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The Brain and the Modern AI

Drastic differences and curious similarities
Ilya Kuzovkin
We need to structure this conversation
Suggestion

David Marr's

Three levels of analysis
We need to structure this conversation
Suggestion

David Marr's

Three levels of analysis
Goal of the computation

What is the purpose of computation?
We need to structure this conversation
Suggestion

David Marr's

Three levels of analysis
Goal of the computation

What is the purpose of computation?
Algorithm and representation

What representations does the system use?

What processes are in use to manipulate representations?
We need to structure this conversation
Suggestion

David Marr's

Three levels of analysis
Goal of the computation

What is the purpose of computation?
Algorithm and representation

What representations does the system use?

What processes are in use to manipulate representations?
Implementation

How is the system physically realized?
Implementation
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
Action

potential

(aka Spike)
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
Action

potential

(aka Spike)
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
Action

potential

(aka Spike)
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
Molecular neurobiology
- different types of channels

- different receptors

- mechanism of 

neurotransmitter release
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
Action

potential

(aka Spike)
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
Molecular neurobiology
- different types of channels

- different receptors

- mechanism of 

neurotransmitter release
Dendritic spike
action potential can be
generated in the dendrite
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
Action

potential

(aka Spike)
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
Molecular neurobiology
- different types of channels

- different receptors

- mechanism of 

neurotransmitter release
Dendritic spike
action potential can be
generated in the dendrite
Timing
- no clock synchronization

- time of arrival is important
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
Action

potential

(aka Spike)
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
Molecular neurobiology
- different types of channels

- different receptors

- mechanism of 

neurotransmitter release
Dendritic spike
action potential can be
generated in the dendrite
Timing
- no clock synchronization

- time of arrival is importantTopology
Artificial networks are neatly
organized into graphs,
biological network is a mess
https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e
Output
Activation

function
Sum
Bias
Weights
Inputs
https://www.dreamstime.com/pyramidal-cell-cerebral-cortex-neurons-stained-golgi’s-silver-chromate-conic-shaped-soma-large-apical-image117240595
Axon terminals

(from other neurons)
Dendrites
Synaptic

connection
Cell

depolarization
Polarization

threshold
Action

potential

(aka Spike)
https://www.sciencedirect.com/science/article/pii/S0896627312005727#fig1
https://antranik.org/synaptic-transmission-by-somatic-motorneurons/
Molecular neurobiology
- different types of channels

- different receptors

- mechanism of 

neurotransmitter release
Dendritic spike
action potential can be
generated in the dendrite
Timing
- no clock synchronization

- time of arrival is importantTopology
Artificial networks are neatly
organized into graphs,
biological network is a mess
Power consumption
Brain uses 20 watt, one

Titan X — 250 watt
The brain cannot be doing backpropagation

(at least not the way we do it)
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
The brain cannot be doing backpropagation

(at least not the way we do it)
Forward pass
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
The brain cannot be doing backpropagation

(at least not the way we do it)
Forward pass
Backwards pass
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
The brain cannot be doing backpropagation

(at least not the way we do it)
Forward pass
Backwards pass
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
✓
✓
The brain cannot be doing backpropagation

(at least not the way we do it)
Forward pass
Backwards pass
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
✓
✓
The brain cannot be doing backpropagation

(at least not the way we do it)
Forward pass
Backwards pass
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
Forward pass
Neurons send spikes,

not real-valued outputs
https://www.cs.toronto.edu/~hinton/backpropincortex2014.pdf
✓
✓
The brain cannot be doing backpropagation

(at least not the way we do it)
Forward pass
Backwards pass
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
Forward pass
Neurons send spikes,

not real-valued outputs
Backwards pass
https://www.cs.toronto.edu/~hinton/backpropincortex2014.pdf
Neurons would need
bidirectional connections
with the same weight
Neurons would need to
send two types of signal
and estimate derivative
✓
✓
The brain cannot be doing backpropagation

(at least not the way we do it)
Forward pass
Backwards pass
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
Forward pass
Neurons send spikes,

not real-valued outputs
Backwards pass
https://www.cs.toronto.edu/~hinton/backpropincortex2014.pdf
Neurons would need
bidirectional connections
with the same weight
Neurons would need to
send two types of signal
and estimate derivative
✓
✓
✓'ish
✗
Quite different on the
level of implementation
<
✓'ish
✗
Algorithm and representation
Hippocampus and experience replay
Hippocampus
Hippocampus and experience replay
Hippocampus
Hippocampus and experience replay
Hippocampus
Hippocampus and experience replay
Hippocampus
Hippocampus and experience replay
Hippocampus
DQN algorithm has state memory buffer
and experience replay mechanism:



- store experiences while interacting 

with the environment



- randomly sample and replay 

experiences from memory while learning
Replay buffer
Vision: hierarchy of layers
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream https://www.jneurosci.org/content/35/27/10005
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence https://www.nature.com/articles/srep27755
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex https://www.nature.com/articles/s42003-018-0110-y
Vision: hierarchy of layers
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream https://www.jneurosci.org/content/35/27/10005
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence https://www.nature.com/articles/srep27755
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex https://www.nature.com/articles/s42003-018-0110-y
& multiple papers since:
Vision: hierarchy of layers
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream https://www.jneurosci.org/content/35/27/10005
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence https://www.nature.com/articles/srep27755
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex https://www.nature.com/articles/s42003-018-0110-y
& multiple papers since:
Layered structure of visual cortex
Neurons focus on certain areas of visual input
Lower layers process simple visual features, higher layers -- complex features
Vision: hierarchy of layers
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream https://www.jneurosci.org/content/35/27/10005
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence https://www.nature.com/articles/srep27755
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex https://www.nature.com/articles/s42003-018-0110-y
& multiple papers since:
Layered structure of visual cortex
Neurons focus on certain areas of visual input
Lower layers process simple visual features, higher layers -- complex features
Vision: hierarchy of layers
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream https://www.jneurosci.org/content/35/27/10005
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence https://www.nature.com/articles/srep27755
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex https://www.nature.com/articles/s42003-018-0110-y
& multiple papers since:
Layered structure of visual cortex
Neurons focus on certain areas of visual input
Lower layers process simple visual features, higher layers -- complex features
Vision: hierarchy of layers
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream https://www.jneurosci.org/content/35/27/10005
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence https://www.nature.com/articles/srep27755
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex https://www.nature.com/articles/s42003-018-0110-y
& multiple papers since:
Layered structure of visual cortex
Neurons focus on certain areas of visual input
Lower layers process simple visual features, higher layers -- complex features
Vision: hierarchy of layers
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream https://www.jneurosci.org/content/35/27/10005
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence https://www.nature.com/articles/srep27755
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex https://www.nature.com/articles/s42003-018-0110-y
& multiple papers since:
Layered structure of visual cortex
Neurons focus on certain areas of visual input
Lower layers process simple visual features, higher layers -- complex features
Model of the world: grid cells
The Nobel Prize in Physiology or Medicine 2014
"for their discoveries of cells
that constitute a positioning
system in the brain"
http://www.scholarpedia.org/article/Grid_cells
O'Keefe Moser Moser
https://deepmind.com/blog/article/grid-cells
https://www.nature.com/articles/s41586-018-0102-6
Model of the world: grid cells
Entorhinal cortex
The Nobel Prize in Physiology or Medicine 2014
"for their discoveries of cells
that constitute a positioning
system in the brain"
http://www.scholarpedia.org/article/Grid_cells
O'Keefe Moser Moser
https://deepmind.com/blog/article/grid-cells
https://www.nature.com/articles/s41586-018-0102-6
Model of the world: grid cells
Entorhinal cortex
The Nobel Prize in Physiology or Medicine 2014
"for their discoveries of cells
that constitute a positioning
system in the brain"
http://www.scholarpedia.org/article/Grid_cells
O'Keefe Moser Moser
https://deepmind.com/blog/article/grid-cells
https://www.nature.com/articles/s41586-018-0102-6
Model of the world: grid cells
Entorhinal cortex
The Nobel Prize in Physiology or Medicine 2014
"for their discoveries of cells
that constitute a positioning
system in the brain"
http://www.scholarpedia.org/article/Grid_cells
O'Keefe Moser Moser
https://deepmind.com/blog/article/grid-cells
https://www.nature.com/articles/s41586-018-0102-6
Model of the world: grid cells
Entorhinal cortex
The Nobel Prize in Physiology or Medicine 2014
"for their discoveries of cells
that constitute a positioning
system in the brain"
http://www.scholarpedia.org/article/Grid_cells
O'Keefe Moser Moser
https://deepmind.com/blog/article/grid-cells
https://www.nature.com/articles/s41586-018-0102-6
Model of the world: successor representations
https://julien-vitay.net/2019/05/successor-representations/
Model-free RL
+ fast

– inflexible to change
http://gershmanlab.webfactional.com/pubs/Stachenfeld17.pdf
Model of the world: successor representations
https://julien-vitay.net/2019/05/successor-representations/
Model-free RL
+ fast

– inflexible to change
Model-based RL
+ adaptable

– hard to learn
transition

probs
reward

probs
http://gershmanlab.webfactional.com/pubs/Stachenfeld17.pdf
Model of the world: successor representations
https://julien-vitay.net/2019/05/successor-representations/
Model-free RL
+ fast

– inflexible to change
Model-based RL
+ adaptable

– hard to learn
transition

probs
reward

probs
Successor representation for RL
discounted future

state occupancy

matrix
expected 

state

reward
Why learn the full model when we only 

care about the chance of getting into a state
http://gershmanlab.webfactional.com/pubs/Stachenfeld17.pdf
Model of the world: successor representations
https://julien-vitay.net/2019/05/successor-representations/
Model-free RL
+ fast

– inflexible to change
Model-based RL
+ adaptable

– hard to learn
transition

probs
reward

probs
Successor representation for RL
discounted future

state occupancy

matrix
expected 

state

reward
Why learn the full model when we only 

care about the chance of getting into a state
http://gershmanlab.webfactional.com/pubs/Stachenfeld17.pdf
Hippocampus Place cells
Gaussian distance to the location?
Model of the world: successor representations
https://julien-vitay.net/2019/05/successor-representations/
Model-free RL
+ fast

– inflexible to change
Model-based RL
+ adaptable

– hard to learn
transition

probs
reward

probs
Successor representation for RL
discounted future

state occupancy

matrix
expected 

state

reward
Why learn the full model when we only 

care about the chance of getting into a state
http://gershmanlab.webfactional.com/pubs/Stachenfeld17.pdf
Hippocampus Place cells
Gaussian distance to the location?
Adding constraints to the environment modifies the shape of the
place fields.

SR hypothesis is in agreement with the observation that rewarded
locations are represented by a higher number of place cells.
Dual DQN network
MERLIN Architecture
Predictive coding
Visual attention
...
Generative Adversarial Networks
Predictive coding
Successor representations
Attention in CNNs
Consolidation of experience

Novel vs. routine circuits
& &
& &
Replay buffer
Curious similarities on the level
of algorithm and representation
discounted future

state occupancy

matrix
expected 

state

reward
Goal of computation
Same goal, different strengths... merge!
Fetz Lab

University of Washington
NeuroChip Neuralink
Elon Musk

San Franciso, CA
http://www.csne-erc.org/sites/default/files/Neurochip.pdf https://depts.washington.edu/fetzweb/closed-loop-bci.html https://neuralink.com/
Same goal, different strengths... merge!
Fetz Lab

University of Washington
NeuroChip Neuralink
Elon Musk

San Franciso, CA
http://www.csne-erc.org/sites/default/files/Neurochip.pdf https://depts.washington.edu/fetzweb/closed-loop-bci.html https://neuralink.com/
Same goal, different strengths... merge!
Fetz Lab

University of Washington
NeuroChip Neuralink
Elon Musk

San Franciso, CA
http://www.csne-erc.org/sites/default/files/Neurochip.pdf https://depts.washington.edu/fetzweb/closed-loop-bci.html https://neuralink.com/
Goal of the computation

What is the purpose of computation?
Algorithm and representation

What representations does the system use?

What processes are in use to manipulate representations?
Implementation

How is the system physically realized?
The Brain and the Modern AI
Almost the same
Quite different
Comparable

here and there
Ilya Kuzovkin



ilya.kuzovkin@gmail.com

www.ikuz.eu
Neurotech
Sydney
meetup group
Podcast by

Paul Middlebrooks

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