Exploring the statistical structure of large-scale neural recordings using a sparse coding model
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1
University of California, Redwood Center for Theoretical Neuroscience, United States
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2
Weill Cornell Medical College, United States
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3
National University of Singapore, Singapore
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4
Georgia Institute of Technology, United States
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5
University of California, United States
We present a method for exploratory data analysis that attempts to learn the underlying statistical structure of large-scale neural recordings via a sparse coding model (Olshausen and Field, 1996; Olshausen 2003). In this model, a vector time series of filtered electrode potentials is represented as the sum of a convolution of latent sparse coefficients with a set of kernels. The set of kernels are learned from the data by maximizing the log-likelihood of the model through stochastic coordinate-wise ascent separately in the sparse coefficients and kernel parameters. At each step, a random batch of data is used to infer sparse coefficients using the current kernel basis, then the kernels are updated to minimize residual error. The algorithm was implemented in parallel with the inference step scaling linearly in the number of processes. The efficiency of the learning step was improved by the use of a 2nd-order stochastic gradient method. The model was applied to data recorded from visual cortex of an anesthetized cat viewing full-field natural movies. The recording device was a single shank polytrode with 54 contacts staggered vertically in two columns inserted perpendicular to the cortical surface and spanning all layers. The model was applied separately to bandpass filtered data (500Hz-10kHz) and and to LFP (0-150Hz) from a single penetration. For the bandpass data, a subset of learned kernels consisted of localized waveforms that corresponded closely with spike waveforms estimated through a separate cluster-based spike sorting algorithm. For the LFP, the learned kernels recovered the dominant oscillation frequencies in the gamma range that were a characteristic feature of this particular recording. While the biophysical underpinnings of the learned kernels in some cases are unknown, the method provides a data-driven parsing of the multi-channel recordings into different groups according to the spatiotemporal statistics of neural activity. These groups, or "virtual units," thus serve as additional candidates for regressing with features of the stimulus, along with single-unit activity, to help unravel the ensemble coding and circuit dynamics that underlie cortical computations.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session III
Citation:
Khosrowshahi
A,
Baker
J,
Herikstad
R,
Yen
S,
Rozell
CJ and
Olshausen
BA
(2010). Exploring the statistical structure of large-scale neural recordings using a sparse coding model.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00260
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Received:
05 Mar 2010;
Published Online:
05 Mar 2010.
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Correspondence:
Amir Khosrowshahi, University of California, Redwood Center for Theoretical Neuroscience, Berkeley, United States, amirk@berkeley.edu