Neural Coding

TopicsTopics inin BrainBrain ComputerComputer InterfacesInterfaces CS295CS295--7,7, FallFall 20022002

Neural Coding

Michael J. Black Brown University Department Computer Science

“One might think of the computer in this case as a prosthetic device. Just as a man who has his arm amputated can receive a mechanical equivalent of the lost arm, so a brain-damaged man can receive a mechanical aid to overcome the effects of brain damage. … It makes the computer a high-class wooden leg.” Michael Crichton, The Terminal Man, 1972

Note that CIS released a new version of Matlab yesterday. Both are available, but the older version is the default. The links are /usr/local/bin/matlab /usr/local/bin/matlab61 /usr/local/bin/matlab65 The first two point to the same thing.

Thursday: Matt Fellows: Detecting and Discriminating Neural Signals See Syllabus for reading. Tuesday: Moran and Schwartz – need presenter Assignment 1 – currently being updated with new data.

Sensation

Action

Sensation

Action

Single cells of the nervous system

NEURON

source: David Sheinberg

source: Tanya McGraw

source: Health South Press

Source: Chudler

Spikes

2/1000’s second

1/10 mm

source: David Sheinberg

Computational Elements 100,000,000,000 100,000,000 Neurons Transistors Speed (operations/second/element) 30-300 1.5 * 109

source: Intel

SYNAPSES

source: David Sheinberg

• How do cells represent information? • ie, how is representation “coded” in action potentials.

• If we understand the encoding then we can tackle the “decoding” problem. • inference – from activity to encoded property

Michael J. Black, © 2002 Neural Coding What are the possibilities? 1. Localist encoding in on/off response . 2. Rate coding. 3. Precise timing – pattern of spiking carries information. 4. Ensembles code information that individuals can’t. 5. Synchronous firing within and across ensembles (it is the interdependencies that matter).

• Localist view – each neuron codes a particular value • “computer”-like model where neurons are binary • at the low level cells represent things like orientation • at the high level they represent complex information • Problems?

Michael J. Black, © 2002 Neural Coding Population codes • distributed representation • information encoded in the overall activity of many cells • graded response – level of activity conveys information. Not binary.

Hubel & Weisel, 1962

source: Zemel & McNaughton, NIPS2000 tutorial rate = (# of spikes in time bin) / (length of time bin) Rate is related to the probability the cell will spike in a given time interval

Source: Rob Kass

Watkins & Berkley ‘74

Snowden ‘94

Performance Reaction Time

Eye Position BEHAVIOR

NEURAL SIGNAL

10ms

source: David Sheinberg

Possible targets Movement target Position Feedback Cursor

C e n t e r h o l d VideoM onitor

Digitiz iTnga blet Digitizing Tablet A B C

Georgopoulos, Schwartz, & Kettner, ’86. Moran & Schwartz, ‘99

Moran & Schwartz, J Neurophysiology ‘99

Data averaged over multiple animals and multiple trials.

Single cells from multiple animals. Average rate over RT and MT to each target (300-600 ms). Fit with cosine model. Infer firing conditioned on speed by assuming a bell- Moran & Schwartz, ’99 shaped function and factoring out direction 1/ 2 f j = b0 + bx sin(q j ) + by cos(q j ) effects.

Georgopuolos, Schwartz & Vetter ‘86

• Take each cell’s “preferred” direction and weight it by its current activity. • Summing all the weighted directions gives some measure of the current direction. • Populations computed from multiple animals.

ˆ q = åriqi i