Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks

[EN] Hippocampal firing is organized in theta sequences controlled by internal memory processes and by external sensory cues, but how these computations are coordinated is not fully understood. Although theta activity is commonly studied as a unique coherent oscillation, it is the result of complex interactions between different rhythm generators. Here, by separating hippocampal theta activity in three different current generators, we found epochs with variable theta frequency and phase coupling, suggesting flexible interactions between theta generators. We found that epochs of highly synchronized theta rhythmicity preferentially occurred during behavioral tasks requiring coordination between internal memory representations and incoming sensory information. In addition, we found that gamma oscillations were associated with specific theta generators and the strength of theta-gamma coupling predicted the synchronization between theta generators. We propose a mechanism for segregating or integrating hippocampal computations based on the flexible coordination of different theta frameworks to accommodate the cognitive needs.European Regional Development Fund BFU2015-64380-C2-1-R Santiago Canals European Regional Development Fund BFU2015-64380-C2-2-R David Moratal European Regional Development Fund PGC2018-101055-B-I00 Santiago Canals Horizon 2020 Framework Programme 668863 (SyBil-AA) Santiago Canals Agencia Estatal de Investigacion SEV-2017-0723 Santiago Canals Ministerio de Economia y Competitividad TEC2016-80063-C3-3-R Claudio R Mirasso Ministerio de Economia y Competitividad TEC2016-80063-C3-2-R Ernesto Pereda Agencia Estatal de Investigacion MDM-2017-0711 Claudio R Mirasso Ministerio de Economi ' a y Competitividad SAF2016-80100-R Oscar Herreras The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.López-Madrona, VJ.; Pérez-Montoyo, E.; Alvarez-Salvado, E.; Moratal, D.; Herreras, O.; Pereda, E.; Mirasso, CR.... (2020). Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks. eLife. 9:1-35. https://doi.org/10.7554/eLife.57313S1359Ahmed, O. J., & Mehta, M. R. (2012). Running Speed Alters the Frequency of Hippocampal Gamma Oscillations. Journal of Neuroscience, 32(21), 7373-7383. doi:10.1523/jneurosci.5110-11.2012Ainge, J. A., van der Meer, M. A. A., Langston, R. F., & Wood, E. R. (2007). Exploring the role of context-dependent hippocampal activity in spatial alternation behavior. Hippocampus, 17(10), 988-1002. doi:10.1002/hipo.20301Alonso, A., & García-Austt, E. (1987). Neuronal sources of theta rhythm in the entorhinal cortex of the rat. Experimental Brain Research, 67(3), 502-509. doi:10.1007/bf00247283Álvarez-Salvado, E., Pallarés, V., Moreno, A., & Canals, S. (2014). Functional MRI of long-term potentiation: imaging network plasticity. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1633), 20130152. doi:10.1098/rstb.2013.0152Amzica, F., & Steriade, M. (1998). Electrophysiological correlates of sleep delta waves. Electroencephalography and Clinical Neurophysiology, 107(2), 69-83. doi:10.1016/s0013-4694(98)00051-0Andersen, P., Holmqvist, B., & Voorhoeve, P. E. (1966). Entorhinal Activation of Dentate Granule Cells. Acta Physiologica Scandinavica, 66(4), 448-460. doi:10.1111/j.1748-1716.1966.tb03223.xBarnett, L., & Seth, A. K. (2011). Behaviour of Granger causality under filtering: Theoretical invariance and practical application. Journal of Neuroscience Methods, 201(2), 404-419. doi:10.1016/j.jneumeth.2011.08.010Barth, A. M., Domonkos, A., Fernandez-Ruiz, A., Freund, T. F., & Varga, V. (2018). Hippocampal Network Dynamics during Rearing Episodes. Cell Reports, 23(6), 1706-1715. doi:10.1016/j.celrep.2018.04.021Bell, A. J., & Sejnowski, T. J. (1995). An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7(6), 1129-1159. doi:10.1162/neco.1995.7.6.1129Belluscio, M. A., Mizuseki, K., Schmidt, R., Kempter, R., & Buzsaki, G. (2012). Cross-Frequency Phase-Phase Coupling between Theta and Gamma Oscillations in the Hippocampus. Journal of Neuroscience, 32(2), 423-435. doi:10.1523/jneurosci.4122-11.2012Benito, N., Fernández-Ruiz, A., Makarov, V. A., Makarova, J., Korovaichuk, A., & Herreras, O. (2013). Spatial Modules of Coherent Activity in Pathway-Specific LFPs in the Hippocampus Reflect Topology and Different Modes of Presynaptic Synchronization. Cerebral Cortex, 24(7), 1738-1752. doi:10.1093/cercor/bht022Bland, B. H., & Whishaw, I. Q. (1976). Generators and topography of hippocampal Theta (RSA) in the anaesthetized and freely moving rat. Brain Research, 118(2), 259-280. doi:10.1016/0006-8993(76)90711-3Bragin, A., Jando, G., Nadasdy, Z., Hetke, J., Wise, K., & Buzsaki, G. (1995). Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat. The Journal of Neuroscience, 15(1), 47-60. doi:10.1523/jneurosci.15-01-00047.1995Bruns, A., & Eckhorn, R. (2004). Task-related coupling from high- to low-frequency signals among visual cortical areas in human subdural recordings. International Journal of Psychophysiology, 51(2), 97-116. doi:10.1016/j.ijpsycho.2003.07.001Buzsáki, G. (2002). Theta Oscillations in the Hippocampus. Neuron, 33(3), 325-340. doi:10.1016/s0896-6273(02)00586-xBuzsáki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13(6), 407-420. doi:10.1038/nrn3241Buzsáki, G., & Draguhn, A. (2004). Neuronal Oscillations in Cortical Networks. Science, 304(5679), 1926-1929. doi:10.1126/science.1099745Buzsáki, G., & Moser, E. I. (2013). Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nature Neuroscience, 16(2), 130-138. doi:10.1038/nn.3304Cabral, H. O., Vinck, M., Fouquet, C., Pennartz, C. M. A., Rondi-Reig, L., & Battaglia, F. P. (2014). Oscillatory Dynamics and Place Field Maps Reflect Hippocampal Ensemble Processing of Sequence and Place Memory under NMDA Receptor Control. Neuron, 81(2), 402-415. doi:10.1016/j.neuron.2013.11.010Canals, S., Beyerlein, M., Merkle, H., & Logothetis, N. K. (2009). Functional MRI Evidence for LTP-Induced Neural Network Reorganization. Current Biology, 19(5), 398-403. doi:10.1016/j.cub.2009.01.037Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., … Knight, R. T. (2006). High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex. Science, 313(5793), 1626-1628. doi:10.1126/science.1128115Canolty, R. T., & Knight, R. T. (2010). The functional role of cross-frequency coupling. Trends in Cognitive Sciences, 14(11), 506-515. doi:10.1016/j.tics.2010.09.001Cardin, J. A., Carlén, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., … Moore, C. I. (2009). Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature, 459(7247), 663-667. doi:10.1038/nature08002Castellanos, N. P., & Makarov, V. A. (2006). Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods, 158(2), 300-312. doi:10.1016/j.jneumeth.2006.05.033Charpak, S., Paré, D., & Llinás, R. (1995). The Entorhinal Cortex Entrains Fast CA1 Hippocampal Oscillations in the Anaesthetized Guinea-pig: Role of the Monosynaptic Component of the Perforant Path. European Journal of Neuroscience, 7(7), 1548-1557. doi:10.1111/j.1460-9568.1995.tb01150.xChen A. 2006. Fast kernel density independent component analysis. Independent Component Analysis and Blind Signal Separation, Lecture Notes in Computer Science.Cohen, M. X. (2014). Analyzing Neural Time Series Data. doi:10.7551/mitpress/9609.001.0001Cole, S. R., & Voytek, B. (2017). Brain Oscillations and the Importance of Waveform Shape. Trends in Cognitive Sciences, 21(2), 137-149. doi:10.1016/j.tics.2016.12.008Cole, S., & Voytek, B. (2018). Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns. doi:10.1101/452987Cole, S., & Voytek, B. (2019). Cycle-by-cycle analysis of neural oscillations. Journal of Neurophysiology, 122(2), 849-861. doi:10.1152/jn.00273.2019Colgin, L. L., Denninger, T., Fyhn, M., Hafting, T., Bonnevie, T., Jensen, O., … Moser, E. I. (2009). Frequency of gamma oscillations routes flow of information in the hippocampus. Nature, 462(7271), 353-357. doi:10.1038/nature08573Colgin, L. L. (2013). Mechanisms and Functions of Theta Rhythms. Annual Review of Neuroscience, 36(1), 295-312. doi:10.1146/annurev-neuro-062012-170330Colgin, L. L. (2015). Theta–gamma coupling in the entorhinal–hippocampal system. Current Opinion in Neurobiology, 31, 45-50. doi:10.1016/j.conb.2014.08.001Colgin, L. L. (2016). Rhythms of the hippocampal network. Nature Reviews Neuroscience, 17(4), 239-249. doi:10.1038/nrn.2016.21Csicsvari, J., Hirase, H., Czurkó, A., Mamiya, A., & Buzsáki, G. (1999). Oscillatory Coupling of Hippocampal Pyramidal Cells and Interneurons in the Behaving Rat. The Journal of Neuroscience, 19(1), 274-287. doi:10.1523/jneurosci.19-01-00274.1999DeCoteau, W. E., Thorn, C., Gibson, D. J., Courtemanche, R., Mitra, P., Kubota, Y., & Graybiel, A. M. (2007). Learning-related coordination of striatal and hippocampal theta rhythms during acquisition of a procedural maze task. Proceedings of the National Academy of Sciences, 104(13), 5644-5649. doi:10.1073/pnas.0700818104Douchamps, V., Jeewajee, A., Blundell, P., Burgess, N., & Lever, C. (2013). Evidence for Encoding versus Retrieval Scheduling in the Hippocampus by Theta Phase and Acetylcholine. Journal of Neuroscience, 33(20), 8689-8704. doi:10.1523/jneurosci.4483-12.2013Dudai, Y., & Morris, R. G. M. (2013). Memorable Trends. Neuron, 80(3), 742-750. doi:10.1016/j.neuron.2013.09.039Dvorak, D., Radwan, B., Sparks, F. T., Talbot, Z. N., & Fenton, A. A. (2018). Control of recollection by slow gamma dominating mid-frequency gamma in hippocampus CA1. PLOS Biology, 16(1), e2003354. doi:10.1371/journal.pbio.2003354Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: Oscillations and synchrony in top–down processing. Nature Reviews Neuroscience, 2(10), 704-716. doi:10.1038/35094565Fell, J., & Axmacher, N. (2011). The role of phase synchronization in memory processes. Nature Reviews Neuroscience, 12(2), 105-118. doi:10.1038/nrn2979Fernandez-Ruiz, A., Makarov, V. A., Benito, N., & Herreras, O. (2012). Schaffer-Specific Local Field Potentials Reflect Discrete Excitatory Events at Gamma Frequency That May Fire Postsynaptic Hippocampal CA1 Units. Journal of Neuroscience, 32(15), 5165-5176. doi:10.1523/jneurosci.4499-11.2012Fernández-Ruiz, A., Makarov, V. A., & Herreras, O. (2012). Sustained increase of spontaneous input and spike transfer in the CA3-CA1 pathway following long-term potentiation in vivo. Frontiers in Neural Circuits, 6. doi:10.3389/fncir.2012.00071Fernández-Ruiz, A., Oliva, A., Nagy, G. A., Maurer, A. P., Berényi, A., & Buzsáki, G. (2017). Entorhinal-CA3 Dual-Input Control of Spike Timing in the Hippocampus by Theta-Gamma Coupling. Neuron, 93(5), 1213-1226.e5. doi:10.1016/j.neuron.2017.02.017Fernández-Ruiz, A., & Herreras, O. (2013). Identifying the synaptic origin of ongoing neuronal oscillations through spatial discrimination of electric fields. Frontiers in Computational Neuroscience, 7. doi:10.3389/fncom.2013.00005Freeman, J. A., & Nicholson, C. (1975). Experimental optimization of current source-density technique for anuran cerebellum. Journal of Neurophysiology, 38(2), 369-382. doi:10.1152/jn.1975.38.2.369Fries, P. (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences, 9(10), 474-480. doi:10.1016/j.tics.2005.08.011Fries, P. (2015). Rhythms for Cognition: Communication through Coherence. Neuron, 88(1), 220-235. doi:10.1016/j.neuron.2015.09.034Goutagny, R., Gu, N., Cavanagh, C., Jackson, J., Chabot, J.-G., Quirion, R., … Williams, S. (2013). Alterations in hippocampal network oscillations and theta-gamma coupling arise before Aβ overproduction in a mouse model of Alzheimer’s disease. European Journal of Neuroscience, 37(12), 1896-1902. doi:10.1111/ejn.12233Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424. doi:10.2307/1912791Green, K. F., & Rawlins, J. N. P. (1979). Hippocampal theta in rats under urethane: Generators and phase relations. Electroencephalography and Clinical Neurophysiology, 47(4), 420-429. doi:10.1016/0013-4694(79)90158-5Hasselmo, M. E., Bodelón, C., & Wyble, B. P. (2002). A Proposed Function for Hippocampal Theta Rhythm: Separate Phases of Encoding and Retrieval Enhance Reversal of Prior Learning. Neural Computation, 14(4), 793-817. doi:10.1162/089976602317318965Helfrich, R. F., Mander, B. A., Jagust, W. J., Knight, R. T., & Walker, M. P. (2018). Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting. Neuron, 97(1), 221-230.e4. doi:10.1016/j.neuron.2017.11.020Helfrich, R. F., Lendner, J. D., Mander, B. A., Guillen, H., Paff, M., Mnatsakanyan, L., … Knight, R. T. (2019). Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans. Nature Communications, 10(1). doi:10.1038/s41467-019-11444-xHerreras, O. (1990). Propagating dendritic action potential mediates synaptic transmission in CA1 pyramidal cells in situ. Journal of Neurophysiology, 64(5), 1429-1441. doi:10.1152/jn.1990.64.5.1429Herreras, O., Makarova, J., & Makarov, V. A. (2015). New uses of LFPs: Pathway-specific threads obtained through spatial discrimination. Neuroscience, 310, 486-503. doi:10.1016/j.neuroscience.2015.09.054Herreras, O. (2016). Local Field Potentials: Myths and Misunderstandings. Frontiers in Neural Circuits, 10. doi:10.3389/fncir.2016.00101Holsheimer, J. (1987). Electrical conductivity of the hippocampal CA1 layers and application to current-source-density analysis. Experimental Brain Research, 67(2). doi:10.1007/bf00248560Iaccarino, H. F., Singer, A. C., Martorell, A. J., Rudenko, A., Gao, F., Gillingham, T. Z., … Tsai, L.-H. (2016). Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature, 540(7632), 230-235. doi:10.1038/nature20587Igarashi, K. M., Lu, L., Colgin, L. L., Moser, M.-B., & Moser, E. I. (2014). Coordination of entorhinal–hippocampal ensemble activity during associative learning. Nature, 510(7503), 143-147. doi:10.1038/nature13162Jackson, J. C., Johnson, A., & Redish, A. D. (2006). Hippocampal Sharp Waves and Reactivation during Awake States Depend on Repeated Sequential Experience. Journal of Neuroscience, 26(48), 12415-12426. doi:10.1523/jneurosci.4118-06.2006Jiang, H., Bahramisharif, A., van Gerven, M. A. J., & Jensen, O. (2015). Measuring directionality between neuronal oscillations of different frequencies. NeuroImage, 118, 359-367. doi:10.1016/j.neuroimage.2015.05.044Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., … Vicente, R. (2015). Untangling cross-frequency coupling in neuroscience. Current Opinion in Neurobiology, 31, 51-61. doi:10.1016/j.conb.2014.08.002Klausberger, T., Magill, P. J., Márton, L. F., Roberts, J. D. B., Cobden, P. M., Buzsáki, G., & Somogyi, P. (2003). Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo. Nature, 421(6925), 844-848. doi:10.1038/nature01374Klausberger, T., & Somogyi, P. (2008). Neuronal Diversity and Temporal Dynamics: The Unity of Hippocampal Circuit Operations. Science, 321(5885), 53-57. doi:10.1126/science.1149381Kocsis, B., Bragin, A., & Buzsáki, G. (1999). Interdependence of Multiple Theta Generators in the Hippocampus: a Partial Coherence Analysis. The Journal of Neuroscience, 19(14), 6200-6212. doi:10.1523/jneurosci.19-14-06200.1999Korovaichuk, A., Makarova, J., Makarov, V. A., Benito, N., & Herreras, O. (2010). Minor Contribution of Principal Excitatory Pathways to Hippocampal LFPs in the Anesthetized Rat: A Combined Independent Component and Current Source Density Study. Journal of Neurophysiology, 104(1), 484-497. doi:10.1152/jn.00297.2010Kramer, M. A., Tort, A. B. L., & Kopell, N. J. (2008). Sharp edge artifacts and spurious coupling in EEG frequency comodulation measures. Journal of Neuroscience Methods, 170(2), 352-357. doi:10.1016/j.jneumeth.2008.01.020Kramis, R., Vanderwolf, C. H., & Bland, B. H. (1975). Two types of hippocampal rhythmical slow activity in both the rabbit and the rat: Relations to behavior and effects of atropine, diethyl ether, urethane, and pentobarbital. Experimental Neurology, 49(1), 58-85. doi:10.1016/0014-4886(75)90195-8Lakatos, P., Shah, A. S., Knuth, K. H., Ulbert, I., Karmos, G., & Schroeder, C. E. (2005). An Oscillatory Hierarchy Controlling Neuronal Excitability and Stimulus Processing in the Auditory Cortex. Journal of Neurophysiology, 94(3), 1904-1911. doi:10.1152/jn.00263.2005Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., & Schroeder, C. E. (2008). Entrainment of Neuronal Oscillations as a Mechanism of Attentional Selection. Science, 320(5872), 110-113. doi:10.1126/science.1154735Lasztóczi, B., & Klausberger, T. (2014). Layer-Specific GABAergic Control of Distinct Gamma Oscillations in the CA1 Hippocampus. Neuron, 81(5), 1126-1139. doi:10.1016/j.neuron.2014.01.021Lasztóczi, B., & Klausberger, T. (2016). Hippocampal Place Cells Couple to Three Different Gamma Oscillations during Place Field Traversal. Neuron, 91(1), 34-40. doi:10.1016/j.neuron.2016.05.036Łęski, S., Kublik, E., Świejkowski, D. A., Wróbel, A., & Wójcik, D. K. (2009). Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density. Journal of Computational Neuroscience, 29(3), 459-473. doi:10.1007/s10827-009-0203-1Lever, C., Burton, S., & Ο’Keefe, J. (2006). Rearing on Hind Legs, Environmental Novelty, and the Hippocampal Formation. Reviews in the Neurosciences, 17(1-2). doi:10.1515/revneuro.2006.17.1-2.111Lisman, J. E., & Idiart, M. A. P. (1995). Storage of 7 ± 2 Short-Term Memories in Oscillatory Subcycles. Science, 267(5203), 1512-1515. doi:10.1126/science.7878473Lisman, J. E., & Jensen, O. (2013). The Theta-Gamma Neural Code. Neuron, 77(6), 1002-1016. doi:10.1016/j.neuron.2013.03.007Lopes-dos-Santos, V., van de Ven, G. M., Morley, A., Trouche, S., Campo-Urriza, N., & Dupret, D. (2018). Parsing Hippocampal Theta Oscillations by Nested Spectral Components during Spatial Exploration and Memory-Guided Behavior. Neuron, 100(4), 940-952.e7. doi:10.1016/j.neuron.2018.09.031López-Aguado, L., Ibarz, J. ., & Herreras, O. (2001). Activity-dependent changes of tissue resistivity in the CA1 region in vivo are layer-specific: modulation of evoked potentials. Neuroscience, 108(2), 249-262. doi:10.1016/s0306-4522(01)00417-1Lozano-Soldevilla, D., ter Huurne, N., & Oostenveld, R. (2016). Neuronal Oscillations with Non-sinusoidal Morphology Produce Spurious Phase-to-Amplitude Coupling and Directionality. Frontiers in Computational Neuroscience, 10. doi:10.3389/fncom.2016.00087Makarov, V. A., Makarova, J., & Herreras, O. (2010). Disentanglement of local field potential sources by independent component analysis. Journal of Computational Neuroscience, 29(3), 445-457. doi:10.1007/s10827-009-0206-yMakarova, J. (2011). Parallel readout of pathway-specific inputs to laminated brain structures. Frontiers in Systems Neuroscience, 5. doi:10.3389/fnsys.2011.00077Martín-Vázquez, G., Makarova, J., Makarov, V. A., & Herreras, O. (2013). Determining the True Polarity and Amplitude of Synaptic Currents Underlying Gamma Oscillations of Local Field Potentials. PLoS ONE, 8(9), e75499. doi:10.1371/journal.pone.0075499Martín-Vázquez, G., Benito, N., Makarov, V. A., Herreras, O., & Makarova, J. (2015). Diversity of LFPs Activated in Different Target Regions by a Common CA3 Input. Cerebral Cortex, 26(10), 4082-4100. doi:10.1093/cercor/bhv211McNaughton, B. L., Barnes, C. A., & O’Keefe, J. (1983). The contributions of position, direction, and velocity to single unit activity in the hippocampus of freely-moving rats. Experimental Brain Research, 52(1). doi:10.1007/bf00237147Mitzdorf, U. (1985). Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiological Reviews, 65(1), 37-100. doi:10.1152/physrev.1985.65.1.37Mizuseki, K., Sirota, A., Pastalkova, E., & Buzsáki, G. (2009). Theta Oscillations Provide Temporal Windows for Local Circuit Computation in the Entorhinal-Hippocampal Loop. Neuron, 64(2), 267-280. doi:10.1016/j.neuron.2009.08.037Mizuseki, K., & Buzsaki, G. (2014). Theta oscillations decrease spike synchrony in the hippocampus and entorhinal cortex. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1635), 20120530. doi:10.1098/rstb.2012.0530Montgomery, S. M., Betancur, M. I., & Buzsaki, G. (2009). Behavior-Dependent Coordination of Multiple Theta Dipoles in the Hippocampus. Journal of Neuroscience, 29(5), 1381-1394. doi:10.1523/jneurosci.4339-08.2009Montgomery, S. M., & Buzsaki, G. (2007). Gamma oscillations dynamically couple hippocampal CA3 and CA1 regions during memory task performance. Proceedings of the National Academy of Sciences, 104(36), 14495-14500. doi:10.1073/pnas.0701826104Moreno, A., Morris, R. G. M., & Canals, S. (2015). Frequency-Dependent Gating of Hippocampal–Neocortical Interactions. Cerebral Cortex, 26(5), 2105-2114. doi:10.1093/cercor/bhv033Mormann, F., Fell, J., Axmacher, N., Weber, B., Lehnertz, K., Elger, C. E., & Fernández, G. (2005). Phase/amplitude reset and theta-gamma interaction in the human medial temporal lobe during a continuous word recognition memory task. Hippocampus, 15(7), 890-900. doi:10.1002/hipo.20117Neym

Characterization of the Biosynthesis, Processing and Kinetic Mechanism of Action of the Enzyme Deficient in Mucopolysaccharidosis IIIC

Heparin acetyl-CoA:alpha-glucosaminide N-acetyltransferase (N-acetyltransferase, EC 2.3.1.78) is an integral lysosomal membrane protein containing 11 transmembrane domains, encoded by the HGSNAT gene. Deficiencies of N-acetyltransferase lead to mucopolysaccharidosis IIIC. We demonstrate that contrary to a previous report, the N-acetyltransferase signal peptide is co-translationally cleaved and that this event is required for its intracellular transport to the lysosome. While we confirm that the N-acetyltransferase precursor polypeptide is processed in the lysosome into a small amino-terminal alpha- and a larger ß- chain, we further characterize this event by identifying the mature amino-terminus of each chain. We also demonstrate this processing step(s) is not, as previously reported, needed to produce a functional transferase, i.e., the precursor is active. We next optimize the biochemical assay procedure so that it remains linear as N-acetyltransferase is purified or protein-extracts containing N-acetyltransferase are diluted, by the inclusion of negatively charged lipids. We then use this assay to demonstrate that the purified single N-acetyltransferase protein is both necessary and sufficient to express transferase activity, and that N-acetyltransferase functions as a monomer. Finally, the kinetic mechanism of action of purified N-acetyltransferase was evaluated and found to be a random sequential mechanism involving the formation of a ternary complex with its two substrates; i.e., N-acetyltransferase does not operate through a ping-pong mechanism as previously reported. We confirm this conclusion by demonstrating experimentally that no acetylated enzyme intermediate is formed during the reaction

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Inhibitory gating in the Dentate Gyrus

Trabajo presentado al 18th National Meeting of the Spanish Society of Neuroscience (SENC), celebrado en Santiago de Compostela del 4 al 6 de septiembre de 2019.-Electrophysiological recordings have demonstrated the existence of a tight inhibitory control of hilar interneurons over Dentate Gyrus granule cells (DGgc). -Our experiments show that LTP induced in the perforant pathway (PP) potentiates glutamatergic synapses and reduces feed-foward inhibition in the DG. -To investigate this phenomenon we implemented a model that includes entorhinal cortex (EC) neurons, DGgc, mossy cells, basket cells and Hil cells. -Our results show that the increase of the glutamatergic inputs results in a net inhibition of the basket cell population. -Results of the model are supported by experiments in vitro where the LTP has been performed in vivo, obtaining this effect in the slice without antagonist GABAa. -Our findings suggest that LTP applied at the EC outputs modifies the excitation/inhibition balance in the Dentate Gyrus facilitating communication with CA3.Peer reviewe

Analizando los circuitos neuronales de la memoria desde una perspectiva interdisciplinar

Conferencia impartida en la Reial Acadèmia de Medicina de les Illes Balears el 2 de julio de 2019.Peer reviewe

Mealworm (Tenebrio molitor) Diets Relative to the Energy Requirements of Small Mygalomorph Spiders (Paraphysa sp.)

This article describes the basic prey requirements of Paraphysa sp., a small mygalomorph spider from the central Andes. Paraphysa sp. can be maintained in captivity using mealworms (Tenebrio molitor) as its primary food source. During a period of 66 days the prey requirements (larvae/day) were calculated for weight maintenance and compared with findings of previously reported resting and active metabolic rates. The spiders in this study ate at frequencies between 0.18 and 0.59 larvae/day, with an average of 0.43 ± 0.14 larvae/day. From the regression line between frequency of feeding (larvae/day) and weight gain, we determined that 0.31 larvae/day were needed for a weight gain of 0. Thus, for the spiders to increase their weight, they would need to eat more than 1 larva every 3 days. This frequency yields a caloric intake of 0.193 kcal/d, or equivalently, a carbon dioxide production of 0.189 mL CO2/g·h. The findings in this report are greater than the resting metabolic rate at 35°C, a

Regulation of inhibitory circuits in the dentate gyrus: role on temporal coding and pattern separation

Trabajo presentado en la SENC Meeting 2021 (Sociedad Española de Neurociencia), celebrada en Lleida del 3 al 5 de noviembre de 2021

An inhibitory gating mechanism operated by synaptic plasticity regulates information transmission between the dentate gyrus and CA3

Trabajo presentado en la 30th Annual Computational Neuroscience Meeting (CNS*2021), celebrada online del 3 al 7 de julio de 2021