{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T02:39:20Z","timestamp":1779331160173,"version":"3.51.4"},"reference-count":26,"publisher":"MIT Press","issue":"8","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some \u201csilent\u201d mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.<\/jats:p>","DOI":"10.1162\/jocn_a_01569","type":"journal-article","created":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T10:25:36Z","timestamp":1588847136000},"page":"1455-1465","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":10,"title":["A Geometric Characterization of Population Coding in the Prefrontal Cortex and Hippocampus during a Paired-Associate Learning Task"],"prefix":"10.1162","volume":"32","author":[{"given":"Yue","family":"Liu","sequence":"first","affiliation":[{"name":"Boston University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott L.","family":"Brincat","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Earl K.","family":"Miller","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael E.","family":"Hasselmo","sequence":"additional","affiliation":[{"name":"Boston University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2020,8,1]]},"reference":[{"key":"2022042815224120300_bib1","doi-asserted-by":"crossref","unstructured":"Bernardi,  S., Benna,  M. K., Rigotti,  M., Munuera,  J., Fusi,  S., & Salzman,  C. D. (2019). The geometry of abstraction in hippocampus and pre-frontal cortex. bioRxiv. https:\/\/doi.org\/10.1101\/408633.","DOI":"10.1101\/408633"},{"key":"2022042815224120300_bib2","doi-asserted-by":"crossref","unstructured":"Bi,  G. Q., & Poo,  M. M. (1998). Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience, 18, 10464\u201310472.","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998"},{"key":"2022042815224120300_bib3","doi-asserted-by":"crossref","unstructured":"Brincat,  S. L., & Miller,  E. K. (2015). Frequency-specific hippocampal\u2013prefrontal interactions during associative learning. Nature Neuroscience, 18, 576\u2013581.","DOI":"10.1038\/nn.3954"},{"key":"2022042815224120300_bib4","doi-asserted-by":"crossref","unstructured":"Brincat,  S. L., & Miller,  E. K. (2016). Prefrontal cortex networks shift from external to internal modes during learning. Journal of Neuroscience, 36, 9739\u20139754.","DOI":"10.1523\/JNEUROSCI.0274-16.2016"},{"key":"2022042815224120300_bib5","doi-asserted-by":"crossref","unstructured":"Buonomano,  D. V., & Maass,  W. (2009). State-dependent computations: Spatiotemporal processing in cortical networks. Nature Reviews Neuroscience, 10, 113\u2013125.","DOI":"10.1038\/nrn2558"},{"key":"2022042815224120300_bib6","doi-asserted-by":"crossref","unstructured":"Chaudhuri,  R., Ger\u00e7ek,  B., Pandey,  B., Peyrache,  A., & Fiete,  I. (2019). The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nature Neuroscience, 22, 1512\u20131520.","DOI":"10.1038\/s41593-019-0460-x"},{"key":"2022042815224120300_bib7","doi-asserted-by":"crossref","unstructured":"DiCarlo,  J. J., & Cox,  D. D. (2007). Untangling invariant object recognition. Trends in Cognitive Sciences, 11, 333\u2013341.","DOI":"10.1016\/j.tics.2007.06.010"},{"key":"2022042815224120300_bib8","doi-asserted-by":"crossref","unstructured":"Durstewitz,  D., Vittoz,  N. M., Floresco,  S. B., & Seamans,  J. K. (2010). Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron, 66, 438\u2013448.","DOI":"10.1016\/j.neuron.2010.03.029"},{"key":"2022042815224120300_bib9","doi-asserted-by":"crossref","unstructured":"Eichenbaum,  H.\n           (2017). On the integration of space, time, and memory. Neuron, 95, 1007\u20131018.","DOI":"10.1016\/j.neuron.2017.06.036"},{"key":"2022042815224120300_bib10","doi-asserted-by":"crossref","unstructured":"Freedman,  D. J., Riesenhuber,  M., Poggio,  T., & Miller,  E. K. (2001). Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291, 312\u2013316.","DOI":"10.1126\/science.291.5502.312"},{"key":"2022042815224120300_bib11","doi-asserted-by":"crossref","unstructured":"Golub,  M. D., Sadtler,  P. T., Oby,  E. R., Quick,  K. M., Ryu,  S. I., Tyler-Kabara,  E. C., et al (2018). Learning by neural reassociation. Nature Neuroscience, 21, 607\u2013616.","DOI":"10.1038\/s41593-018-0095-3"},{"key":"2022042815224120300_bib12","doi-asserted-by":"crossref","unstructured":"Mante,  V., Sussillo,  D., Shenoy,  K. V., & Newsome,  W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503, 78\u201384.","DOI":"10.1038\/nature12742"},{"key":"2022042815224120300_bib13","doi-asserted-by":"crossref","unstructured":"Maris,  E., & Oostenveld,  R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164, 177\u2013190.","DOI":"10.1016\/j.jneumeth.2007.03.024"},{"key":"2022042815224120300_bib14","doi-asserted-by":"crossref","unstructured":"McKenzie,  S., Frank,  A. J., Kinsky,  N. R., Porter,  B., Rivi\u00e8re,  P. D., & Eichenbaum,  H. (2014). Hippocampal representation of related and opposing memories develop within distinct, hierarchically organized neural schemas. Neuron, 83, 202\u2013215.","DOI":"10.1016\/j.neuron.2014.05.019"},{"key":"2022042815224120300_bib15","doi-asserted-by":"crossref","unstructured":"Mongillo,  G., Barak,  O., & Tsodyks,  M. (2008). Synaptic theory of working memory. Science, 319, 1543\u20131546.","DOI":"10.1126\/science.1150769"},{"key":"2022042815224120300_bib16","doi-asserted-by":"crossref","unstructured":"Murray,  J. D., Bernacchia,  A., Freedman,  D. J., Romo,  R., Wallis,  J. D., Cai,  X., et al (2014). A hierarchy of intrinsic timescales across primate cortex. 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Nature, 512, 423\u2013426.","DOI":"10.1038\/nature13665"},{"key":"2022042815224120300_bib20","doi-asserted-by":"crossref","unstructured":"Sakai,  K., & Miyashita,  Y. (1991). Neural organization for the long-term memory of paired associates. Nature, 354, 152\u2013155.","DOI":"10.1038\/354152a0"},{"key":"2022042815224120300_bib21","doi-asserted-by":"crossref","unstructured":"Schapiro,  A. C., Turk-Browne,  N. B., Norman,  K. A., & Botvinick,  M. M. (2016). Statistical learning of temporal community structure in the hippocampus. Hippocampus, 26, 3\u20138.","DOI":"10.1002\/hipo.22523"},{"key":"2022042815224120300_bib22","doi-asserted-by":"crossref","unstructured":"Stachenfeld,  K. L., Botvinick,  M. M., & Gershman,  S. J. (2017). The hippocampus as a predictive map. Nature Neuroscience, 20, 1643\u20131653.","DOI":"10.1038\/nn.4650"},{"key":"2022042815224120300_bib23","doi-asserted-by":"crossref","unstructured":"Stokes,  M. G.\n           (2015). \u2018Activity-silent\u2019 working memory in prefrontal cortex: A dynamic coding framework. Trends in Cognitive Sciences, 19, 394\u2013405.","DOI":"10.1016\/j.tics.2015.05.004"},{"key":"2022042815224120300_bib24","doi-asserted-by":"crossref","unstructured":"Sussillo,  D.\n           (2014). Neural circuits as computational dynamical systems. Current Opinion in Neurobiology, 25, 156\u2013163.","DOI":"10.1016\/j.conb.2014.01.008"},{"key":"2022042815224120300_bib25","doi-asserted-by":"crossref","unstructured":"Suzuki,  W.\n           (2007). Integrating associative learning signals across the brain. Hippocampus, 17, 842\u2013850.","DOI":"10.1002\/hipo.20321"},{"key":"2022042815224120300_bib26","doi-asserted-by":"crossref","unstructured":"Vyas,  S., Even-Chen,  N., Stavisky,  S. D., Ryu,  S. I., Nuyujukian,  P., & Shenoy,  K. V. (2018). Neural population dynamics underlying motor learning transfer. 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