English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Inference of a mesoscopic population model from population spike trains

René, A., Longtin, A., & Macke, J. H. (2019). Inference of a mesoscopic population model from population spike trains. arXiv, q-bio, arXiv:1910.01618.

Item is

Files

show Files
hide Files
:
1910.01618.pdf (Preprint), 7MB
Name:
1910.01618.pdf
Description:
version 2
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show
hide
Locator:
https://arxiv.org/abs/1910.01618 (Supplementary material)
Description:
-

Creators

show
hide
 Creators:
René, Alexandre1, Author
Longtin, André2, Author
Macke, Jakob H1, Author              
Affiliations:
1Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society, ou_2173683              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Machine Learning (stat.ML)
 Abstract: To understand how rich dynamics emerge in neural populations, we require models exhibiting a wide range of activity patterns while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single neuron scale to empirical population data. To close this gap, we propose to fit such data at a meso scale, using a mechanistic but low-dimensional and hence statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous `pools' of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived, and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent to a mesoscopic population model impact the accuracy of the inferred single-neuron parameters.

Details

show
hide
Language(s): eng - English
 Dates: 2020-03-082019-10-032019-10-03
 Publication Status: Published online
 Pages: 48
 Publishing info: -
 Table of Contents: -
 Rev. Type: No review
 Identifiers: arXiv: 1910.01618
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: arXiv, q-bio
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: arXiv:1910.01618 Start / End Page: - Identifier: -