Neural Decoding: Model and Algorithm for Evidence Accumulator

Inference

Thomas DesautelsUniversity College London

Gatsby Computational Neuroscience Group

Whitaker Enrichment Seminar, Budapest, 30 April 2015

Thomas Desautels, Gatsby Unit 2

Some (Engineering) Motivation: BrainGate

(Hochberg et al., Nature, 2012)

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Motivation• Areas of the brain have task-related neuronal

activity.o E.g.: hand location-related activity in motor and premotor areas.

• Patients with paralyzing conditions (SCI, ALS, etc.) could be aided by an interface which extracts information from the brain.o Brain – Computer Interface (BCI)

• Can test hypotheses about functions of specific brain areas

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Neural Decoding• A variety of signals have been examined:

o penetrating electrodes, EEG, ECoG

• How can we make sense of these signals? • Goal: Extract meaningful information from neural

recordings

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Neural Decoding II• Procedure (Bayesian):

o Create generative mathematical model of the neural activity• Expressive enough to capture the important features of the data• Links observed activity to latent variables (intent) we want to

decodeo Create a learning algorithm which can fit the model to an individual

patient’s data• Computational efficiency in learning (parameters) and inference

(online / single trial trajectory estimation)

• Variants of Kalman filtering have often been applied in BCI (e.g., Hochberg 2012, Shenoy, Carmena)

o This works best with many channels / neurons

• What if we don’t have enough data for that?

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Structure• If you have less data, you may be able to use a

more structured model• In my problem, we know a lot about:

o the task the being performed, and o how this problem should be solved

• Other problems may also fall into this low-data, known-structure regime

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Poisson Clicks:Evidence Accumulation

From Brunton et al., Science, 2013(C. Brody, Princeton)

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Model• Correctly solve task: evidence accumulator a(t)

o a(tc+) = a(tc

-) + sc, where sc = +1 for right, -1 for left

• Animal’s performance is suboptimalo Model the suboptimalities in a(t) and its inputs

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Brunton et al., 2013

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Goals• Have recordings

from PPC, FOF: o Neural spiking data y[t]

• Estimate the trial-by-trial trajectories a(t) of the evidence

• Learn the model parameters ϴGP , ϴGLM which describe each animal’s neural data

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Algorithm

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Results: Trajectory Estimates

• Given good parameters, get reasonable decoded trajectories

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Results: Learned Parameters

• Estimating ϴGP is much harder, and the subject of ongoing verification efforts

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Ongoing Work• Continue to verify the performance of the

algorithmo Parameter identification: Compare with other methodso Expand set of latents to include common noise processes

• Examine alternative algorithmso Variations on the existing Laplace EM algorithmo Variational EMo Expectation Propagation (EP)

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