<commit_before># Overview

This repository provides MATLAB code for estimating the parameters of the Generalized Quadratic Model (GQM), as described in the paper:

* <NAME>, <NAME>, <NAME>, <NAME>, [Spectral methods for neural characterization using generalized quadratic models](http://pillowlab.princeton.edu/pubs/ParkI_GQM_NIPS2013.pdf), Advances in Neural Information Processing Systems 26, 2454--2462

The GQM is a statistical modeling framework arising from the neuroscience literature, and in particular the problem of modeling a neuron's response to a high-dimensional experimental stimulus. For instance: would you like to understand what a neuron in your visual cortex does when you watch random noise on your TV? If so, the GQM may be the right tool for you.

## The GQM

The GQM is a probabilistic model of a response `y` conditioned upon a stimulus `x`,

> y|x ~ P(f(Q(x)))

where `Q(x) = x^T C x + b^T x + a` is a quadratic function of the stimulus and `P` is a noise model. 

For example, for continuous-valued $y$ one might select `P` to be Gaussian, whereas for discreted-valued `y` one might choose `P` to be Poisson. The GQM is closely-related to the Generalized Linear Model (GLM), the Linear-Nonlinear-Poisson model and the 2nd-order Volterra model, among others; please see the paper for further details. 


## Maximum Expected Log-Likelihood (MEL) Estimators 


The code in this repository implements several Maximum Expected Log Likelihood (MEL) estimators for the parameters `C`, `b`, and `a`. These estimators take different forms depending upon the distribution of `x` and the noise model `P`.

MEL estimators provide a connection between the GQM and moment-based [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) techniques such as the [Spike-triggered covariance](https://en.wikipedia.org/wiki/Spike-triggered_covariance).


# Using the Code


The code provides methods for working with GQM models and efficiently computing the moments of `x` and `y`. The file `gqm_example.m` provides a tutorial overview of simulating and fitting a GQM model. # Overview

This repository provides MATLAB code for estimating the parameters of the Generalized Quadratic Model (GQM), as described in the paper:

* <NAME>, <NAME>, <NAME>, <NAME>, [Spectral methods for neural characterization using generalized quadratic models](http://pillowlab.princeton.edu/pubs/ParkI_GQM_NIPS2013.pdf), Advances in Neural Information Processing Systems 26, 2454--2462

The GQM is a statistical modeling framework arising from the neuroscience literature, and in particular the problem of modeling a neuron's response to a high-dimensional experimental stimulus. For instance: would you like to understand what a neuron in your visual cortex does when you watch random noise on your TV? If so, the GQM may be the right tool for you.

## The GQM

The GQM is a probabilistic model of a response `y` conditioned upon a stimulus `x`,

> y|x ~ P(f(Q(x)))

where `Q(x) = x^T C x + b^T x + a` is a quadratic function of the stimulus and `P` is a noise model. 

For example, for continuous-valued $y$ one might select $P$ to be Gaussian, whereas for discreted-valued $y$ one might choose $P$ to be Poisson. The GQM is closely-related to the Generalized Linear Model (GLM), the Linear-Nonlinear-Poisson model and the 2nd-order Volterra model, among others; please see the paper for further details. 


## Maximum Expected Log-Likelihood (MEL) Estimators 


The code in this repository implements several Maximum Expected Log Likelihood (MEL) estimators for the parameters `C`, `b`, and `a`. These estimators take different forms depending upon the distribution of `x` and the noise model `P`.

MEL estimators provide a connection between the GQM and moment-based [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) techniques such as the [Spike-triggered covariance](https://en.wikipedia.org/wiki/Spike-triggered_covariance).


# Using the Code


The code provides methods for working with GQM models and efficiently computing the moments of `x` and `y`. The file `gqm_example.m` provides a tutorial overview of simulating and fitting a GQM model. # Overview

This repository provides MATLAB code for estimating the parameters of the Generalized Quadratic Model (GQM), as described in the paper:

* <NAME>, <NAME>, <NAME>, <NAME>, [Spectral methods for neural characterization using generalized quadratic models](http://pillowlab.princeton.edu/pubs/ParkI_GQM_NIPS2013.pdf), Advances in Neural Information Processing Systems 26, 2454--2462

The GQM is a statistical modeling framework arising from the neuroscience literature, and in particular the problem of modeling a neuron's response to a high-dimensional experimental stimulus. For instance: would you like to understand what a neuron in your visual cortex does when you watch random noise on your TV? If so, the GQM may be the right tool for you.

## The GQM

The GQM is a probabilistic model of a response $y$ conditioned upon a stimulus $x$, 

$$ y|x \sim P(f(Q(x)))$$

where $Q(x) = x^T C x + b^T x + a$ is a quadratic function of the stimulus and $P$ is a noise model. 

For example, for continuous-valued $y$ one might select $P$ to be Gaussian, whereas for discreted-valued $y$ one might choose $P$ to be Poisson. The GQM is closely-related to the Generalized Linear Model (GLM), the Linear-Nonlinear-Poisson model and the 2nd-order Volterra model, among others; please see the paper for further details. 


## Maximum Expected Log-Likelihood (MEL) Estimators 


The code in this repository implements several Maximum Expected Log Likelihood (MEL) estimators for the parameters $C$, $b$, and $a$. These estimators take different forms depending upon the distribution of $x$ and the noise model $P$.

MEL estimators provide a connection between the GQM and moment-based [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) techniques such as the [Spike-triggered covariance](https://en.wikipedia.org/wiki/Spike-triggered_covariance).


<commit_msg>correcting emacs fumbles<commit_after># Overview

This repository provides MATLAB code for estimating the parameters of the Generalized Quadratic Model (GQM), as described in the paper:

* <NAME>, <NAME>, <NAME>, <NAME>, [Spectral methods for neural characterization using generalized quadratic models](http://pillowlab.princeton.edu/pubs/ParkI_GQM_NIPS2013.pdf), Advances in Neural Information Processing Systems 26, 2454--2462

The GQM is a statistical modeling framework arising from the neuroscience literature, and in particular the problem of modeling a neuron's response to a high-dimensional experimental stimulus. For instance: would you like to understand what a neuron in your visual cortex does when you watch random noise on your TV? If so, the GQM may be the right tool for you.

## The GQM

The GQM is a probabilistic model of a response `y` conditioned upon a stimulus `x`,

> y|x ~ P(f(Q(x)))

where `Q(x) = x^T C x + b^T x + a` is a quadratic function of the stimulus and `P` is a noise model. 

For example, for continuous-valued $y$ one might select `P` to be Gaussian, whereas for discreted-valued `y` one might choose `P` to be Poisson. The GQM is closely-related to the Generalized Linear Model (GLM), the Linear-Nonlinear-Poisson model and the 2nd-order Volterra model, among others; please see the paper for further details. 


## Maximum Expected Log-Likelihood (MEL) Estimators 


The code in this repository implements several Maximum Expected Log Likelihood (MEL) estimators for the parameters `C`, `b`, and `a`. These estimators take different forms depending upon the distribution of `x` and the noise model `P`.

MEL estimators provide a connection between the GQM and moment-based [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) techniques such as the [Spike-triggered covariance](https://en.wikipedia.org/wiki/Spike-triggered_covariance).


# Using the Code


The code provides methods for working with GQM models and efficiently computing the moments of `x` and `y`. The file `gqm_example.m` provides a tutorial overview of simulating and fitting a GQM model. 
{'fraction_non_alphanumeric': 0.07287104622871046, 'fraction_numerical': 0.0072992700729927005, 'mean_word_length': 4.855413105413105, 'pattern_counts': {'":': 0, '<': 19, '<?xml version=': 0, '>': 22, 'https://': 8, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6041695', 'n_tokens_mistral': 2395, 'n_tokens_neox': 2168, 'n_words': 1118}