The document presents a lecture by Prof. Dr. Jelena Mladenović on brain-machine intelligence, focusing on the evolution and future of brain-computer interfaces (BCIs), computational brain models, and the integration of cognitive and affective computing. It highlights ongoing projects in brain research, the mechanics of BCIs, and the role of machine learning in enhancing human-computer interactions. The discussion also encompasses the implications of neuronal dynamics and Bayesian inference for developing advanced artificial intelligence systems.

1. Brain-Machine Intelligence :
Past, Present and Future
Prof Dr Jelena Mladenović
Computing Faculty, Union University
Belgrade, Serbia

2. About the speaker
PhD in Brain-computer Interface in Inria and Inserm, France
Research Fellow in Reichman University, Israel
Associate professor at Computing Faculty, teaching:
1. Brain-computer Interface
2. Physiological Computing
3. Robotics

3. Ongoing projects on the brain
1. Connectomics – 3D road map of the brain (MIT)
2. Brainbow – colourful genetically modified brain (Harvard)
3. Optogenetics – light activated brain circuits (genetically modified)
4. Computer simulations of the brain :
– EU Blue Brain Project – EPFL
– Obama BRAIN Initiative – USA
5. Brain-computer/machine Interfaces – interacting with the machine
via only neural activity

4. Today’s topic – Brain-Machine intelligence
1. Brain-Computer Interface
- Bridge between machine and computer intelligence
2. Computational brain models
a. From fundamental neuronal circuit to ANN
b. Active (Bayesian) Inference
3. Brain (Cognitive) + Visceral (Affective) Computing
– next level of machine intelligence
Present
Past
Future

5. Today’s topic – Brain-Machine intelligence
1. Brain-Computer Interface
- Bridge between machine and computer intelligence
2. Computational brain models
a. From fundamental neuronal circuit to ANN
b. Active (Bayesian) Inference
3. Brain (Cognitive) + Visceral (Affective) Computing
– next level of machine intelligence
Present

6. Brain-Machine Interface
“A system that measures neural activity
and converts it into artificial output that
replaces, restores, enhances, supplements,
or improves natural neural output and
thereby changes the ongoing interactions
between the central nervous system and its
external or internal environment”
Wolpaw, Handbook of Clinical Neurology 2013.

7. Brain-Computer Interface (BCI)
A system as a type of human-computer
interaction in real time, which uses neural
impulses as input data, and displays
perceptual events at the output. It is used
in medicine as a neurorehabilitation tool, in
psychotherapy, interaction, art, gaming,
transport, security, marketing, etc.

8. BCI
Measure brain
activity with e.g. EEG

9. BCI
Measure brain
activity with e.g. EEG
Filter and process
signals

10. BCI
Measure brain
activity with e.g. EEG
Filter and process
signals
Machine learning
Calibration or
training
Testing

11. BCI
Measure brain
activity with e.g. EEG
Filter and process
signals
Machine learning
Calibration or
training
Testing
Machine control

12. BCI
Measure brain
activity with e.g. EEG
Filter and process
signals
Machine learning
Calibration or
training
Testing
Machine control
Influences human

13. BCI
Machine learning
Calibration or
training
Testing
Human learning
Ideally the machine should
learn the same way the
brain does
But how?

14. Today’s topic – Brain-Machine intelligence
1. Brain-Computer Interface
- Bridge between machine and computer intelligence
2. Computational brain models
a. From fundamental neuronal circuit to ANN – micro level
b. Active (Bayesian) Inference – macro level
3. Brain (Cognitive) + Visceral (Affective) Computing
– next level of machine intelligence
Past

15. Hodgkin-Huxley
Experiments with Squid axon (1939)
H-H set of differential Equations
1952 (Nobel Prizes)
Image courtesy of hans hillewaert

16. Neuron I/O device
INPUT
Soma
Dendrites
Axon
Synapses (Excitatory, Inhibitory)

17. Neuron I/O electrical device
INPUT OUTPUT
Soma
Dendrites
Axon
Synapses

18. Neuron as R-C Circuit
I
IN
OUT
V – response Voltage of the cell
time
Neuron
R C
OUT
IN
I

19. Temporal summation
I
IN
OUT
V – Temporal summation (in dendrites) analog signal
time
Neuron

20. Action potential – spike (0 or 1)
I
IN
OUT
V – Temporal summation
time
Neuron
Threshold
around 10 mV
relative to resting
potential

21. Action potential – spike (0 or 1)
I
IN
OUT
V – Temporal summation
time
Neuron
Threshold
Digital signal

22. INPUT OUTPUT
Synapses as DA converter
Axon
DA, AD Converters
Temporal summation
Soma
Soma as AD converter

23. ANN (Perceptron)
1958 Rosenblatt – Psychologist at Cornell University

24. ANN (Perceptron)
1958 Rosenblatt
1
1
0
0

25. ANN (Perceptron)
1958 Rosenblatt
Excitatory
Excitatory
Inhibitory
1
1
0
0

26. ANN (Perceptron)
1958 Rosenblatt
Excitatory
Excitatory
Inhibitory
+ 5
+ 2
- 1
- 4
weights
1
1
0
0

27. ANN (Perceptron)
1958 Rosenblatt
Excitatory
Excitatory
Inhibitory
+ 5
+ 2
- 1
- 4
weights
= 4 < 6
Bias
0
Threshold else 1
1
1
0
0

28. ANN issues
Has impressive results, but needs:
- Many layers
- Many training data
- Stronger, bigger GPUs
(more power consumption)
- In BCI we do not have enough data
- Data changes in time (adapts)
We need adaptive algorithms

29. 1. Brain-computer interface
2. Computational brain models
a. From fundamental neuronal circuit to ANN – micro level
b. Active (Bayesian) Inference – macro level
3. Brain (Cognitive) + Physiological (Affective) Computing
– next level of machine intelligence
Today’s topic – Brain-Machine intelligence

30. What do you see?

31. Bayesian brain
“The brain has no colour, no sound, it
has only electrical impulses…”
It makes approximations of perceptual
events and creates an image (mental
model) of the outside world
Anil Seth

32. Bayesian Inference
The brain constantly updates a
probabilistic model of the
environment and predicts
future events
Rao & Ballard. Nature neuroscience,
2(1):79, 1999

33. Cat Carousel
Experiment by
Held, R. and Hein A. (1963)
Action and perception are
intrinsically linked
Only by moving
one develops normal sight

34. Perceptual Affordance
Gaver 1996
An object affords finite set of possible actions
to be performed, given the experience
The brain optimizes the response time

35. Active Inference
ACTION
We perform such
Karl Friston. The free-energy principle: a unified brain theory?
Nature reviews neuroscience, 11(2):127, 2010

36. Active Inference
ACTION
We perform such
which maximizes the epistemic value and
maximize our utility function (goal)
Karl Friston. The free-energy principle: a unified brain theory?
Nature reviews neuroscience, 11(2):127, 2010

37. Active Inference

38. Application of Active Inference
Mladenovic et al. JNE 2020

39. ANN + Active Inference
- NN for specialized tasks and
- Active inference for generalization and adaptation
- Combining both micro and macro levels to achieve optimal AI

40. Cognitive-Affective Machine Intelligence
Future of Machine Intelligence
Humans are not only brains
Our visceral physiological signals play a great role
in learning and decision making
(James-Lang theory, Damasio etc.)
Affective Computing – Rosalind Picard (MIT)

41. Brain-Machine Intelligence :
Past, Present and Future
Prof Dr Jelena Mladenović
Computing Faculty, Union University
Belgrade, Serbia
https://jmladeno.net
emails: jmladenovic@raf.rs ; jelena.mladenovic@neurotechx.com