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SUMMARY:BMI Seminar // BMI Thesis Prize 2019 : Max Nolte - Untangling emer
 gent cortical dynamics: neurons from networks\, noise from chaos
DTSTART:20200129T121500
DTEND:20200129T131500
DTSTAMP:20260609T230124Z
UID:1d5f8e673d2c6228fcdd076aa143cdc59d9909a2544baf328be65bd5
CATEGORIES:Conferences - Seminars
DESCRIPTION:Max Nolte\, Blue Brain Project\nThe way in which cortical micr
 ocircuit components -- most importantly neurons -- and their connectivity 
 -- the network -- shape and constrain emergent dynamics is a long-standing
  question in neuroscience. Experimentally observed dynamical properties ca
 n often be explained by circuit models with different simplifying assumpti
 ons for the underlying neuron models and their network structure\, such as
  deterministic synapse models describing stochastic synapses\, or a unifor
 m network structure describing heterogeneous synaptic connectivity. Intrin
 sic neural variability\, for example\, can emerge from both stochastic syn
 aptic properties (noise) and deterministic network dynamics (chaos). It is
  therefore often not clear if models with ad hoc simplifying assumptions f
 or various biological details provide correct explanations for the emergen
 ce of cortical dynamics. In my thesis\, I set out to advance our understan
 ding of how detailed biological properties of cortical neurons and their n
 etwork structure shape emergent dynamics by studying a model of a prototyp
 ical neocortical microcircuit that was reconstructed using all relevant av
 ailable biological data (https://www.cell.com/fulltext/S0092-8674(15)01191
 -5). To make our predictions as biologically accurate as possible\, we use
 d a "zero tweak" strategy wherein parameters in the model were not adjuste
 d to replicate specific experimentally observed dynamical properties\, but
  instead were constrained by biological data. This allowed us to character
 ize the effect of two biological properties that are often abstracted away
  in ad hoc simplifications: stochastic synaptic transmission and a heterog
 eneous network structure with complex higher-order connectivity. Studying 
 the model\, we made several important predictions: (1) Stochastic synaptic
  transmission\, in an interplay with recurrent network dynamics\, causes r
 apid chaotic divergence of spontaneous activity. (2) Synaptic noise oversh
 adows other local cellular noise sources. (3) Amid the noise and chaos\, n
 eurons can reliably respond to external inputs with millisecond spike-time
  precision. (4) This reliable response goes beyond mere feedforward suppre
 ssion of recurrent dynamics and is driven by the circuit at a near-critica
 l excitation-inhibition balance (https://www.nature.com/articles/s41467-01
 9-11633-8). (5) An abundance of high-dimensional cliques of all-to-all con
 nected neurons which shape correlations between neurons in a hierarchical 
 manner (https://www.frontiersin.org/articles/10.3389/fncom.2017.00048). (6
 ) This effect is strongly reduced when synaptic connectivity is replaced b
 y a rejected null model with reduced higher-order network structure (https
 ://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00124). We conclude tha
 t a detailed representation of cellular noise sources and high-dimensional
  network structure is imperative to accurately model emergent cortical net
 work dynamics. Models that make ad hoc simplifying assumptions need to car
 efully justify the exclusion of such details.\n 
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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