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Sunday, July 19 • 5:40pm - 6:20pm
F2: Using evolutionary algorithms to explore single-cell heterogeneity and microcircuit operation in the hippocampus

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Andrea Navas-Olive, Liset M de la Prida Instituto Cajal CSIC. Ave Doctor Arce 37. Madrid 28002.

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 The hippocampus-entorhinal system is critical for learning and memory. Recent cutting-edge single-cell technologies from RNAseq to electrophysiology are disclosing a so far unrecognized heterogeneity within the major cell types (1). Surprisingly, massive high-throughput recordings of these very same cells identify low dimensional microcircuit dynamics (2,3). Reconciling both views is critical to understand how the brain operates.

The CA1 region is considered high in the hierarchy of the entorhinal-hippocampal system. Traditionally viewed as a single layered structure, recent evidence has disclosed an exquisite laminar organization across deep and superficial pyramidal sublayers at the transcriptional, morphological and functional levels (1,4,5). Such a low-dimensional segregation may be driven by a combination of intrinsic, biophysical and microcircuit factors but mechanisms are unknown.

Here, we exploit evolutionary algorithms to address the effect of single-cell heterogeneity on CA1 pyramidal cell activity (6). First, we developed a biophysically realistic model of CA1 pyramidal cells using the Hodgkin-Huxley multi-compartment formalism in the Neuron+Python platform and the morphological database Neuromorpho.org. We adopted genetic algorithms (GA) to identify passive, active and synaptic conductances resulting in realistic electrophysiological behavior. We then used the generated models to explore the functional effect of intrinsic, synaptic and morphological heterogeneity during oscillatory activities. By combining results from all simulations in a logistic regression model we evaluated the effect of up/down-regulation of different factors. We found that muyltidimensional excitatory and inhibitory inputs interact with morphological and intrinsic factors to determine a low dimensional subset of output features (e.g. phase-locking preference) that matches non-fitted experimental data.


Andrea Navas-Olive is supported by PhD Fellowship FPU17/03268.


1. Cembrowski MS, Spruston N. Heterogeneity within classical cell types is the rule: lessons from hippocampal pyramidal neurons. Nat Rev Neurosci. 2019, 20(4):193-204 2. Chaudhuri R, Gerçek B, Pandey B, Peyrache A, Fiete I. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nat Neurosci. 2019, 22(9):1512-1520

3. Guo W, Zhang JJ, Newman JP, Wilson MA. Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations. bioRxiv. 2020, doi.org/10.1101/2020.02.27.967794

4. Bannister, NJ, and Larkman, AU. Dendritic morphology of CA1 pyramidal neurones from the rat hippocampus: I. Branching patterns. J. Comp. Neurol. 1995, 360, 150–160

5. Valero, M, Cid, E, Averkin, RG, Aguilar, J, Sanchez-Aguilera, A, Viney, TJ, Gomez-Dominguez, D, Bellistri, E, and De La Prida, LM. Determinants of different deep and superficial CA1 pyramidal cell dynamics during sharp-wave ripples. Nat. Neurosci. 2015,18

6. Navas-Olive A, Valero M, de Salas A, Jurado-Parras T, Averkin RG, Gambino G, Cid E, de la Prida LM. Multimodal determinants of phase-locked dynamics across deep-superficial hippocampal sublayers during theta oscillations. Nat Commun 11, 2217 (2020). https://doi.org/10.1038/s41467-020-15840-6

avatar for Andrea Navas-Olive

Andrea Navas-Olive

PhD Student, Instituto Cajal CSIC
Realistic modelsNEURONGenetic AlgorithmMachine Learning

Sunday July 19, 2020 5:40pm - 6:20pm CEST
  Featured Talk, Hippocampus
  • Moderator Jean-Marc Fellous; Soledad Gonzalo Cogno; Tom Burns