Machine learning for biomedical science

@kordinglab

ML is getting popular in biomedical science

01992 2000 2008 2016

PublicationsPatents

10

100

1000

Trends in using ML for biomedical sciences

Year

Num

ber o

f occ

urre

nces

Typical Supervised ML setting

label

Model

Training

features predictions

sam

ples

sam

ples

features

Solve real problems

UPMC, Schwartz lab

Understand data

e.g. Bialek

Provide a benchmark

Your Model

Machine learning

See Jonas and Kording, Could a neuroscientist understand a microprocessor 2017

Being better than another model does not make a model true.

Model for brain

see Marblestone, Wayne, Kording, 2017

Use standard ML first

• Countless possibilities

• Biomedical scientists do not know how to do it well

• (Most) Machine learning scientists don’t either

• Kaggle competition winners do

State of the art ML

• Feature engineering based on domain knowledge, e.g. spike counts

• Handful of ML methods

• Ensemble method (xgboost) on top

• Or deep learning for other kinds of problems

Auto-ML is coming

• Approaches are sufficiently standard that this part can easily be automated, e.g. auto-SKlearn, auto-WEKA

• Implication: knowledge about details of ML techniques will become less relevant for biomedical scientists

Example uses of ML in Neuroscience

Bensmaia, Miller, 2014

DecodingEncoding

Decoding (Neurons-> movement)

SVRKalman Filt. XGBoost

Feedfrwrd NNSimple RNNGRU

LSTMEnsembleWiener Casc.

Wiener Filter

R2

1.0

0.6

Motor Cortex

WF WC KF SVR XGB FNN RNN GRU LSTM Ens

Finding generalizes

R2

1.0

0.6

R2

0.75

0.25

Somatosensory Cortex

Hippocampus

WF WC KF SVR XGB FNN RNN GRU LSTM Ens

WF WC KF SVR XGB FNN RNN GRU LSTM Ens

SVRKalman Filt. XGBoost

Feedfrwrd NNSimple RNNGRU

LSTMEnsembleWiener Casc.

Wiener Filter

Encoding (movements->neurons)

GLM Feedfrwrd NN XGBoost Ensemble

pseu

do R

20.2

0

Motor Cortex

GLM XGBFNN Ens

0.1

Finding Generalizesps

eudo

R2

0.2

0

Rat Hippocampus

GLM XGBFNN Ens

0.1

pseu

do R

2

0.2

0

Macaque S1

GLM XGBFNN Ens

0.1

Take home: Standard ML

• Work really well, fast

• Challenge people to get better results with brain intuitions

• Set baseline

• Ok, lets talk about non-standard now

Cryptography

Hello Bob h23nf9$bX

Plaintext m Ciphertext Ek(m)

h23nf9$bXHello Bob

m=Dk(Ek(m)) Dk=Ek-1

Ciphertext only attackWe can estimate p(m) from text

We know samples from c=El(m)

Generic solution strategy

P

For hypothesized decryption q=Dk(c)

D̂k = argminKL(P ||Q)

• Instructions change signals

• Instructions disturb subjects

• Brain changes

• Internal state not observable

Hard to get plaintext/supervised data from brain

typical distribution of movements

Use ciphertext only

P

Measured

With Daniel Wolpert, James Ingram, EBR

mis-aligned decodertypical movementdistribution distribution

mis-aligned decoder typical movement

Intuition

Qk P

neural data

Y

prior knowledge (kinematics)V

neural data

dimensionality reductionstep 1

Y

prior knowledge (kinematics)V

neural data

distribution alignment

dimensionality reductionstep 1

step 2

Y

Y` prior knowledge (kinematics)V

Estimating distributions

let . Then for every the density

• K-Nearest Neighbors (KNN) estimator:

V = [v1,v2, . . . ,vT ] vi

where is the distance between and its k-nearest neighbor in

⇢k(vi,V) vi

V

p(vi) /k

T⇢2k(vi,V)

See Poczos and Schneider, 2011, Loftsgaarden et al 1965

• the same approach can be used for estimating

rotation angle [o]

ground truth

KL-d

iver

genc

e [a

.u.]

0.9

1

180900-90-180

1.1

1.2

prior

no reflectionwith reflection

prediction

estim

ated

dens

ityki

nem

atic

s

examples of transformations of projected neural data

best alignment

Alignment

5 100time [s]

v x [a.

u.]

supervised within-subject DADground truth

-4

0

4

5 100time [s]

v y [a.

u.]

-4

0

4

Accuracy

0

0.2

0.4

0.6

0.8

1

across subjectwithin subject

DADsupervised

n.s

*R

2

* average

CIs: 95% bootstrap

• A ciphertext only decoder (unsupervised)

• uses no simultaneous recording of neural data and kinematics

• only requires a prior distribution over the kinematics

• Alignment compares well to a supervised decoder which has access to the supervised data

• Need more data!

• Next, a cipher text only attack. On your brain.

Take home: neural cryptography

Acknowledgements

• Ari Benjamin

• Joshua Glaser

• Mohammad Azar

• Eva Dyer

• Pavan Ramkumar

• Lee Miller

• Matt Perich

• Tucker Tomlinson

• Chris Versteeg

• Raeed Choudhury

Funding: NIH