Understanding Information Processing in Human Brain by Interpreting Machine Learning Models - PhD Defense Slides

Understanding Information Processing in Human
Brain by Interpreting Machine Learning Models
Ilya Kuzovkin
All models are wrong,
but some are useful. 
 
– George E. P. Box
Supervised by Raul Vicente

http://ircamera.as.arizona.edu/NatSci102/NatSci102/lectures/kepler.htm
https://www.helioseducore.com/keplers-laws-of-planetary-motion
Modeling is a well-proven way of obtaining knowledge
https://www.forbes.com/sites/andrewbusby/2019/03/04/new-pwc-survey-reveals-consumer-data-is-the-most-highly-valued/#6a084d3d640c
https://cs.hse.ru/en/bayesgroup/education/momlhttps://towardsdatascience.com/black-boxes-and-their-intrusion-620aa3c4c56b
MANUALAUTOMATED

Johannes KeplerTycho Brahe's observations
27 years
D A T A A L G O R I T H M M O D E L
Three Laws of Planetary Motion
MANUALAUTOMATED

27 years
Some data
A machine learning algorithm The model
Black 
box 
model
MANUALAUTOMATED

27 years
K N O W L E D G E
MODEL
=
Some data
IT'S THERE 
BUT 
HIDDEN
Black 
box 
model
MANUALAUTOMATED

27 years
K N O W L E D G E
MODEL
=
Some data
IT'S THERE 
BUT 
HIDDEN
Black 
box 
model
Machine learning can
uncover knowledge
Model interpretation is
required to articulate it
MANUALAUTOMATED

In life sciences model interpretation has a special significance
https://www.sciencenewsforstudents.org/article/life-earth-mostly-green
https://www.youtube.com/watch?v=Evsqy0a_Lrk https://www.sciencemag.org/news/2018/08/scientists-tweak-dna-viable-human-embryos
https://astronomy.com/news/2019/01/maybe-you-really-can-use-black-holes-to-travel-the-universe
https://www3.mpifr-bonn.mpg.de/staff/rbeck/MKSP/pictures.html
http://andysbrainblog.blogspot.com/2015/04/slice-analysis-of-fmri-data-with-spm.html
https://www.wired.com/story/the-rise-of-dna-data-storage/

Interpretability in ML
• Trust in model's decision
• Legal transparency
• Debugging

• Debugging
A model that can make correct
predictions about reality
Black 
box 
model

At this point the model
knows something about
the world that the
scientist does not
• Debugging
Black 
box 
model

the world that the
scientist does not
• Debugging
• Articulating the knowledge
about the underlying process
that the model has captured
Black 
box 
model

the world that the
scientist does not
• Debugging
• Articulating the knowledge
about the underlying process
that the model has captured
Black 
box 
model
Machine-learned models 
as scientific theories 
• Both capture and model observations
• Both make correct predictions 
on new observations
• Computers can generate and test
theories faster than humans
• In the big data regime there is not
enough scientists to sift through all
potential explanations of the data
• Algorithms have different bias than
humans, hence find different solutions

"Identifying task-relevant
spectral signatures of
perceptual categorization in
the human cortex" 
Scientific Reports, 2020
"Activations of deep
convolutional neural networks
are aligned with gamma band
activity of human visual cortex" 
Communications Biology, 2018
"Mental state space visualization
for interactive modeling of
personalized BCI control
strategies" 
Journal of Neural Engineering, 2020
Applying this principle in Neuroscience

Identifying task-relevant spectral signatures of perceptual
categorization in the human visual cortex
100 patients 
12,000 electrodes

100 patients 
12,000 electrodes
4,528,400 responses
400 images from
8 categories

100 patients 
12,000 electrodes
4,528,400 responses
400 images from
8 categories
x = {f1, f2, . . . , f3504, . . . , f7008}

100 patients 
12,000 electrodes
4,528,400 responses
400 images from
8 categories
x = {f1, f2, . . . , f3504, . . . , f7008}
Which parts of
this brain
activity are
generated by
the underlying
process of
mental image
categorization?

100 patients 
12,000 electrodes
4,528,400 responses
400 images from
8 categories
x = {f1, f2, . . . , f3504, . . . , f7008}
Feature importance map
Which parts of
this brain
activity are
generated by
the underlying
process of
mental image
categorization?

Multiple brain regions respond when a stimulus is shown,
but only a small fraction (5%) are predictive of a category.

While some locations are predictive of multiple visual
categories, others have narrow specialization.

It is believed that high-frequency activity
reflects the information processing
during high cognitive tasks. We show
that low-frequency activity is almost as
important for the task at hand.
Across all categories the classifier
relied on power decreases in different
brain networks, not only on the
increases to perform the classification

Activations of deep convolutional neural networks are
aligned with gamma band activity of human visual cortex

From previous fMRI research we knew that
there is a mapping between the hierarchies
Intracranial electrophysiological data allowed
us to learn when and at which frequencies

- simple features (mapped to lower layers of DCNN)
- complex features (higher layers)

Mental state space visualization for interactive modeling
of personalized BCI control strategies
Mental 
actions
Machine learning 
algorithm
Control 
commands
An external 
device
Brain 
signal

Mental 
actions
Machine learning 
algorithm
Control 
commands
An external 
device
Brain 
signal
The algorithm must
distinguish between
different mental actions
The user must produce mental
actions that are distinguishable by
the machine and do that consistently

Mental 
actions
Machine learning 
algorithm
Control 
commands
An external 
device
Neural 
signal
The algorithm must
distinguish between
different mental actions
The user must produce mental
actions that are distinguishable by
the machine and do that consistently
If the user could see machine's representation of his mental
actions he could find out which ones are suitable

L E V E L OF N E U R A L 
O R G A N I Z A T I O N
K N O W L E D G E R E P R E S E N T A I O N 
I N T H E M O D E L
Random Forests: 
feature-based rules
Self-Organizing Maps: 
topology of the samples
DNNs: distributed representations
over features (input or latent)
http://news.mit.edu/2014/optogenetic-toolkit-goes-multicolor-0209
Local responses of a
neural population
Hierarchy of visual areas
R E S E A R C H 
P R O J E C T
Kuzovkin et al., 
"Identifying task-relevant spectral
signatures of perceptual
categorization in the human cortex" 
Scientific Reports, 2020 (in review)
Kuzovkin et al., 
Kuzovkin et al., 
"Mental state space visualization for
interactive modeling of personalized
BCI control strategies" 
Correlates of
mental states 
("thoughts")

Interpretability adds a new axis for algorithm selection
Different machine learning algorithms capture knowledge
into different representations
Pick the one that will reveal the knowledge you are after, 
not the one that just gives the best performance on a metric.
Support Vector Machines:
points in the feature space
Self-Organizing Maps: 
topology of the samples
Random Forests: 
feature-based rules
DNNs: distributed representations
over features (input or latent)
Hidden Markov Models: 
states and transitions
Gaussian Processes: 
functions
https://www.superdatascience.com/blogs/the-ultimate-guide-to-self-organizing-maps-somshttps://math.stackexchange.com/tags/neural-networks/info
https://blog.toadworld.com/2018/08/31/random-forest-machine-learning-in-r-python-and-sql-part-1https://www.economind.org/single-post/2017/11/23/Is-Economic-Modelling-a-futile-attempt-before-the-everlasting-Uncertainty

Memory buffer
Curiously similar mechanisms in biological and artificial systems

Interpreting the mechanisms of machine learning models 
can shed light on the mechanisms of the brain
Memory buffer

• Modeling is a well-proven way of obtaining knowledge 
• Machine-learned models do capture the knowledge, but an additional step of
interpretation is required 
• In life sciences model interpretation has a special significance 
• Three examples of applying this principle in Neuroscience 
 
 
 
 
 
 
 
• Interpretability adds a new axis for algorithm selection 
• Interpreting the mechanisms of machine learning models can shed light on the
mechanisms of the brain
"Identifying task-relevant
spectral signatures of
perceptual categorization in
the human cortex" 
Scientific Reports, 2020
"Mental state space
visualization for interactive
modeling of personalized BCI
control strategies" 
Discussion

Understanding Information Processing in Human Brain by Interpreting Machine Learning Models - PhD Defense Slides

This would not be possible without
the constant flow of ideas born at the seminars, lunch breaks, discussions with Anna Leontjeva,
Tambet Matiisen, Jaan Aru, Ardi Tampuu, Kristjan Korjus and all lab members and alumni, students,
and co-authors,
the support and knowledge given by the University of Tartu and the Institute of Computer Science,
Raul Vicente establishing the Computational Neuroscience Lab and supervising my PhD studies,
Sven Laur further developing my understanding of machine learning,
the work done by Juan R. Vidal and the co-authors from Lyon Neuroscience Research Center,
Konstantin Tretyakov introducing me to the field of machine learning,
my parents and family, who showed me the value of knowledge and provided with the opportunity
to pursue it.
Thank you!

Neuroscience
Machine Learning & AI
Aims to understand
learning systems
and intelligence 
by analyzing 
and reverse
engineering 
the existing
example
Aims 
to build 
an intelligent 
system ground-up 
by figuring out the 
building blocks and rules 
of interactions between them
a special kind
of synergy that
leads to curious
similarities

Hippocampus
Experience replay Hierarchy of visual layers An efficient spacial code
Memory 
consolidation
Buffer
Deep Q-Learning
Experience 
replay
Layers of visual cortex
Deep convolutional neural network
• Layered structure
• Hierarchy of representational complexity
• Receptive fields convolutional filters
Self-emergent spacial code
Grid cells in entorhinal cortex
Grid-based code for location

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