Machine intelligence, particularly Neural Networks are inspired by the underlying mechanisms of the brain. On the other hand, we are using machine learning and AI to have a better understanding of the brain, and its plasticity. Brain-computer interface is a system that enables a person to manipulate a machine by using only neural activity. It extends the boundaries of the nervous system to external devices as additional virtual limbs. However, in order to work successfully, the system needs both machine and human learning to take place, as a form of partnership or symbiosis.
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.
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
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
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
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
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