Neuroscience 2002 Abstract
| Presentation Number: | 357.1 |
|---|---|
| Abstract Title: | Nonlinear encoding and decoding in primary motor cortex (MI). |
| Authors: |
Paninski, L. M.*1
; Fellows, M. R.2
; Shoham, S.3
; Donoghue, J. P.2
1Neural Sci, New York Univ, New York, NY 2Neurosci, Brown Univ, Prov, RI 3Mol Bio, Princeton Univ, Princeton, NJ |
| Primary Theme and Topics |
Motor Systems - Cortex and Thalamus -- Physiology |
| Secondary Theme and Topics | Techniques in Neuroscience<br />- Data analysis, physiological methods, statistics |
| Session: |
357. Cortex and thalamus: physiology--neural prosthetics Poster |
| Presentation Time: | Monday, November 4, 2002 1:00 PM-2:00 PM |
| Location: | Hall A2-B3 F-3 |
| Keywords: | neural coding, estimation, neural prosthetic, probabilistic model |
We introduce a low-dimensional but explicitly nonlinear spatiotemporal
model for the encoding of hand position by primary motor cortical (MI)
neurons during a random pursuit tracking task. We find that the
probability of a spike in a small time bin of size $dt$ is well
approximated by the following formula: $P(spike|x) = exp(Ax+b)) dt$,
where $b$ is a scalar and $A$ is a linear functional of the
time-varying, two-dimensional hand position signal $x$. We give an
algorithm for efficiently fitting the parameters $A$ and $b$ to data.
The model is compact and yet appears to account for all previously
described spatiotemporal hand position tuning properties of MI
cells (Fellows et al, SFN `01). Given the above model and an
additional (maximum-entropy) assumption on the population encoding
properties of MI, we can define the mean-square optimal estimator of
the hand position signal given population neural activity; we give an
algorithm to compute this estimator. Although the encoding properties
of the observed MI cells are inherently nonlinear, we find that the
optimal estimator is a nearly linear function of the population neural
activity; that is, the MI hand position signal can be decoded
accurately using simple and efficient linear methods (Paninski et al,
SFN `99). This has important implications both for the design of
neural prosthetic devices and for the general study of cortical motor
neural coding.
model for the encoding of hand position by primary motor cortical (MI)
neurons during a random pursuit tracking task. We find that the
probability of a spike in a small time bin of size $dt$ is well
approximated by the following formula: $P(spike|x) = exp(Ax+b)) dt$,
where $b$ is a scalar and $A$ is a linear functional of the
time-varying, two-dimensional hand position signal $x$. We give an
algorithm for efficiently fitting the parameters $A$ and $b$ to data.
The model is compact and yet appears to account for all previously
described spatiotemporal hand position tuning properties of MI
cells (Fellows et al, SFN `01). Given the above model and an
additional (maximum-entropy) assumption on the population encoding
properties of MI, we can define the mean-square optimal estimator of
the hand position signal given population neural activity; we give an
algorithm to compute this estimator. Although the encoding properties
of the observed MI cells are inherently nonlinear, we find that the
optimal estimator is a nearly linear function of the population neural
activity; that is, the MI hand position signal can be decoded
accurately using simple and efficient linear methods (Paninski et al,
SFN `99). This has important implications both for the design of
neural prosthetic devices and for the general study of cortical motor
neural coding.
Supported by NIH (NS25074, NS93322), DARPA, and Keck Foundation grants to JPD, and a HHMI Predoctoral Fellowship to LP
JPD is a co-founder of Cyberkinetics, a company formed with the goal of building and marketing neural prosthetic devices.
Sample Citation:
[Authors]. [Abstract Title]. Program No. XXX.XX. 2002 Neuroscience Meeting Planner. Orlando, FL: Society for Neuroscience, 2002. Online.
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