Neuroscience 2001 Abstract
| Presentation Number: | 931.12 |
|---|---|
| Abstract Title: | Motor Cortex Activity and Motor Input/Output Parameters: Neural networks. |
| Authors: |
Krouchev, N. I.*1
; Sergio, L.1
; Kalaska, J.1
1Dept physiol, Univ de Montreal, Montreal, Canada |
| Primary Theme and Topics |
Motor Systems - Cortex and Thalamus -- Physiology |
| Session: |
931. Cortex and thalamus: parameters, population and time Poster |
| Presentation Time: | Thursday, November 15, 2001 11:00 AM-12:00 PM |
| Location: | Exhibit Hall T-7 |
| Keywords: | reaching movements, nonlinear dynamic models, cortical population code, kinetics and kinematics |
We describe an alternative approach to the system identification analysis presented in (Krouchev et al., SFN Abstr. 26:1482, 2000) to interpret neuronal data from (Kalaska & Sergio, SFN Abstr. 23:1554, 1997).
It is based on the neural network model in (Lukashin et al., Biol. Cybern. 74:469, 1996). We enhanced the model to account for time-varying kinetic and neuronal data in an isometric force-ramp paradigm. By analyzing the computability of equilibrium point (EP) conditions for arbitrary points in the workspace, we also provide for handling kinematic data in an arm movement paradigm.
We are thus able to address:
1) The direct problem: Given an EP position, a direction of motor output and time-varying single unit activity, estimate generated forces at the hand.
2) The inverse problem: Given kinematics and kinetics, estimate neural activation.
We compare the predictions of this model based on the population-coding hypothesis with experimental data from behaving primates and with the results of our previous analysis in terms of goodness of fit and generalization across directions. In the latter context, we also present the results of a bootstrap-type autocorrelation analysis of spike sequences.
Factors influencing the performance of both dynamic models are under further study.
It is based on the neural network model in (Lukashin et al., Biol. Cybern. 74:469, 1996). We enhanced the model to account for time-varying kinetic and neuronal data in an isometric force-ramp paradigm. By analyzing the computability of equilibrium point (EP) conditions for arbitrary points in the workspace, we also provide for handling kinematic data in an arm movement paradigm.
We are thus able to address:
1) The direct problem: Given an EP position, a direction of motor output and time-varying single unit activity, estimate generated forces at the hand.
2) The inverse problem: Given kinematics and kinetics, estimate neural activation.
We compare the predictions of this model based on the population-coding hypothesis with experimental data from behaving primates and with the results of our previous analysis in terms of goodness of fit and generalization across directions. In the latter context, we also present the results of a bootstrap-type autocorrelation analysis of spike sequences.
Factors influencing the performance of both dynamic models are under further study.
Supported by MRC/CIHR Group Grant in Neurological Sciences and Human Frontier Science Program Group Grant, and FRSQ and FCAR fellowships to NK and LS
Sample Citation:
[Authors]. [Abstract Title]. Program No. XXX.XX. 2001 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2001. Online.
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