Olympiads Of The Mind 2017(S.T.E.P.S. Foundation)
Presented to meeting in Crete, Greece, 2017
Systems of the brain responsible for the Herd Mentality and the Acceptability of Diversity may determine If we can all live together. H. Weinberg, PhD, OBC, Professor Emeritus, Simon Fraser University, British Columbia, Canada.
The developing technology for an understanding of distributed, dynamic and interacting systems of the brain will be able identify the unique characteristic of each individual. This has significant implications for the enhancement of plasticity of the brain and for reduction of the herd mentality, and for the future of the human species in respect to the acceptance of diversity, and may be the beginning of the way in which we can All Live Together.
Good morning everyone it's a pleasure to be here and to discuss how an understanding of the physiology of our living system might give us a better understanding of we how could all live together - and the related implications.
it might be interesting to identify two characteristics of the human brain system, and perhaps of other living things as well, in order to make discuss and understanding, of the conflict between societies, that we now
see and which has been prevalent for thousands of years - conflicts that resulted in, and continues to result in, us killing each other.
One of systems of the brain which is clearly characteristics of human behaviour, and presumably of all living systems that have a brain, is the Herd Mentality.
And the other system, which initially appears to be contradictory to the herd mentality, is the Diversity of Brain Function, the reality that each brain is different, and that these differences are responsible for the individuality of each person.
And so, from an initial perspective there seems to be competing systems in the human brain systems, - the herd mentality - and the diversity of individuality.
The question is: How can diversity and the heard mentality function cooperatively - and - if there is an evolving technology to change the brain, in order to modify these tendencies, should we proceed with that?
The Herd Mentality: Humans seem to mimic behaviours similar to a flock of herding animals, and may not realize that their decisions and actions are largely based upon the requirement to follow what their ‘herd’ is doing.
The German philosopher Friedrich Nietzsche was, I think, the first to critique what he referred to as the “herd instinct”, in any human society. Modern psychological and economic research has identified herd mentality in humans as an explanation of
why large numbers of people may act in the same way at the same time. Each herd includes a leader and the herd follows that leader -
The modification of this instinct, i.e., modification of the brain systems responsible for herding, could be
implemented in the future, and it might be possible for the herd mentality to be modified such that, what seems to be a characteristic of the herd mentality, the acceptability for killing members of a different heard.
However, what would be the consequences if one were to eliminate the heard mentality in its entirety - the consequences of having no leaders, and no herds? Would humans survive and develop if there were no goals in which groups of humans participated, and if there were no leaders?
How can individuality and herd mentality work together?
One solution to control negative contributions of the herd mentality is to increase plasticity of the brain and consequently the acceptability of diversity, - a critical element in the determination of the individuality of the individual.
Therefore, can the recognition, facilitation, understanding, and acceptability of the plasticity of brain systems as a method for establishing diversity, i.e, of continuously changing, distributed and interacting systems - of the diversity of brains, positively modify the herd mentality?
The history of brain physiology in the last 100 years is actually the history of a pendulum swinging, from the concept of localized function, to the concept of distributed function, through physically distributed, interacting and dynamic systems.
Until recently individual differences in brain function were described primarily in terms of deviations from the average, from a central tendency.
However, recently it has been possible to begin an understanding of how systems in the brain changes as the result experience, how these systems are responsible for different people being different people.
The identification and understanding of brain systems related to input, output, and neuroplasticity has expanded significantly as the result of developing technologies like Magnetoencephalography (MEG), Functional Magnetic Resonance Imaging (FMRI) and Positron Emission Tomography (PET) - and the use of non invasive brains stimulation (NBS) the use of magnetic fields.
Because of their time resolution th, and their ability to directly measure the function of interacting neuronal systems in real time these technologies, and others, can be used to measure individual differences in the brain.
These, and others methods, like non-invasive brain stimulation (NBS) using magnetic fields, and an increasingly advanced technology of brain imaging, will be able to recognize and document individual differences - and predict how those differences may impact the current and the future life of an individual, including the interaction of that individual with others, that is, the degree of their participation in the herd. An understanding of distributed, dynamic and interacting systems of the brain will be able identify the characteristic of individuals - and this has significant implications for the future of the human species in respect to the acceptance of diversity.
The history of attempts to understand the individuality of brain function date back to 1700 B.C when Aristotle, thought the heart, not the brain, was the location of intelligence for all individuals. Ancient Egyptians were probably the first to distribute written accounts of anatomy of the brain, and that anatomy was considered to be the same in all individuals, and the primary determinant of function in the brain. and there was very little understanding of the neurophysiological basis of individual differences.
As technology progressed specific anatomical structures of the brain were identified as being responsible for different types of complex behaviour for all individuals.
The history of brain physiology in the last 100 years is actually the history of a movement from the concept of localized function - primarily but not exclusively for cortical function - to the concept of continually changing, distributed, interacting and dynamic systems. The early concepts of specific localized functions attributable to specific areas of the brain, has a long history that includes studies of brain stimulation and lesions of the brain including the use of frontal cortex lobotomies to treat behavioural disorders.
Frontal lobotomies, temporal lobectomies and other massive lesions were justified based on changes in behaviour that resulted from those lesions, without any clear understanding of how the brain functioned in respect to those behaviours.
In the late 50’s I was doing “cutting edge research”, i.e., the cutting of medial and lateral hypothalamus to study hunger, thirst and sex, (it seems as if hunger, thirst and sex have always been related to each other, in one way or another).
At that time, in the 50's, I wondered, to myself of course, whether a complex function like hunger, or sex, could be attributed to only one part of the brain, when the history of each individual clearly determined, to a large extent, the way in which those functions were individual to the individual, and until relatively recently, brain imaging of activity was primarily thought of as a method for measuring only localized function.
An example was the first measurement of magnetic fields related to brain function, initiated by David Cohen in 1968
The recording was done using a copper induction coil as the detector. The idea was that electrical currents produce orthogonally oriented magnetic fields, and the net currents can be modelled of as current dipole, with a specific function, a location, orientation, and magnitude in the brain. This was, I think, the beginning of the idea to use "dipole sources" in the brain to explain different types of information processing.
The SQUID was then introduced which was used Josephson Junctions to detect very small magnetic fields, the Superconducting Quantum Interference Device.
MEG began with the assumption that it was a method for detecting dipoles. Dipoles were originally defined as an electrical source with a particular spatial orientation. The idea was that the dipole, located in a
specific place, was responsible for complex information processing and response output.
Thus the idea of a single dipole came to be the model of the sources of complex behavior. Of course an argument at that time was, and still is, whether the dipole is the location of "a source", or rather it is an index of a complex distributed system.
Later, it became increasing clear that the dipole was not a viable explanation of brain function responsible for complex behaviour.
The dipole was then considered as the ‘centre of gravity’ of a system, and was primarily considered an estimate, or average, of multiple sources, with respect to both direction and strength.
Brain imaging began ask the question as as to whether a dipole could be responsible for complex processing and there was the emphasis on understanding and focus on the distribution of function in the began to emerge.
When I was working at the Burden Neurological Institute with Grey Walter, in Bristol England, during the late 60s, a period of social revolution within the Western world, we were doing multifocal stimulation of frontal white, with 64 gold electrodes that were implanted for as long as 6 months. This was to treat obsessive compulsive disorders. We worked from the midline outward to lateral structures, and observed behavioural changes specific to individual obsessions (Weinberg, et. al, 1969).
Increasing Complexity as a
Function of Time
We thought that we were reorganizing the interaction of subcortical and cortical system, facilitating plasticity, but of course we really did not know what those systems were.
We were doing what most scientists did at that time, and what is being done today to a large extent, i.e., trying out a treatment and determining its effectiveness, through an observation of its effect on behaviour.
Although our knowledge of distributed function was limited we all began to realize that we did not have a real understanding of complex distributed brain function, and we hoped to get insight into the nature of those systems through studies of the distribution of evoked and spontaneous activity.
In an attempt to study complex systems that were not the immediate result of stimulation we developed the concept of "emitted cerebral events" - events in the brain that reflected brain activity related to specific events when the events were not present - memories.
The idea was to develop, for each individual, a template of the activity that resulted from the presentation of stimuli that required a response, and then to use that template
for a match to what was happening in the brain when the patient was thinking about an expected but absent stimulus - it was called the Recognition Index.
Of course one thing we found is that the 'template' was different in different parts of the brain, and for different people - but there was a template that reflected what that patient was thinking about.
Basically the endeavour was to use pattern recognition (Weinberg, 1972) as an alternative to signal averaging.
The idea we pursued in those early days was that an event in the brain was an electrophysiological change that could be described as a "template" for a "thought", a distributed system that could be discovered through the measurement of what was going on in a distribution of electrodes, over the whole head, when a particular type of information was being processed (Weinberg, et al, 1972).
The reason I mention this now is that it is an example of a methodology that attempted to describe complex distributed brain function, using spontaneous activity, related to information processing - activity that was different for each individual.
We published some of this in the early 70's with Grey Walter, Ray Cooper, and with Rosa Gombi who was visiting from, what was then, the Soviet Union.
However, the really important element of this research was that, for us, it began the attempt to identify patterns of brain activity related to information processing that was specific to individuals - the concept that measurement of brain activity related to the processing of the same information may be different for different individuals.
I would like to expand a bit on this.
I often think of Mozart. Can you imagine how a pianist can remember 10 different concerti – or more - and produce the frequently varied motor output related to those memories, i.e., to produce the same auditory concept?
Clearly a distributed programme must exist that includes the use of sensory and motor systems, as well as the complex processing, and memory that occurs when each performance is almost, but not identical to, the last. And of course each musician could have a different pattern of brain activity that results in the same output, and that output is the characteristics of "that individual". Or think about something "a lot simpler". A person walking from point 'a' to point 'b'. Each person has their own gait, which results from their own input, processing and output systems, although they "perform the same action" As we all know you can identify someone by their gait - and of course, if one of those legs was amputated would you identify "walking" for that person as being located in that leg?
The pendulum was clearly shifting to distributed systems in the late '90's. For example, Paul Nunez book on Neocortical Dynamics (1995) and and Gerald Edelman’s book, The Remembered Present (1989), were important in the re-development of ideas of distributed function. Edelman described what he called re- entrant neural networks that were distributed systems that included the interaction of cortical, thalamic and brain stem activity.
And then there was the application of Chaos Theory as another example (Christine, eal., 1990). Chaos theory applied to brain function considers the brain a complex, high dimensional dynamic system of billions of interacting sub- systems
Chaos theory applied to brain function considers the brain a complex, high dimensional dynamic system of billions of
The underlying idea of using chaos theory to study brain function is that complex function requires an interaction of widespread, spatially distributed functions. The assumption is that everything within the system is interacting.
This is illustrated by the Butterfly Effect, whereby a single butterfly flapping its wings, e/g/, a change in a system of the brain as a result of experience, can cause a tornado in the rest of the brain.
Butterfly Effect, whereby a single butterfly flapping its wings, e/g/, a change in a system of the brain as a result of experience, can cause a tornado in the rest of the brain
And then there is the application of nonlinear dynamical systems theory - Christine Skarda and Walter Freeman (1990) "We have coined the term 'cooperative neural mass' to express this level of neuronal functioning. It is largely thanks to the analytical tools of nonlinear dynamics that we have been able to measure and interpret these spatially extended patterns of activity in the nervous system. Our approach has been to record and measure the neural activity patterns within the olfactory bulb before, and again after a subject had learned to discriminate two or more sensory stimuli, in order to identify precisely the differences in activity patterns that serve to distinguish and classify the neural events with respect to the discriminanda".
And then in the early 90's emerged the NeuroChaos Solutions technology is based on Chaos Theory, a scientific principle describing the unpredictability of systems. Its premise is that systems sometimes reside in chaos, generating energy but without any predictability or direction. They are highly sensitive to initial conditions as illustrated by the Butterfly Effect, whereby a single butterfly flapping its wings can cause a tornado halfway across the world. Examples of these systems include the earth's weather system, the behavior of water boiling on a stove or the migratory patterns of birds. While their chaotic behavior may appear random at first, chaotic systems can be defined by a mathematical formula, and they are not without order or finite boundaries. Such systems considers the brain a complex, high dimensional biodynamic system of billions of interacting elements, with "Deterministic Chaos".
And then of course the concept of brain plasticity emerged as a focus - that there are changes in brain's structure and function as a result of experience - and genetics, that experience can produce dynamic changes in neuronal systems, and the interaction of distributed systems.
The whole concept of plasticity includes the assumption that fixed and unchanging localization is not viable, and of course the importance of understanding plasticity is critical to an understanding of input, interacting and output systems.
Eberhard Fuchs and Gabriele Flügge (2014), in their discussion of adult plasticity of the brain clearly summarized the new approach to an understanding of neuronal plasticity - "neuronal plasticity” can stand not only for morphological changes in brain areas, for alterations in neuronal networks including changes in neuronal connectivity as well as the generation of new neurons (neurogenesis), but also for neurobiochemical changes".
And now there is an emphasis on Brain Plasticity as a critical element in determination of the diversity of individuals. Brain plasticity is now clearly recognized as normal brain function related to the acquisition of behaviour and information processing - and the utilization of that information. Plasticity is now recognized a fundamental property of the brain and has been implicated in various psychiatric and neurodegenerative disorders including obsession, depression, compulsion, psychosocial stress.
Plasticity is clearly a characterization of the individual as an individual - it may be that plasticity actually defines the character of the individual, and it is now clear that plasticity has become a real challenge to the concept of anatomically localized sources in the brain. How can plasticity of the brain be facilitated. It is clear that the complexity of the brain's response to input, and to the organization for output begins at an early age and is facilitated by variability of experience and has an influence on the capability of fluid intelligence, processing speeds, and the utilization of cognitive abilities (Rogier A. Kievit, et.al., Neuropsychologia, Vol 91, 2016, 186–198). Variability of experience in early ages and the practice and abilities for multi-tasking are of primary importance in development of plasticity. The ability to rapidly change attention and preparation for output, and to evaluate and deal with distractors varies between individuals and training, and experience with multitasking, changes brain systems to improve performance of multitasking as a result of changes in the interaction of systems in the brain The developing brain in a multitasking world (Mary K. Rothbart, Michael Posner, Developmental Reviews, Vol 35, 2015, 42-63).
In the 70's, after coming back from the UK, where we were implanting electrodes in humans, for long term stimulation to treat obsessive compulsive disorders, it was clear that the stimulation influenced some form of plasticity that resulted in the influence of memories on current systems of the brain but what was needed was a better method of recording the distributed function of dynamic systems.
Now back to the Brain Imaging, in the context of distributed dynamic systems - that are unique to individuals
- and the implications of this for the character of the human species.
MEG introduced of a new approach to the analysis of complex information processing because of its time resolution, and its ability to directly measure function in real time, without the use of any high frequency or chemical impositions on that function for the measurement of blood-oxygen-level dependent contrast imaging (MRI, fMRI, DTI).
As indicated the approach of MEG, and other imaging technologies was initially focused on finding source localizations that were part of the 'common brain'. When I returned from the Burden Neurological Institute, Max Burbank and his group were developing a single channel MEG system, and we began to collaborate in the development of studies of distributed systems - and at that time by multiple recordings using an MEG that mechanically moved around the head.
Of course at that time the idea was that there were fixed systems for processing input which could be identified by the configuration of dipoles that were computed, using different locations of the sensor
The CTF MEG technology began its development in 1970. In those early years the underlying SQUID technology was originally employed in geophysical exploration - at that time everyone was initially looking for dipoles in the brain.
I remember asking if MEG could discriminated between excitatory and inhibitory systems. Inhibitory systems are of course critical in an understanding of distributed interacting systems and in the 70's I organized several symposia through the Canadian Psychological Association, to discuss the question of whether the contingent negative variation (CNV) was a unitary potential
Current technologies are now focusing on the new look in brain imaging, i.e., the focus on Individual Differences in Brain Systems - and the control and modification of individuals capabilities. For an understanding of individuals is it time to stop describing an individual with respect to the central tendencies, and standard deviations. Each brain is different and that is why we are different people. What will be the result of new technologies that will be able to control those individual properties of systems in the brain.
One of the new efforts to understand individual differences, and the use of those differences, is the consortium of Washington University, University of Minnesota, and Oxford University to begin a comprehensively mapping human brain circuitry in a target number of 1200 healthy adults using cutting- edge methods of noninvasive neuroimaging - to understand brain connectivity, its relationship to individual differences in brain circuitry and to behavior - the Connectome Project (https://humanconnectome.org).
From current studies the apparent primary goal of the Human Connectome Project appears to be an
understanding of individual differences - differences that actually define the individual - the personal characteristics of information processing.
Coordination Dynamics, the idea of Scott Kelso is another important example of the new look with respect to understanding the dynamics and plasticity of brain systems. This new look considers brain function as a dynamic coordination within and between systems of the brain, that establishes the dynamic processing of input, and the preparation of output. The cortical dynamics may result in both cooperative and competitive interactions within and between systems that result in cognition (Steven L.Bressler and J.A. Scott Kelso, 2016, Coordination Dynamics in Cognitive Neuroscience Neurosci., 2016 | https://doi.org/10.3389/fnins.
Another example is the use of fMRI and DTI in projects like Human Connectome, but the time resolution of systems, for fMRI and DTI of the brain, are only indirect measurements of electrical changes that are actually occurring on the order of milliseconds - and therefore they are only an approximation of real-time dynamics.
The use of graphic analysis of the correlations of activity in distributed dipole locations, is also a new methodology for a differentiation of systems, e.g, Ye, 2014.
The new look for the recognition in distributed systems, includes the reconstruction of activity in either resting or task based observations, and then the observations into different frequency ranges are filtered to extract regional phase synchrony, calculated within and between regions.
When we introduced the MEG to the British Columbia Downs Syndrome Foundation the funding and enthusiasm we encountered was built around the idea that the MEG could identify the characteristics of specific individual information processing, and motor capabilities, for each child who had Down's Syndrome
- through the measurements of brain function.
Of corse the complexity of input and output is an index of the complexity of the system. However, the concept was to individualize training based on brain imaging, and to maximize the capabilities and contributions of each disabled person to the society, and to facilitate the development of themselves - as individuals.
What would this world be like if the new look for brain Imaging is to combine imaging with an increasingly complex computational neuroscience, e.g., the study of brain function in terms of the information processing properties of individual dynamics, and distributed systems, in each individual brain.
What are the possible consequences of being able to identify the characteristics of, and potential for, different kinds of behaviour and information processing. What if the dynamic interaction of systems in the brain actually constituted, i.e., defined, "the individual".
What are the distinctions between individual and societal advantages and what if an individual 'consents' to have their brain changed - and then after it was changed the individual changed mind?
There are of course positive and negative consequences of a real understanding of the potential of each individual. At the particular time the consequences can be considered, an advantage to the 'society,' as it is defined at that time. What would be the current response of the Society if control were able to stop the killing of people by people - and would that be a good thing?
Of course the answer today is no - since the killing of people is current, and has been in the past, implemented primarily for the purpose of control. The question is what are the ultimate consequence of the uses of science, to understand the human brain and whether the will result in a negative, or a positive future for the human race.
The technology now is close to being able to identify capability of different system configurations for different types of information, and the degree to which they are unique to different individuals.
The Recognition Index Recognition Indices for Each Person - what would be the consequences? A review, published in the journal Neurone, highlights a number of recent studies showing Dynamics of System Predicts Behaviour. John Gabrieli (2015)
that brain imaging can help predict an individual's future learning, their future criminality, their health-related behaviours, and their potential response to drug or behavioural treatments.
The developing technology may offer opportunities to personalize educational and clinical practices.
An example is the studies of Dr. John Gabrieli (2015), of the Massachusetts Institute of Technology in Cambridge, and his colleagues, who describe the predictive power of brain imaging across a variety of different future behaviours, including the infants' later performance in reading, in math and the likelihood of them becoming repeat offenders, of adolescent future drug and alcohol use.
"Presently, we often wait for failure, in school or in mental health, to prompt attempts to help, but by then a lot of harm has occurred," says Gabrieli. "If we can use Neuroimaging to identify individuals at high risk for future failure, we may be able to help those individuals avoid such failure altogether."
The authors also point to the clear ethical and societal issues that are raised by studies attempting to predict individuals' behavior. "We will need to make sure that knowledge of future behavior is used to personalize educational, and medical practices, and not be used to limit support for individuals at higher risk of failure." For example, rather than simply identifying individuals to be more or less likely to succeed in a program of education, such information could be used to promote differentiated education for those less likely to succeed with the standard education program. A critical element of the educational system may be the understanding, encouragement and acceptability of diversity of complex systems in the brain, and the recognition of the uncertainty principle in the understanding of those systems, i.e. observation of a system in disturbs the system.
I think the concept of how we understand the world around us is changing, from a focus on central tendency to an understanding of variability. An understanding of variability could be more important than the understanding of central tendency. The importance and function of variability for survival, of any system, is beginning to emerge in the understanding of any system, regardless how molar or molecular that system is.
As soon as any system becomes homogenized and variability disappears ,the system become completely unable to change, and dies. Variability of function in the brain is an index of processing, the analysis of input and the preparation for output all of which is variable and was, and is, instrumental in how the species, any species, survived.
The importance of variability is universal and the understanding of it, not just the record of it, is critical in
understanding underlying principals of how humans, of how societies, function.
Of course it has always been clear that the homogenization of ideas results in the process of control, this a primary element in politics and religion.
If one were to consider the broader implications of this with respect to the nature of our society as a whole, the question is whether survival of a society depends on acceptability of diversity.
It has always been clear that the homogenization of ideas results in the process of control. If one were to consider the broader implications of this with respect to the nature of our society as a whole, the question is whether survival of a society depends on acceptability of diversity.
Well, I guess what I suggested at the beginning of this is that the pendulum has begun to swing, from a focus on localized sources in the brain to an understanding of complex, distributed, processing systems.
But what happens when the pendulum begins to swing back again - to an analysis of molecular attributes of the 'system', e.g., the effect of a small localized changes that could make dynamic changes throughout the brain (the Butterfly Effect).
The bottom line is that the acceptance of diversity requires a configuration of when the herd mentality is not acceptable - and how is that decided?. Is there a method for increasing plasticity of the the brain, increase its capabilities to change but those changes do not decrease survival of the species - or- to put it differently is there a method of changing the brain to accept diversity?
Therefore the final question is: Is it possible that future technology will result a change in the Herd Mentality as a result of an increasing plasticity of the brain, A consequence could be and acceptability of diversity, and be the beginning of the way in which we can "All Live Together"
Well I guess the answer is that everything evolves - or dies.
Bressler, Steven L., J.A. Scott Kelso. Coordination Dynamics in Cognitive Neuroscience
Neuroscience, 2016, https://doi.org/10.3389/fnins.2016.00397
Bullmore, Ed, Sporns, Olf Complex brain networks: graph theoretical analysis of structural and functional systems. BrainwaveR Toolbox: http://www.nitrc.org/projects/,
Edelman, Gerald (1989) The Remembered Present, A Biological Theory of Consciousness . Basic Books: 1989.
Fuchs, Eberhard, Gabriel Flugge. Adult Neural Plasticity, Volume 2014 (2014), Article ID 541870.
Gabrieli, John D.E. Satrajit S. Ghosh, Susan Whitfield-Gabrieli. Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience. Neurone, 2015; 85 (1)
Hari, R., Salmelin, R., Magnetoencephalography: From SQUIDs to neuroscience, NeuroImage (2012)
Hawking, Stephen, Transcendence looks at the implications of artificial intelligence - but are we takingArtificial Intelligence I seriously enough? TheIndependent 13 February 2015.
Holistic Atlases of Functional Networks andInteractions Reveal Reciprocal Organizational Architecture of Cortical Function. IEEE transactions on page 3# 3 of #36
bio-medical engineering 11/2014; 2.15 Page 16 of17.
Jason Taylor (2011) MRC Cognition and BrainSciences Unit (CBU) Cambridge Centre for Ageing and Neuroscience
Nunez, Paul (1995) Neocortical Dynamics and HumanEEG Rhythms Oxford University Presshttps:// www.youtube.com/watch?v=et-cnKqPFFwInfluence of magnetic fields on electrical fields
Polich, John (2007) Updating P300: An integrative theory of P3a and P3b,Clinical Neurophysiology 118 , 2128–2148*page 3# 2 of #36
Skarda and. Freeman, Walter. J. (1990) Concepts in Neuroscience, Vol. 1, No. 2 (1990) Connecthome www.nature.com/reviews/neuro 198 |march 2009 | VOlume 10
Jinglei Lv, Xi Jiang, Xiang Li, Dajiang Zhu, Shu Zhang,Shijie Zhao, Hanbo Chen, Tuo Zhang, Xintao Hu, Junwei Han, Jieping Ye, Lei Guo, Tianming Liu Sacco K, Cauda F, D'Agata F, Duca S, Zettin M, Virgilio R, Nascimbeni A, Belforte G, Eula G, Gastaldi L,
Appendino S, Geminiani G. A combined robotic and cognitive training for locomotor rehabilitation: evidences of cerebral functional reorganization in two chronic traumatic brain injured patients.
Kievita,Robert A, Simon W. Davisb, c, John Griffithsb, d, Marta M. Correiaa, Cam-CANe, 1, Richard N.Hensona, A watershed model of individual differences in fluid intelligence, Neuropsychologia,Volume 91, October 2016, Pages 186–198
Pinel, John P.J. Biopsychology, 8th Edition Published by Pearson Copyright © 2011, Published Date: Oct 19, 2010
Rothbart, .Mary K. , Michael Posner, The developing brain in a multitasking world Developmental Reviews, Vol 35, 2015, 42-63).
Skarda, Christine A. and Freeman Walter, J. ChaosAnd The New Science of The Brain: Concepts inNeuroscience, Vol. 1, No. 2 (1990)
Tarkka, Ina, Stokic, Dobrivoje, S. Stokic. Source Localization of P300 from Oddball, Single Stimulus, and Omitted-Stimulus Paradigms. December 1998, Volume 11, Issue 2, pp 141-151
Ulrich Hegeri, Thomas Frodl Bauch, (1997) Dipole source analysis of P300 component of the auditory evoked potential: a methodological advance? Psychiatry Research: Neuroimaging, (1997) Volume 74, Issue 2, Pages 109-118
Walter, W.G; Cooper, R.; Aldridge, V.J.; McCallum, W.C.; Winter, A.L. (1964). "Contingent Negative Variation: an electric sign of sensorimotor association page 3# 4 of #36 and expectancy in the human brain". Nature (1943): 380–384.
Weinberg , H., Walter, W. Gray and Crow, H.J. (1969) Intracerebral events in humans related to real and imaginary stimuli. EEG Clin. Neurophysiol., 27, 665.
Weinberg , H. and Cooper, R. (1972) The recognition index: a pattern recognition technique suitable for noisy signals. EEG Clin.Neurophysiol., 1972, 33, 608-613.
Weinberg, H, W.Grey Walter, R. Cooper, V.J. Aldridge.Emitted Cerebral Events, Eletroencephalography andClinical Neurophysiology, Vol. 36, 449-456, 1974Christine A.
Weinberg , H. (1972) The recognition index: A pattern recognition technique suitable for noisy signals. Electroencephalography and Clinical Neurophysiology Meetings, London, England, January, l972.
Weinberg, H. Carson, P, Joly, R, , Jantzen K.J., Cheyne, D and Vincent, A. Measurement and Monitoring of the Effects of Work Schedule and Jet Lag on the Information Processing Capacity of Individual Pilots, J. Aviation Psychology, 1999.
Weinberg , H., Walter, W. Gray and Crow, H.J. (1969) Intracerebral events in humans related to real and imaginary stimuli. EEG Clin.
Neurophysiol., 27, 665.
Ye Annette X., Leung,Rachel C., Schafer,Carmen B., Taylor, Margot J., Doesburg, Sam M.. Atypical Resting Synchrony in Autism Spectrum Disorder,Human Brain Mapping 35:6049–6066 (2014
Xin Zhang, Xiang Li, Changfeng Jin, Hanbo Chen, Kaiming Li, Dajiang Zhu, Xi Jiang, Tuo Zhang, Jinglei Lv, Xintao Hu, Junwei Han, Qun Zhao, Lei Guo, Lingjiang Li, Tianming Liu Identifying and Characterizing Resting State Networks in Temporally page 3# 5 of #36 Dynamic Functional Connectomes Brain Topography, 2014
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusant doloremque laudantium, totam rem.
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusant doloremque laudantium, totam rem.
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusant doloremque laudantium, totam rem.