# Empirical models of spiking in neural populations

@inproceedings{Macke2011EmpiricalMO, title={Empirical models of spiking in neural populations}, author={Jakob H. Macke and Lars Buesing and John P. Cunningham and Byron M. Yu and Krishna V. Shenoy and Maneesh Sahani}, booktitle={NIPS}, year={2011} }

Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fitting statistical models to unaveraged data. What statistical structure best describes the concurrent spiking of cells within a local network? We argue that in the cortex, where firing exhibits extensive correlations in both time and space and where a typical sample of neurons still reflects only a…

## 194 Citations

### Inference of a Mesoscopic Population Model from Population Spike Trains

- Computer ScienceNeural Computation
- 2020

This work proposes to fit mechanistic spiking networks at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model, and extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data.

### Low-dimensional models of neural population activity in sensory cortical circuits

- BiologyNIPS
- 2014

A statistical model of neural population activity that integrates a nonlinear receptive field model with a latent dynamical model of ongoing cortical activity that captures temporal dynamics and correlations due to shared stimulus drive as well as common noise is introduced.

### Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models

- Computer ScienceNeural Computation
- 2018

This letter generates spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current, and fits a statistical model, a generalized linear model, with multiplicative influences of past spiking, which concludes that the GLM captures essential features of the simulated spike trains but for near-deterministic spike trains, goodness-of-fit analyses reveal the model does not fit very well in a statistical sense.

### Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

- BiologybioRxiv
- 2019

A statistically principled approach based on a population of doubly-stochastic integrate-and-fire neurons, taking into account basic biophysics is presented, providing statistical inference tools for a class of reasonably constrained, mechanistic models.

### Mesoscopic modeling of hidden spiking neurons

- Computer ScienceArXiv
- 2022

It is shown, on synthetic spike trains, that a few observed neurons are sufﬁcient for neuLVM to perform model inversion of large SNNs, in the sense that it can recover connectivity parameters, infer single-trial latent population activity, reproduce ongoing metastable dynamics, and generalize when subjected to perturbations mimicking photo-stimulation.

### Neural dynamics in cortical populations

- Biology, Psychology
- 2015

New efficient methods for discovering the low-dimensional dynamics that underlie simultaneously-recorded spike trains from a neural population are developed and a functional role for dynamics in the representation of visual motion in visual cortex is proposed.

### A Statistical Model for In Vivo Neuronal Dynamics

- Biology, Computer SciencePloS one
- 2015

A novel single neuron model is proposed that characterizes the statistical properties of in vivo recordings and has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential.

### Fitting summary statistics of neural data with a differentiable spiking network simulator

- Computer ScienceNeurIPS
- 2021

This new fitting algorithm improves upon other fitting algorithms for spiking network models like GLMs (Generalized Linear Models) which do not usually rely on back-propagation and enables the consideration of hidden neurons which is otherwise notoriously hard.

### Inferring neural population dynamics from multiple partial recordings of the same neural circuit

- BiologyNIPS
- 2013

A statistical method for "stitching" together sequentially imaged sets of neurons into one model by phrasing the problem as fitting a latent dynamical system with missing observations is added, which allows us to substantially expand the population-sizes for which population dynamics can be characterized—beyond the number of simultaneously imaged neurons.

### Population activity statistics dissect subthreshold and spiking variability in V1.

- Biology, PsychologyJournal of neurophysiology
- 2017

This work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability, and argues that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations.

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