Download artificial intelligence meets natural consciousness: is it possible to

ARTIFICIAL INTELLIGENCE
MEETS NATURAL
CONSCIOUSNESS: IS IT
POSSIBLE TO CODIFY QUALIA ?
Rita Pizzi
Department of
Computer Science
University of Milan
Brain dynamics research
Walter J. Freeman: relationship between
olfactory stimulations and cahotic
attractors in neural signals
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 ?
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
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.
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
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
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%.
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 ndimensional input is mapped
to a k-dimensional output
(k<<n)
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.
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.
The z-score
The cumulative scores for each input are
normalized following the z standardized variable
distribution
z
x

• x = number of wins for each input
• μ = average of scores
• σ = standard deviation
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.
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.
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 (0.5300Hz) using a third order
Butterworth filter
 Artefacts were reduced with
a Tukey windowed median
filtering.
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.
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.
K7
SLEEP
WAKE
N2
Attractors in the phase space
Filmato fasi.mp4
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
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,
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ITSOM attractors
 Filmati attr.mp4 e attr2.mp4 da mettere insieme
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
The new project
The new project
The new project
A Matlab procedure synchronizes the
acquired signals with various sensory
experiences presented in a video.
The new project
 Filmato provaestratto.mp4 (tratto da prova.mp4)
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.
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.
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.
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.
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. 137-144

3. W.J. Freeman, Neurodynamics: An Exploration in Mesoscopic Brain Dynamics. Springer
2000

4. D. Balduzzi, and G. Tononi. 2008. Integrated information in discrete dynamical systems:
motivation and theoretical framework. PLoS Comput. Biol. 4: e1000091.

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: 290-326, 2012 (Chapter on Attractor dynamics
in the corticothalamic Complex)