038/381425a0.Search in Google Scholar PubMed

O’Keefe, J. and Recce, M.L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3: 317–330, https://doi.org/10.1002/hipo.450030307.Search in Google Scholar PubMed

Oby, E.R., Golub, M.D., Hennig, J.A., Degenhart, A.D., Tyler-Kabara, E.C., Yu, B.M., Chase, S.M., and Batista, A.P. (2019). New neural activity patterns emerge with long-term learning. Proc. Natl. Acad. Sci. U. S. A. 116: 15210–15215, https://doi.org/10.1073/pnas.1820296116.Search in Google Scholar PubMed PubMed Central

Ohki, T. (2022). Measuring phase-amplitude coupling between neural oscillations of different frequencies via the Wasserstein distance. J. Neurosci. Methods 374: 109578, https://doi.org/10.1016/j.jneumeth.2022.109578.Search in Google Scholar PubMed

Ohki, T., Gunji, A., Takei, Y., Takahashi, H., Kaneko, Y., Kita, Y., Hironaga, N., Tobimatsu, S., Kamio, Y., Hanakawa, T., et al.. (2016). Neural oscillations in the temporal pole for a temporally congruent audio-visual speech detection task. Sci. Rep. 6: 37973, https://doi.org/10.1038/srep37973.Search in Google Scholar PubMed PubMed Central

Ohki, T. and Takei, Y. (2018). Neural mechanisms of mental schema: a triplet of delta, low beta/spindle and ripple oscillations. Eur. J. Neurosci. 48: 2416–2430, https://doi.org/10.1111/ejn.13844.Search in Google Scholar PubMed

Oyanedel, C.N., Durán, E., Niethard, N., Inostroza, M., and Born, J. (2020). Temporal associations between sleep slow oscillations, spindles and ripples. Eur. J. Neurosci. 52: 4762–4778, https://doi.org/10.1111/ejn.14906.Search in Google Scholar PubMed

Palm, G., Knoblauch, A., Triesch, J., Parisi, G.I., Tani, J., Weber, C. and Wermter, S. (2018). Lifelong learning of spatiotemporal representations with Dual-Memory recurrent self-organization. Front. Neurorob. 12: 78, https://doi.org/10.3389/fnbot.2018.00078.Search in Google Scholar PubMed PubMed Central

Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., and Wermter, S. (2019). Continual lifelong learning with neural networks: a review. Neural Network 113: 54–71, https://doi.org/10.1016/j.neunet.2019.01.012.Search in Google Scholar PubMed

Patania, A., Selvaggi, P., Veronese, M., Dipasquale, O., Expert, P., and Petri, G. (2019). Topological gene expression networks recapitulate brain anatomy and function. Netw. Neurosci. 3: 744–762, https://doi.org/10.1162/netn_a_00094.Search in Google Scholar PubMed PubMed Central

Pavlides, C. and Winson, J. (1989). Influences of hippocampal place cell firing in the awake state on the activity of these cells during subsequent sleep episodes. J. Neurosci. 9: 2907–2918, https://doi.org/10.1523/jneurosci.09-08-02907.1989.Search in Google Scholar

Pica, P., Lemer, C., Izard, V., and Dehaene, S. (2004). Exact and approximate arithmetic in an Amazonian indigene group. Science 306: 499–503, https://doi.org/10.1126/science.1102085.Search in Google Scholar PubMed

Qasim, S.E., Fried, I., and Jacobs, J. (2021). Phase precession in the human hippocampus and entorhinal cortex. Cell 184: 3242.e10–3255.e10, https://doi.org/10.1016/j.cell.2021.04.017.Search in Google Scholar PubMed PubMed Central

Raichle, M.E. (2010). Two views of brain function. Trends Cognit. Sci. 14: 180–190, https://doi.org/10.1016/j.tics.2010.01.008.Search in Google Scholar PubMed

Rasmussen, M.A. and Bro, R. (2012). A tutorial on the Lasso approach to sparse modeling. Chemometr. Intell. Lab. Syst. 119: 21–31, https://doi.org/10.1016/j.chemolab.2012.10.003.Search in Google Scholar

Revkin, S.K., Piazza, M., Izard, V., Cohen, L., and Dehaene, S. (2008). Does subitizing reflect numerical estimation? Psychol. Sci. 19: 607–614, https://doi.org/10.1111/j.1467-9280.2008.02130.x.Search in Google Scholar PubMed

Robins, A. (1995). Catastrophic forgetting, rehearsal and pseudorehearsal. Connect. Sci. 7: 123–146, https://doi.org/10.1080/09540099550039318.Search in Google Scholar

Romano, D., Nicolau, M., Quintin, E.M., Mazaika, P.K., Lightbody, A.A., Cody Hazlett, H., Piven, J., Carlsson, G., and Reiss, A.L. (2014). Topological methods reveal high and low functioning neuro-phenotypes within fragile X syndrome. Hum. Brain Mapp. 35: 4904–4915, https://doi.org/10.1002/hbm.22521.Search in Google Scholar PubMed PubMed Central

Roscow, E.L., Chua, R., Costa, R.P., Jones, M.W., and Lepora, N. (2021). Learning offline: memory replay in biological and artificial reinforcement learning. Trends Neurosci. 44: 808–821, https://doi.org/10.1016/j.tins.2021.07.007.Search in Google Scholar PubMed

Rostami, B., Anisuzzaman, D.M., Wang, C., Gopalakrishnan, S., Niezgoda, J., and Yu, Z. (2021). Multiclass wound image classification using an ensemble deep CNN-based classifier. Comput. Biol. Med. 134: 104536, https://doi.org/10.1016/j.compbiomed.2021.104536.Search in Google Scholar PubMed

Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016). Progressive neural networks. arXiv Preprint, https://doi.org/10.48550/arXiv.1606.04671.Search in Google Scholar

Sadeh, A., Mindell, J.A., Luedtke, K., and Wiegand, B. (2009). Sleep and sleep ecology in the first 3 years: a web-based study. J. Sleep Res. 18: 60–73, https://doi.org/10.1111/j.1365-2869.2008.00699.x.Search in Google Scholar PubMed

Samanta, A., Alonso, A., and Genzel, L. (2020). Memory reactivations and consolidation: considering neuromodulators across wake and sleep. Curr. Opin. Physiol. 15: 120–127, https://doi.org/10.1016/j.cophys.2020.01.003.Search in Google Scholar

Sanders, H., Wilson, M.A., and Gershman, S.J. (2020). Hippocampal remapping as hidden state inference. Elife 9: 1–31, https://doi.org/10.7554/elife.51140.Search in Google Scholar PubMed PubMed Central

Sarel, A., Finkelstein, A., Las, L., and Ulanovsky, N. (2017). Vectorial representation of spatial goals in the hippocampus of bats. Science 355: 176–180, https://doi.org/10.1126/science.aak9589.Search in Google Scholar PubMed

Sawamura, H., Shima, K., and Tanji, J. (2002). Numerical representation for action in the parietal cortex of the monkey. Nature 415: 918–922, https://doi.org/10.1038/415918a.Search in Google Scholar PubMed

Sayer, R.J., Friedlander, M.J., and Redman, S.J. (1990). The time course and amplitude of EPSPs evoked at synapses between pairs of CA3/CAl neurons in the hippocampal slice. J. Neurosci. 70: 828–838.10.1523/JNEUROSCI.10-03-00826.1990Search in Google Scholar PubMed PubMed Central

Schilling, C., Gappa, L., Schredl, M., Streit, F., Treutlein, J., Frank, J., Deuschle, M., Meyer-Lindenberg, A., Rietschel, M., and Witt, S.H. (2018). Fast sleep spindle density is associated with rs4680 (Val108/158Met) genotype of catechol-O-methyltransferase (COMT). Sleep 41, https://doi.org/10.1093/sleep/zsy007.Search in Google Scholar PubMed

Sezgin, E., Sirrianni, J., and Linwood, S.L. (2022). Operationalizing and implementing pretrained, large artificial intelligence linguistic models in the US health care system: outlook of Generative Pretrained Transformer 3 (GPT-3) as a service model. JMIR Med. Inform. 10: e32875, https://doi.org/10.2196/32875.Search in Google Scholar PubMed PubMed Central

Shahbaba, B., Li, L., Agostinelli, F., Saraf, M., Cooper, K.W., Haghverdian, D., Elias, G.A., Baldi, P. and Fortin, N.J. (2022). Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events. Nat. Commun. 13: 787, https://doi.org/10.1038/s41467-022-28057-6.Search in Google Scholar PubMed PubMed Central

Sherry, D.F., Jacobs, L.F. and Gaulin, S.J.C. (1992). Spatial memory and adaptive specialization of the hippocampus. Trends Neurosci. 15: 298–303, https://doi.org/10.1016/0166-2236(92)90080-r.Search in Google Scholar PubMed

Shin, H., Lee, J.K., Kim, J., and Kim, Sk. (2017). Continual learning with deep generative replay. Adv. Neural Inf. Process. Syst. 30.Search in Google Scholar

Singh, G., Memoli, F., Ishkhanov, T., Sapiro, G., Carlsson, G. and Ringach, D.L. (2008). Topological analysis of population activity in visual cortex. J. Vis. 8: 11.1–1118, https://doi.org/10.1167/8.8.11.Search in Google Scholar PubMed PubMed Central

Sizemore, A.E., Phillips-Cremins, J.E., Ghrist, R., and Bassett, D.S. (2019). The importance of the whole: topological data analysis for the network neuroscientist. Netw. Neurosci. 3: 656–673, https://doi.org/10.1162/netn_a_00073.Search in Google Scholar PubMed PubMed Central

Song, S., Sjöström, P.J., Reigl, M., Nelson, S., and Chklovskii, D.B. (2005). Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3: e68, https://doi.org/10.1371/journal.pbio.0030068.Search in Google Scholar PubMed PubMed Central

Stevenson, R.F., Zheng, J., Mnatsakanyan, L., Vadera, S., Knight, R.T., Lin, J.J. and Yassa, M.A. (2018). Hippocampal CA1 gamma power predicts the precision of spatial memory judgments. Proc. Natl. Acad. Sci. U. S. A. 115: 10148–10153, https://doi.org/10.1073/pnas.1805724115.Search in Google Scholar PubMed PubMed Central

Stoianov, I. and Zorzi, M. (2012). Emergence of a “visual number sense” in hierarchical generative models. Nat. Neurosci. 15: 194–196, https://doi.org/10.1038/nn.2996.Search in Google Scholar PubMed

Stolz, B.J., Emerson, T., Nahkuri, S., Porter, M.A., and Harrington, H.A. (2021). Topological data analysis of task-based fMRI data from experiments on schizophrenia. J. Phys. Complex. 2: 035006, https://doi.org/10.1088/2632-072x/abb4c6.Search in Google Scholar

Strubell, E., Ganesh, A., and McCallum, A. (2020). Energy and policy considerations for modern deep learning research. AAAI 34: 13693–13696, https://doi.org/10.1609/aaai.v34i09.7123.Search in Google Scholar

Sunaga, M., Takei, Y., Kato, Y., Tagawa, M., Suto, T., Hironaga, N., Ohki, T., Takahashi, Y., Fujihara, K., Sakurai, N., et al.. (2020). Frequency-specific resting connectome in bipolar disorder: an MEG study. Front. Psychiatr. 11: 597, https://doi.org/10.3389/fpsyt.2020.00597.Search in Google Scholar PubMed PubMed Central

Tagawa, M., Takei, Y., Kato, Y., Suto, T., Hironaga, N., Ohki, T., Takahashi, Y., Fujihara, K., Sakurai, N., Ujita, K., et al.. (2022). Disrupted local beta band networks in schizophrenia revealed through graph analysis: a magnetoencephalography study. Psychiatr. Clin. Neurosci. 76: 309–320, https://doi.org/10.1111/pcn.13362.Search in Google Scholar PubMed

Takahashi, N., Sasaki, T., Matsumoto, W., Matsuki, N., and Ikegaya, Y. (2010). Circuit topology for synchronizing neurons in spontaneously active networks. Proc. Natl. Acad. Sci. U. S. A. 107: 10244–10249, https://doi.org/10.1073/pnas.0914594107.Search in Google Scholar PubMed PubMed Central

Tenenbaum, J.B., Kemp, C., Griffiths, T.L., and Goodman, N.D. (2011). How to grow a mind: statistics, structure, and abstraction. Science 331: 1279–1285, https://doi.org/10.1126/science.1192788.Search in Google Scholar PubMed

Terada, S., Sakurai, Y., Nakahara, H., and Fujisawa, S. (2017). Temporal and rate coding for discrete event sequences in the hippocampus. Neuron 94: 1248.e4–1262.e4, https://doi.org/10.1016/j.neuron.2017.05.024.Search in Google Scholar PubMed

Tingley, D. and Peyrache, A. (2020). On the methods for reactivation and replay analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375: 20190231, https://doi.org/10.1098/rstb.2019.0231.Search in Google Scholar PubMed PubMed Central

Tonolini, F., Jensen, B.S., and Murray-Smith, R. (2020). Variational sparse coding. In: Proceedings of the 35th uncertainty in artificial intelligence conference. PMLR 115, pp. 690–700.Search in Google Scholar

Tononi, G. and Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81: 12–34, https://doi.org/10.1016/j.neuron.2013.12.025.Search in Google Scholar PubMed PubMed Central

Topaz, C.M., Ziegelmeier, L., and Halverson, T. (2015). Topological data analysis of biological aggregation models. PLoS One 10: e0126383, https://doi.org/10.1371/journal.pone.0126383.Search in Google Scholar PubMed PubMed Central

Tse, D., Langston, R.F., Kakeyama, M., Bethus, I., Spooner, P.A., Wood, E.R., Witter, M.P., and Morris, R.G.M. (2007). Schemas and memory consolidation. Science 316: 76–82, https://doi.org/10.1126/science.1135935.Search in Google Scholar PubMed

Vaidya, A.R., Jones, H.M., Castillo, J., and Badre, D. (2021). Neural representation of abstract task structure during generalization. Elife 10, https://doi.org/10.7554/elife.63226.Search in Google Scholar PubMed PubMed Central

van de Ven, G.M., Siegelmann, H.T., and Tolias, A.S. (2020). Brain-inspired replay for continual learning with artificial neural networks. Nat. Commun. 11: 4069, https://doi.org/10.1038/s41467-020-17866-2.Search in Google Scholar PubMed PubMed Central

van der Meer, M.A.A., Kemere, C., and Diba, K. (2020). Progress and issues in second-order analysis of hippocampal replay. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375: 20190238, https://doi.org/10.1098/rstb.2019.0238.Search in Google Scholar PubMed PubMed Central

Wallenstein, G.V., Eichenbaum, H., and Hasselmo, M.E. (1998). The hippocampus as an associator of discontiguous events. Trends Neurosci. 21: 317–323, https://doi.org/10.1016/s0166-2236(97)01220-4.Search in Google Scholar PubMed

Walker, M.P. and Stickgold, R. (2004). Sleep-dependent learning and memory consolidation. Neuron 44: 121–133, https://doi.org/10.1016/j.neuron.2004.08.031.Search in Google Scholar PubMed

Wang, L., Lei, B., Li, Q., Su, H., Zhu, J., and Zhong, Y. (2022). Triple-memory networks: a brain-inspired method for continual learning. IEEE Trans. Neural Network Learn. Syst. 33: 1925–1934, https://doi.org/10.1109/tnnls.2021.3111019.Search in Google Scholar

Wei, X.X., Prentice, J., and Balasubramanian, V. (2015). A principle of economy predicts the functional architecture of grid cells. Elife 4: e08362, https://doi.org/10.7554/elife.08362.Search in Google Scholar

Wikenheiser, A.M. and Redish, A.D. (2015). Hippocampal theta sequences reflect current goals. Nat. Neurosci. 18: 289–294, https://doi.org/10.1038/nn.3909.Search in Google Scholar PubMed PubMed Central

Wills, T.J., Cacucci, F., Burgess, N., and O’Keefe, J. (2010). Development of the hippocampal cognitive map in preweanling rats. Science 328: 1573–1576, https://doi.org/10.1126/science.1188224.Search in Google Scholar PubMed PubMed Central

Wittkuhn, L., Chien, S., Hall-McMaster, S., and Schuck, N.W. (2021). Replay in minds and machines. Neurosci. Biobehav. Rev. 129: 367–388, https://doi.org/10.1016/j.neubiorev.2021.08.002.Search in Google Scholar PubMed

Xu, M., Shen, Y., Zhang, S., Lu, Y., Zhao, D., Tenenbaum, J.B., and Gan, C. (2022). Prompting decision transformer for few-shot policy generalization. In: Proceedings of the 39th International conference on machine learning. PMLR 162, pp. 24631–24645.Search in Google Scholar

Yaguchi, A., Suzuki, T., Asano, W., Nitta, S., Sakata, Y. and Tanizawa, A. (2018). Adam induces implicit weight sparsity in rectifier neural networks. Proceedings ICMLA 2018: 318–325.10.1109/ICMLA.2018.00054Search in Google Scholar

Yamaguchi, M. (2010). Understanding mathematics. Chikumashobo, Tokyo.Search in Google Scholar

Zeithamova, D. and Bowman, C.R. (2020). Generalization and the hippocampus: more than one story? Neurobiol. Learn. Mem. 175: 107317, https://doi.org/10.1016/j.nlm.2020.107317.Search in Google Scholar PubMed PubMed Central

Zeki, S. (1999). Splendours and miseries of the brain. Philos. Trans. R. Soc. Lond. B Biol. Sci. 354: 2053–2065, https://doi.org/10.1098/rstb.1999.0543.Search in Google Scholar PubMed PubMed Central

Zeng, H. and Sanes, J.R. (2017). Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18: 530–546, https://doi.org/10.1038/nrn.2017.85.Search in Google Scholar PubMed

Zhang, D. and Raichle, M.E. (2010). Disease and the brain’s dark energy. Nat. Rev. Neurol. 6: 15–28, https://doi.org/10.1038/nrneurol.2009.198.Search in Google Scholar PubMed