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Correlations and signatures of criticality in neural population models
Published on Mar 07, 20161845 Views
Large-scale recording methods make it possible to measure the statistics of neural population activity, and thereby to gain insights into the principles that govern the collective activity of neural
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Correlations and signatures of criticality in neural population models00:00
Data00:39
Are neural networks poised at a thermodynamic critical point?01:32
Signatures of criticality in a recording of retinal ganglion cells - 102:08
Signatures of criticality in a recording of retinal ganglion cells - 202:36
Signatures of criticality in a recording of retinal ganglion cells - 302:49
Signatures of criticality in a recording of retinal ganglion cells - 402:58
We consider the distribution P(x) of binary ‘spikewords’ x03:21
‘K-pairwise model’: An Ising model with spike-count constraints04:27
Maximum entropy models can be used to translate concepts from thermodynamics to neural data06:53
Signatures of criticality in a recording of retinal ganglion cells - 509:38
What neural mechanisms can explain this observation?10:25
The simplest possible model of a patch of retina - 112:16
The simplest possible model of a patch of retina - 213:08
We infer model parameters of the K-pairwise model by maximising a penalised log-likelihood13:41
If you use use smart parameter-updates, you do not need to store the entire MCMC sample15:40
Signatures of criticality arise in a simple simulation of the retina, without any tuning or adaptation16:12
Tkacik et al 2015 - our model17:08
Analysis: Subsampling a homogeneous population19:07
In the beta-binomial model, all limits of interest can be calculated in closed form20:37
One source of ‘criticality inducing correlations’23:11
Neural data analysis24:34
Conclusion26:40
Thanks to...27:50