Existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel independent noise in the measured fluorescence.
We then consider a powerful class of temporally structured latent variable models known as Input-Output Hidden Markov Models (IO-HMMs), which have recently gained prominence in neuroscience.
Approximate Bayesian inference methods provide a powerful suite of tools for finding approximations to intractable posterior distributions.
Here we address this shortcoming by proposing ``signal-noise'' Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity.
We propose high-contrast, binarized versions of natural images---termed gaudy images---to efficiently train DNNs to predict higher-order visual cortical responses.
Specifically, this allows us to: (i) compare different learning rules and objective functions that an animal may be using to update its policy; (ii) estimate distinct learning rates for different parameters of an animal’s policy; (iii) identify variations in learning across cohorts of animals; and (iv) uncover trial-to-trial changes that are not captured by normative learning rules.
A key challenge in understanding the sensory transformations of the visual system is to obtain a highly predictive model of responses from visual cortical neurons.
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision.
We demonstrate that PAL estimators achieve fast and accurate extraction of latent structure from multi-neuron spike train data.
Our approach is based on the Gaussian process latent variable model, and seeks to map odorants to points in a low-dimensional embedding space, where distances between points in the embedding space relate to the similarity of population responses they elicit.
To overcome these limitations, we propose a dynamic psychophysical model that efficiently tracks trial-to-trial changes in behavior over the course of training.
Specifically, we show that the same lawful relationship between bias and discriminability arises whenever Fisher information is allocated proportional to any power of the prior distribution.
Here we propose a new model-based method for targeted dimensionality reduction based on a probabilistic generative model of the population response data.
We use the quadratic estimator to fit a fully-coupled Poisson GLM to spike train data recorded from 831 neurons across five regions of the mouse brain for a duration of 41 minutes, binned at 1 ms resolution.
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights.
We apply the model to spike trains recorded from hippocampal place cells and show that it compares favorably to a variety of previous methods for latent structure discovery, including variational auto-encoder (VAE) based methods that parametrize the nonlinear mapping from latent space to spike rates with a deep neural network.
Our approach represents a hierarchical extension of the relevance determination framework, where we add a transformed Gaussian process to model the dependencies between the prior variances of regression weights.
An exciting branch of machine learning research focuses on methods for learning, optimizing, and integrating unknown functions that are difficult or costly to evaluate.
We show that we can accurately infer the parameters of a policy-gradient-based learning algorithm that describes how the animal's internal model of the task evolves over the course of training.
We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task.
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties.
Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), model parameters as independent a priori, and therefore do not exploit such dependencies.
Many signals, such as spike trains recorded in multi-channel electrophysiological recordings, may be represented as the sparse sum of translated and scaled copies of waveforms whose timing and amplitudes are of interest.
We show that the model fit to extracellular spike trains can predict excitatory and inhibitory conductances elicited by novel stimuli with nearly the same accuracy as a model trained directly with intracellular conductances.
Moreover, because the nonlinear stimulus inputs are mixed by the ongoing dynamics, the model can account for a relatively large number of idiosyncratic receptive field shapes with a small number of nonlinear inputs to a low-dimensional latent dynamical model.
We also establish a condition for equivalence between the cascade-logistic and the 2nd-order maxent or "Ising'' model, making cascade-logistic a reasonable choice for base measure in a universal model.
We present both a fully Bayesian and empirical Bayes entropy rate estimator based on this model, and demonstrate their performance on simulated and real neural spike train data.
Shannon's entropy is a basic quantity in information theory, and a fundamental building block for the analysis of neural codes.
The quadratic form characterizes the neuron's stimulus selectivity in terms of a set linear receptive fields followed by a quadratic combination rule, and the invertible nonlinearity maps this output to the desired response range.
In typical experiments with naturalistic or flickering spatiotemporal stimuli, RFs are very high-dimensional, due to the large number of coefficients needed to specify an integration profile across time and space.
Characterizing the information carried by neural populations in the brain requires accurate statistical models of neural spike responses.
We consider the problem of estimating Shannon's entropy H in the under-sampled regime, where the number of possible symbols may be unknown or countably infinite.
Active learning can substantially improve the yield of neurophysiology experiments by adaptively selecting stimuli to probe a neuron's receptive field (RF) in real time.
With simulated experiments, we show that optimal design substantially reduces the amount of data required to estimate this nonlinear combination rule.
We describe an empirical Bayes method for selecting the number of features, and extend the model to accommodate an arbitrary elliptical nonlinear response function, which results in a more powerful and more flexible model for feature space inference.
Recent work on the statistical modeling of neural responses has focused on modulated renewal processes in which the spike rate is a function of the stimulus and recent spiking history.