MADRAS

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The Most Complicated Complex System 
Prof. Dr. Kelly C. Iarosz
kiarosz@gmail.com
Chennai, 2023
105 Group Science
Webpage
1849-1936 Ivan Pavlov: conditioning concept
1904-1985 Donald Hebb: learning mechanism in neurons
1898-1969 Warren Sturgis Mcculloch e Walter Pitts: prove mathematically that artificial neural 
networks can interpret logical functions
1942-2011 David Rumelhart e James Macclelland: first model with formal rules
1912-1945 Alan Turing: Enigma machine coding
1960 William Bialek: information theory
1873-1941 Hans Berger: record EEG signals
1910-1977 Willian Grey Walter: measured delta waves during sleep
1943 Wolf Singer: gamma oscillations and perception
1929-2014 Geral M. Edelman: Medicine Nobel: Neural Darwinism
1936 Jean-Pierre Changeux: how does nicotine work on the brain
1701-1761 Thomas Bayes: Bayes's theorem
1821-1894 Hermann Von Helmholtz: speed of electrical impulses in nerves
1824-1880 Paul Broca: discovered the area of speech production
1848-1905 Carl Wernicke: discovered the area of speech understanding
1902-1977 Alexander Luria: modern neuropsychology
1929-2007 Paul Lauterbur: pioneer in MRI
1933-2017 Peter Mansfield: medicine Nobel MRI 2003
1945 David Van Essen: human connectome project leader
1961 Cornelia Bargmann: C. elegans
1963 Olaf Sporns: coined the term connectome
1916-2004 Francis Crick: optogenetics as a technique
1971 Karl Deisseroth: pionner in optogenetics
1937 Marcus Raichle: neural network in default mode
1969 Michael Greicius: neuron quiescence time
1913-1994 Roger Sperry: Nobel split brain patients
1891-1976 Wilder Penfield: brain stimulation, sensory and motor neural maps
1950 Anthony baker: Transcranial Magnetic Stimulation (TMS)
1945 John Rothwell: EMT (minutes)
1909-1991 Edwin Land: light stimuli
1940 Semir Zeki: areaa V4
1978-1965 Kurt Goldstein: phantom hand syndrome
1864-1915 Alois Alzheimer: degenerative disease
1755-1824 James Parkinson: shivering paralysis details
Info
About brain – time line
1890
W. James proposed that the interconnections between neurons and their functional
behavior were not static (W. James, 1890).
1923
Experimental evidence (monkeys) (Psychol. Bull. 30, 237, 1923).
1904-1985
Neuroscience with Donald Hebb (D. O. Heeb, 1949).
1964
Brain plasticity by areas (Science, 146, 610, 1964, J. Comp. Neurol., 123, 111, 1964).
2006
Experiments (Bi and Poo; Abarbanel, J. J. Neurosci. 18, 10464, 1998 and J. Neurophysiol.
96, 3305, 2006)
2012
Models and experimental data (Science 338, 60, 2012; Front. Synaptic Neurosci., 4, 1,
2012)
2012 e 2016
Hebbian plasticity and STDP modeling (Commun. Nonlinear Sci. Numer. Simul. 34, 12,
2016)
History
IFUSP, 2017
Models
Spike
Burst
Hodgkin Huxley:
CNSNS, 34, 12-22, 2016; Braz. J. Phys., 47,
678-688, 2017; Ner. Networks, 88, 58-64,
2017; Eur. Phys. J., 227, 673-682, 2018.
Cellular Automata:
Physica A, 430, 236-241, 2015; Physica A,
492, 1045-1052, 2018.
Hindmarsh-Rose:
Phys. Rev. E, 97, 022303, 2018; Chaos,
Solit. Fract., 101, 86-91, 2017.
Maps:
Chaos, 28, 081105, 2018; Chaos, 26,
043107, 2016; Chaos, 28, 085701, 2018;
Physica A, 496, 162-170, 2018.
Integra e Dispara Adaptado:
Chaos, 29, 043106, 2019; Front. Comput.
Neurosci. 13, 19, 2019; Physiol. Meas., 39,
074006, 2018; Neural Networks, 90, 1-7,
2017.
Structure
Physica A, 391, 819-827, 2012.
N1 N2
N6 N3
N5 N4
N1 N2
N6 N3
N5 N4
N1 N2
N6 N3
N5 N4
N1 N2
N6 N3
N5 N4
N1 N2
N6 N3
N5 N4
Data
Physiol. Meas. 39, 074006, 2018.
Chaos, Solitons and Fractals 101, 86-91, 2017.
Chaos 26, 043107, 2016.
Chaos, 22, 043149, 2012.
Popovych et al. (2013), Hebb (1949), Izhikevich (2004), Hodgkin&Huxley, 1952.
• Capacity of HH neuron in a network to change temporarily or permanently their
connections and behavior, the so called STDP, as a function of their synchronous
behavior.
• STDP of excitatory and inhibitory synapses driven by Hebbian rules.
• Final state of networks evolved by a STDP depend on the initial network configuration.
• Initial all-to-all topology evolves to a complex topology.
• External perturbations can induce co-existence of clusters, those whose neurons are
synchronous and those whose neurons are desynchronous.
• Reveals that STDP based on Hebbian rules leads to a change in the direction of the
synapses between high and low frequency neurons, and therefore, Hebbian learning
can be explained in terms of preferential attachment between these two diverse
communities of neurons, those with low-frequency spiking neurons, and those with
higher-frequency spiking neurons
INFORMATION
One of the most important properties of
the mammalian brain, is synaptic plasticity
and it is the changing of the structure,
function and organisation of neurons, in
response to new experiences.
Nature, 33, pages18–41 (2008)
Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms
Results
Results
Fig2 - Bistable Fig2 - Autapse
Fig2 - Emergence
Compare the structural 
connection network of 
a healthy brain and a 
brain affected by 
Alzheimer's disease 
with artificial small-
world networks
� Our results indicate that network quantifiers can be helpful to
identify abnormalities in real structural connections, for instance
Alzheimer’s disease that disrupts the communication among
neurons.
� One of our main results is to show that the network indicators of
the Alzheimer brain are almost identical with the small-world
network, except for the assortativity.
Results 
Human 
data
Physiol. Meas. 39, 074006, 2018.
Chaos, 22, 043149, 2012.
Now
�Networks
�Models: burst, spike, mini brain
�Data: simulation, real data
�Terms: STDP, STP
�Synchronization: exchanges, couplings,
conductance, neurodegenerative diseases/or
not, injuries, neuro drugs (ini/exc)
�Diseases (suppression, interpretations, control
of application parameters)
�Lesions (proliferation control)
Research 
innovation
Norbert Hirschhorn (1964)
Helped save 50 million lives
sugar salt water
Proportion and 
patience
https://www.bbc.com/news/av/health-28585774
kiarosz@gmail.com
105 Group Science