NIPS Proceedingsβ

Jonathan W. Pillow

20 Papers

  • Efficient inference for time-varying behavior during learning (2018)
  • Learning a latent manifold of odor representations from neural responses in piriform cortex (2018)
  • Model-based targeted dimensionality reduction for neuronal population data (2018)
  • Power-law efficient neural codes provide general link between perceptual bias and discriminability (2018)
  • Scaling the Poisson GLM to massive neural datasets through polynomial approximations (2018)
  • Gaussian process based nonlinear latent structure discovery in multivariate spike train data (2017)
  • A Bayesian method for reducing bias in neural representational similarity analysis (2016)
  • Adaptive optimal training of animal behavior (2016)
  • Bayesian latent structure discovery from multi-neuron recordings (2016)
  • Convolutional spike-triggered covariance analysis for neural subunit models (2015)
  • Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit (2014)
  • Inferring synaptic conductances from spike trains with a biophysically inspired point process model (2014)
  • Low-dimensional models of neural population activity in sensory cortical circuits (2014)
  • Optimal prior-dependent neural population codes under shared input noise (2014)
  • Sparse Bayesian structure learning with “dependent relevance determination” priors (2014)
  • Bayesian entropy estimation for binary spike train data using parametric prior knowledge (2013)
  • Bayesian inference for low rank spatiotemporal neural receptive fields (2013)
  • Spectral methods for neural characterization using generalized quadratic models (2013)
  • Spike train entropy-rate estimation using hierarchical Dirichlet process priors (2013)
  • Universal models for binary spike patterns using centered Dirichlet processes (2013)