• Publications
  • Influence
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. Expand
Estimation of Entropy and Mutual Information
  • L. Paninski
  • Computer Science, Chemistry
  • Neural Computation
  • 1 June 2003
TLDR
An exact local expansion of the entropy function is used to prove almost sure consistency and central limit theorems for three of the most commonly used discretized information estimators, and leads to an estimator with some nice properties: the estimator comes equipped with rigorous bounds on the maximum error over all possible underlying probability distributions, and this maximum error turns out to be surprisingly small. Expand
Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition
TLDR
This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. Expand
Maximum likelihood estimation of cascade point-process neural encoding models
TLDR
This work investigates the shape of the likelihood function for this type of model, gives a simple condition on the nonlinearity ensuring that no non-global local maxima exist in the likelihood—leading to efficient algorithms for the computation of the maximum likelihood estimator—and discusses the implications for the form of the allowed nonlinearities. Expand
Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data
TLDR
This work presents a modular approach for analyzing calcium imaging recordings of large neuronal ensembles that relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neurons in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. Expand
Fast nonnegative deconvolution for spike train inference from population calcium imaging.
TLDR
This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations, which outperforms optimal linear deconVolution (Wiener filtering) on both simulated and biological data. Expand
A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data
  • L. Paninski
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 1 October 2008
TLDR
The test for uniformity introduced here is based on the number of observed ldquocoincidencesrdquo (samples that fall into the same bin), the mean and variance of which may be computed explicitly for the uniform distribution and bounded nonparametrically for any distribution that is known to be epsiv-distant from uniform. Expand
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
TLDR
A new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest is described, which substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. Expand
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. Expand
Convergence properties of three spike-triggered analysis techniques
  • L. Paninski
  • Computer Science, Medicine
  • NIPS
  • 1 January 2003
TLDR
An estimator for the LN model parameters which is designed to converge under general conditions to the correct model is introduced, and the rate of convergence of this estimator is derived and provided. Expand
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