global brain talk monday

# misha ahrens: multimodal imaging studies.
five modality imaging
multimodal imaging. gaba voltage glu calcium
Computational correlation framework: optical imaging corelation functional
Radial astrocytes. transition between active and passive behaviral state
glibal imaging Mu cell 2019
noredrenalergic cells. fiber. medula oblongata. glial calcium transients.
Tze Koh from Byron Yu lab.

# Maneesh Sahani. UCL.
population analysis and theory

Gaussian Process Factor Analysis (GPFA) is a method for inferring latent structure that is shared across a population of neurons from single trials [2]. … Thus, inter-trial variability in neural firing that is shared across the population is modelled via the evolution of the latent processes on a given trial.May 27, 2018Temporal alignment and latent Gaussian process … – bioRxiv
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temporal variability. time warping. additive varition. point process observation
http://papers.neurips.cc/paper/8245-temporal-alignment-and-latent-gaussian-process-factor-inference-in-population-spike-trains

neuron x time x condition: neuronXfactor, conditionXdrive, factorXdriveXtime. Factorization of regression tensor. Soldado-Magraner

DIstribution representation of neural code
A. averaging.

B. Distributed distributional code (DDC). encodes distribution.
– expected value int(p(x)x)
– probablistic computations. beliefe propagation. decidsion making (reward expectation), variational learning (latent statistics expected value)
– so neural firing rate is the coefficient of the basis function

Easy to learn, representative power, …
Helmholtz machine: unsupervised learning by bootstrapping generation and recognition
adversarial? generative and recognition network.
wake phase and sleep phase.

Infrerence in time-postdiction (wenliang & sahani 2019 NIPS) same as NeurIPS
carry belief from the past. dynamical DDC


# Session dinner
Ahrens and Ziquin. voltron imaging.
https://www.biorxiv.org/content/10.1101/436840v1

certain behaviors are phased locked to other behaviors. this is related to the behavioral hierarchy. distributed CPG. this sets the mechanism of multi-timescale behavioral hierarchy (Mark Zimmer)
impute corrupted behavior. probablistic genrative model
Scott Linderman biorxiv, behavioral decoding work.
https://web.stanford.edu/~swl1/#publications

NeuroPAL. deterministic brainbow. neuralID and connectome.

# Svoboda Mesoscale activity project (MAP)
memory guided flexible mbehavior. Jorge Jaramillo
David Lui. Susu Chen. premotor circuits in the medulla. medulla delay period firing rate increases. ALM and basal ganglia.
in-cage training setup. track localization. ALM projection to either thalamic or medulla projection types. differentiate those two. basal ganglia disinhibition. release movement. disinhibition. movement initiation

# Mante (ETH zurich)
dynamics attractor can be modelled by different combination of potential landscape + input dynamics combination. compare saddle point, line attractor, point attractor types.
compare residuals to compare likelihood of model correctness
eigenvalue, singular values. point attractor is stable. 2D dimensional projeciton. see how the system responds to the perturbation.

# Sussilo
Interpretable neural dynamics: GOLD. goal oriented learning of dynamics
Encoder RNN. stimulus to behavior to Neural. Encoder RNN.
dynamical mechanism.
C. Chandrasekeran. PMd neuropixels task.

# xiao-jing Wang. distributed persistent acivity in multi-regional brain circuits
persistent activity. can’t be stimulated directly.
mouse model whole brain. Allen institute Julie Harris
https://www.nature.com/articles/s41593-019-0417-0
keeping track of information. mechanism of short term memory.
long firing or firing chain?
sequential activation