1. Rita Pizzi Department of Computer Science University of Milan
ARTIFICIAL INTELLIGENCE MEETS NATURAL CONSCIOUSNESS: IS IT POSSIBLE TO CODIFY QUALIA ?
Rita Pizzi Department of Computer Science University of Milan

2. Brain dynamics research
Walter J. Freeman: relationship between olfactory stimulations and cahotic attractors in neural signals

3. Brain dynamics research
A novel model of Artificial Neural Network (ITSOM, Inductive Tracing SOM) can detect cahotic attractors in signals
It can show self-organization in single signals
It can analyze multiple signals together and show forms of coherence between them
It can identify attractors with specific codes
It attributes identical codes to similar attractors emerging from similar brain states, perceptions and emotions
Are these codes labels of qualia ?

4. The bionic creature (2006)
A biological neural network is connected to an Artificial Neural Network that decodes the neural signals
The hybrid (biological/electronic) brain operates a minirobot

5. The biological neurons
We used human neural stem cells cultured for 15 days in order to get mature neurons
Neurons grow creating a network
We cultured cells directly on Multielectrode arrays (MEAs) previously coated with a matrigel substrate.

6. The hybrid system
The activity of cells is recorded simultaneously from 64 channels
The system can record the cellular activity for a long time without damaging the cultures
It is suitable for our experiments that study the dynamical behavior of a whole neural network

7. The hybrid system
We stimulated the network of neurons by means of directional digital patterns, composed by 8x8 bitmaps
Each stimulation is followed by a 1s pause: during that, our ITSOM (Inductive Tracing SOM) Artificial Neural Network elaborates the signals

8. Results After training, the minirobot moves according to our commands
The statistical evaluation after the delivery of 25 random patterns shows an accuracy of 80.11% and a precision of 90.50%.

9. The SOM network Self-organizing Map: unsupervised learning
Two layers: one input layer and one competitive (in general bidimensional) layer
Each input neuron is connected to all the nodes of the competitive layer
Vector quantization: the n- dimensional input is mapped to a k-dimensional output (k<<n)

10. The SOM network Winner-Take-All rule
Distance calculation between the inputs (signals) xi and the nodes with weights Wvi.
The winning node is the neuron with minimum distance D and is rewarded with a positive increase.
Wv(t + 1) = Wv(t) + α(t)(D(t) - Wv(t))
The procedure is repeated until convergence: the final map classifies the input.

11. The ITSOM network
The sequence of the SOM winning nodes tends to repeat itself creating a time series of cahotic attractors. These attractors characterize univocally the input element that produces them.
The ITSOM network memorizes the time series of the winning nodes, then analyzes them with a z-score method.

12. The z-score
The cumulative scores for each input are normalized following the z standardized variable distribution
x = number of wins for each input
μ = average of scores
σ = standard deviation

13. The z-score Once a threshold τ is fixed , 0<τ<1 , we set
z = 1 for z > τ
z = 0 for z ≤ τ
So each configuration of winning nodes referring to a specific input is represented by a code composed by zeros and ones.
It is immediate to compare these codes and identify similar inputs.

14. Dynamic analysis of signals from intracranial electrodes
We tested the ITSOM on signals from intracranial electrodes provided  by the group of M. Massimini (University of Milan), comparing wakefulness and NREM sleep signals.

15. Dynamic analysis of signals from intracranial electrodes
Data were collected during wakefulness and we analyzed a time window of 30 seconds at 100Hz sampling frequency.
Data were pre-processed with band-pass filtering ( Hz) using a third order Butterworth filter
Artefacts were reduced with a Tukey windowed median filtering.

16. Dynamic analysis of signals from intracranial electrodes
We applied first the standard dynamic analysis, calculating Mutual Information and False Nearest Neighbor to compute embedding dimension and the best delay to reconstruct the multidimensional series of the signals
We also computed Recurrence Quantification graphs and other 8 parameters for each signal.
Then we displayed the attractors in the phase space.

17. Attractors in the phase space
The signal attractors show to be very different from one another, and for the same electrode the wakefulness and sleep signals show different grades of self-organization.

18. Attractors in the phase space
Filmato fasi.mp4

19. ITSOM attractors
Then we analyzed the signals by means of the ITSOM network
The time series of the winning nodes show distinct behavior for wakefulness and NREM sleep, and these states are similar in many electrodes

20. ITSOM attractors
Attractors are labeled with a binary code that identifies them univocally, but the flexibility of the ANN allows to attribute the same codes to similar dynamic events,

21. ITSOM attractors
Filmati attr.mp4 e attr2.mp4 da mettere insieme

22. The new project
In the new study we process signals from a 14 electrodes of the EMOTIV EEG system,  connected to immersive glasses that allow a realistic audiovisual experience.
The SNR of the Emotiv kit was evaluated in literature as equal  to or better than normal  clinical EEG headsets

23. The new project

24. The new project

25. The new project
A Matlab procedure synchronizes the acquired signals with various sensory experiences  presented in a video.

26. The new project
Filmato provaestratto.mp4 (tratto da prova.mp4)

27. The new project
Aim of the research is to test with an AI tool the  interconnections among brain areas in presence of sensory and emotional stimuli, and show how similar stimuli give rise to chaotic attractors identified with identical or similar codes.
We can process both individual signals and many signals simultaneously, highlighting the attractors in which  the corresponding dynamic system is evolving.

28. The new project
Examining more signals together, we can also detect coherence between signals
We can also highlight the time course of this form of coherence.
We can identify individual attractors with a unique code.
It is also possible to quantify these complex dynamic events with many parameters.

29. Our objectives
Test the  interconnections among brain areas in presence of sensory and emotional stimuli
Show how different sensory and emotional stimulations similar result in chaotic attractors identified with identical or similar codes.

30. Our objectives
This approach is close to that proposed by G. Tononi to identify qualia, but was never fully expressed up to now due to the lack of a robust quantification and representation method.
Among the countless number of possible binary codes we can distinguish different dynamic states with unique codes : we will call them qualia codes.

31. References
1. R. Pizzi, M. de Curtis, C. Dickson (2002), Evidence of Chaotic Attractors in Cortical Fast Oscillations Tested by an Artificial Neural Network , in: Soft Computing Applications, ed. Bonarisi Masulli Pasi, Physica Verlag Springer.
2. R. Pizzi, D. Rossetti, G. Cino, D. Marino, A.L. Vescovi, W. Baer (2009). A cultured human neural network operates a robotic actuator. BIOSYSTEMS, vol. 95, p
3. W.J. Freeman, Neurodynamics: An Exploration in Mesoscopic Brain Dynamics. Springer 2000
4. D. Balduzzi, and G. Tononi Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput. Biol. 4: e
5. G. Tononi, Consciousness as Integrated Information: a Provisional Manifesto. Biol. Bull. 215: 216–242. (December 2008)
6. D. Balduzzi, G. Tononi, Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework. PLoS Computational Biology, 1 June 2008 , Volume 4 , Issue 6.
7. Tononi, Integrated information theory of consciousness: an updated account. G. Tononi Archives Italiennes de Biologie, 150: , 2012 (Chapter on Attractor dynamics in the corticothalamic Complex)