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Spatio-temporal correlations and visual signalling in a complete neuronal population
TLDR
The functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells is analysed using a model of multi-neuron spike responses, and a model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlation activity in populations of neurons.
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Spike-triggered neural characterization.
TLDR
Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data and demonstrated with simulated model neuron examples that emphasize practical issues that arise in experimental situations.
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Discovering Event Structure in Continuous Narrative Perception and Memory
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Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model
TLDR
The fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity, and can be used to derive an explicit, maximum-likelihood decoding rule for neural spike trains.
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Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
TLDR
It is proved that the log-likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques.
View on MIT Press
Characterization of Neural Responses with Stochastic Stimuli
TLDR
A fundamental goal of sensory systems neuroscience is the characterization of the functional relationship between environmental stimuli and neural response, and a quasi-linear description of a neuron’s response properties that has dominated sensory neuroscience for the past 50 years is exemplified.
Encoding and decoding in parietal cortex during sensorimotor decision-making
TLDR
This work examined the neural code in LIP at the level of individual spike trains using a statistical approach based on generalized linear models and derived an optimal decoder for heterogeneous, multiplexed LIP responses that could be implemented in biologically plausible circuits.
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A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings
TLDR
This work investigates the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina, and develops diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.
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Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis.
TLDR
An information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model that provides an explicit "default" model of the nonlinear stage mapping the filter responses to spike rate, in the form of a ratio of Gaussians.
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Statistical models for neural encoding, decoding, and optimal stimulus design.
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