Operant Conditioning of Cortical Activity E Fetz, 1969.

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Presentation transcript:

Operant Conditioning of Cortical Activity E Fetz, 1969

Background Many foundations considered a given now Muscle – motor unit correlation Voluntary control of activity Operant conditioning of firing

Design Experimental Paradigm Controls (What could modulate firing rate?) Goal: Determine modulator(s) Stimuli Reaching for pellet Getting a pellet

Methods Jackson 2007

Results ISIs Avg. Wave Different feedback modalities / subjects

Summary / Criticisms Modulation of firing rate is reward bound Do documentation of movement, what is the monkey learning to do?

Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Chapin et al. 1999

Lever-movement/robot-arm mode Robot Arm Water Dispenser Lever

What does spatio- temporal mean in this context? Trials # Time from movement onset (s)

NP-function/robot-arm mode Robot Arm Water Dispenser Lever

NP-function/robot-arm mode Did not press the lever, but still continued to reach

Was it the rats’ “imaginary movement” that triggered the robot-arm movement? The decoder did not discriminate physical movements and imaginary movements in neural signal. What kind of potential drawbacks could that make in real application? What kind of additional signals could potentially help discriminate physical vs imaginary movements?

Real-Time prediction of hand trajectory by ensembles of cortical neurons in primates Wessberg et al. 2000

Background Builds on previous paper, can “binary” decision be expanded to an analog signal? Primary and secondary motor cortices involved in reaching tasks (M1, PMd, PP)

Experiment Hypotheses 1-D and 3-D prediction Task dependent activation Linear vs. Non-linear Models Control External Device Experimental Overview

Methods - Recording Nieder 2005

Methods - Tasks

Artificial Neural Network (non-linear) Methods - Models Position Regression intercepts Firing rates Error Weights Regression (linear)

Results - Data

Results – Prediction, 1-D

Results – Prediction, 3-D

Results – Neuron Dropping

Discussion / Criticisms Max accuracy with short training times (10 minutes) Generalizability of classifier Relative contributions of motor areas More neurons != more accuracy. Why? Why does PMd contribute to better decoding w/ fewer neurons? Real time decoding different from offline decoding (ANNs better in offline, just more computationally expensive) Assumption of hyperbolic relationship, limited in extrapolation Engineering application is showy, but open loop.