Timezone: »
Summarizing high-dimensional data using a small number of parameters is a ubiquitous first step in the analysis of neuronal population activity. Recently developed methods use "targeted" approaches that work by identifying multiple, distinct low-dimensional subspaces of activity that capture the population response to individual experimental task variables, such as the value of a presented stimulus or the behavior of the animal. These methods have gained attention because they decompose total neural activity into what are ostensibly different parts of a neuronal computation. However, existing targeted methods have been developed outside of the confines of probabilistic modeling, making some aspects of the procedures ad hoc, or limited in flexibility or interpretability. Here we propose a new model-based method for targeted dimensionality reduction based on a probabilistic generative model of the population response data. The low-dimensional structure of our model is expressed as a low-rank factorization of a linear regression model. We perform efficient inference using a combination of expectation maximization and direct maximization of the marginal likelihood. We also develop an efficient method for estimating the dimensionality of each subspace. We show that our approach outperforms alternative methods in both mean squared error of the parameter estimates, and in identifying the correct dimensionality of encoding using simulated data. We also show that our method provides more accurate inference of low-dimensional subspaces of activity than a competing algorithm, demixed PCA.
Author Information
Mikio Aoi (Princeton University)
Jonathan Pillow (Princeton University)
More from the Same Authors
-
2021 : Neural Latents Benchmark ‘21: Evaluating latent variable models of neural population activity »
Felix Pei · Joel Ye · David Zoltowski · Anqi Wu · Raeed Chowdhury · Hansem Sohn · Joseph O'Doherty · Krishna V Shenoy · Matthew Kaufman · Mark Churchland · Mehrdad Jazayeri · Lee Miller · Jonathan Pillow · Il Memming Park · Eva Dyer · Chethan Pandarinath -
2022 : Non-exchangeability in Infinite Switching Linear Dynamical Systems »
Victor Geadah · Jonathan Pillow -
2022 Poster: Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior »
Zoe Ashwood · Aditi Jha · Jonathan Pillow -
2022 Poster: Extracting computational mechanisms from neural data using low-rank RNNs »
Adrian Valente · Jonathan Pillow · Srdjan Ostojic -
2020 Poster: High-contrast “gaudy” images improve the training of deep neural network models of visual cortex »
Benjamin Cowley · Jonathan Pillow -
2020 Poster: Identifying signal and noise structure in neural population activity with Gaussian process factor models »
Stephen Keeley · Mikio Aoi · Yiyi Yu · Spencer Smith · Jonathan Pillow -
2020 Poster: Inferring learning rules from animal decision-making »
Zoe Ashwood · Nicholas Roy · Ji Hyun Bak · Jonathan Pillow -
2018 Poster: Scaling the Poisson GLM to massive neural datasets through polynomial approximations »
David Zoltowski · Jonathan Pillow -
2018 Poster: Efficient inference for time-varying behavior during learning »
Nicholas Roy · Ji Hyun Bak · Athena Akrami · Carlos Brody · Jonathan Pillow -
2018 Poster: Power-law efficient neural codes provide general link between perceptual bias and discriminability »
Michael J Morais · Jonathan Pillow -
2018 Poster: Learning a latent manifold of odor representations from neural responses in piriform cortex »
Anqi Wu · Stan Pashkovski · Sandeep Datta · Jonathan Pillow -
2017 Poster: Gaussian process based nonlinear latent structure discovery in multivariate spike train data »
Anqi Wu · Nicholas Roy · Stephen Keeley · Jonathan Pillow -
2016 Poster: Bayesian latent structure discovery from multi-neuron recordings »
Scott Linderman · Ryan Adams · Jonathan Pillow -
2016 Poster: Adaptive optimal training of animal behavior »
Ji Hyun Bak · Jung Choi · Ilana Witten · Athena Akrami · Jonathan Pillow -
2016 Poster: A Bayesian method for reducing bias in neural representational similarity analysis »
Mingbo Cai · Nicolas W Schuck · Jonathan Pillow · Yael Niv -
2015 Poster: Convolutional spike-triggered covariance analysis for neural subunit models »
Anqi Wu · Il Memming Park · Jonathan Pillow