Human brain mapping

All posts tagged Human brain mapping

How many different kinds of rhythms are there in our brain?

Published September 22, 2016 by anirbanbandyo

Sound is a mechanical rhythm and we have 12 different rhythms in the brain, like

1. Electromagnetic rhythm, [carrier is photon or electromagnetic wave is trapped in a cavity to generate beating or rhythm].
2. Magnetic rhythm, [spiral flow of electrons or ions, they are the carriers editing the magnetic flux, geometry of path forms the periodicity].
3. Electrical potential rhythm, [change in the arrangement of dipoles editing the electric field, fractal distribution of local resonators generate a time function of potential]
4. Solitonic & quasi particle rhythm, [carriers are solitons, defect in the order flows in a ordered structure, the ordered structure is edited to make a loop].
5. Ionic diffusion rhythm, [ions are carriers, tube like cavities are formed in a circular shape or continuous path to generate a loop]
6. Molecular chemical rhythm, [molecules like proteins, enzymes etc are carriers, tube like cavities are formed and sensory systems make sure a circular signalling pathway]
7. Quantum beating, [spin is the carrier, wavefunctions interfere in a squeezed excited photonic, electromagnetic or spin state]
8. Density of states rhythm, [orbitals coupling, wave function modulation, virtual carrier, a virtual continuous loop is made]
9. van-der Waal rhythm, [atomic thermal vibration is looped in a spiral pathway].
10. electro-mechanical rhythm [classical beating with a mechanical beating like tuning fork]
11. Quasi-charge rhythm polaron, polariton [topological fractured band based continuous loops]
12. mechanical rhythm, sound wave [similarly elastic pathways to make a circuit of sound waves].

Just imagine, such beautiful patterns are one of 12 different formations. Their physics is different, application domain and range are different.

Ten inventions that we contributed from NIMS, Japan and would consolidate in the next 10 years (2016-2026)

Published September 11, 2016 by anirbanbandyo
  1. Information Theory:          A new information theory. FIT or Fractal Information Theory. This is a new way of computing or decision-making by fractal logic gate, or fractal cavity resonator. For this purpose we proposed Fractal tape like Turing tape and Fractom machine. We often use nested cycle metric to generate fundamental geometric constants.
  2. Mechanics:           We have been developing a new generic version of quantum mechanics, FM or Fractal Mechanics, We conceptualized a new fractal harmonic oscillator alternative to QFT
  3. Language:         We have been developing a new geometric musical language (GML) and constructed Continued Fraction Geometric Alzebra (CFGA) and Geometric musical calculator (GMC).
  4. Human brain model:          We develop fractal cavity resonator model of the human brain, (FCR-Brain) and Frequency Wheel of prime model (FWPM) for any biological machine. We develop cavity math for protein design for life forms. Nested Rhythm map of the Human brain (NRM-Brain). We discovered ferroelectric, piezoelectric & pyroelectric properties of microtubule and every protein, we have also discovered that protein has electromagnetic resonance, Universal protein circuit model (UPCM).
  5. Geometric number system theory:             We construct and information version for developing the theory of everything, and a theory of consciousness we call it Time cycle universe theory (TCU), this is not like GUT, ours is a set of integrated mathematical constructs and protocols that would help in the understanding the universe and or consciousness. For example relating e, pi, phi with all fundamental constants, frequency wheel of primes, ordered factor etc.
  6. Fourth circuit element:            We invent fourth circuit element, Hinductor
  7. Thermal energy harvesting machines:           We invent artificial life forms, kT driven thermal nanobots, Thermo Electro mechanical system (TEMS).
  8. Musiceutical nanobots:            We have developed musiceuticals, vibration based medicines, Nano Brain. Main focused diseases have been cancer and alzheimers.
  9. Fractal chemical kinetics and condensation:          We have developed a new kind of multi-kinetic synthesis, we invent a new class of condensation beyond Froelich condensation, Fractal condensation
  10. Brain jelly:             We invented organic jelly that replicates biological rhythm like information processing, Brain jelly. We invent world’s smallest neural network. Multilevel switch. Fractal beating like quantum beating for nested beating to be used to create fractal cavity resonator for brain jelly.


Does fractal cavity resonator model conflict with circuit model of human brain?

Published June 12, 2016 by anirbanbandyo

António Egas Moniz  theorized that mental illness was caused by nerve cells that couldn’t communicate properly and caused people to in pathological symptoms like mood disorder, sudden switching, panic and several other depression. He developed the lobotomy to try cut off the nerve fibers that he presumed facilitated the mental disorders. After experimenting on 19 patients, he reported that they did indeed improve, though they seemed to also lose crucial elements of their personality and creativity. It was a painful and a bad experiment, however, it does tell us that brain circuits do play a vital role in generating the conscious behavior.

When we develope our fractal cavity resonator model of the human brain, does it have any contradiction with the circuit model? Does it discard the cavity model? Here are some of the arguments which would establish that transmission of local density of states that we need in the molecular scale for the phase based information processing, we need the same at the centimeters scale. Then how would it be possible to transmit signal from one part of the brain to another, we need wiring. Therefore the same cavity resonator model which uses membrane based cavity made of proteins and ionic wave to generate the superposition of waves and interference of waves to create a pattern of vibrations, the save cavity principles work in the largest scale. We can never expect that at all scales we will be able to generate similar forms of vibrations and cavity effects with similar principles.

This is the reason our suggestion of building a frequency fractal to represent a biological system is an essential part of the cavity resonator model. The massive neural wiring of the connectome project is the key to the geometric phase and dynamic phase manipulation at the large scale in the same way it happens for a molecule. Therefore, from the molecular scale to the largest brain scale there is not much fundamental changes in the principle of operation. We should be careful about the consistency of the parameters.

What is the resonance chain and what is frequency wheel then? How tiny pieces of resonance chain creates a frequency wheel? Every single element in the brain is a cavity resonator and has a electromagnetic resonance band, and when several materials come together their resonance bands overlap to form a chain. Since, the number of cavities in a cavity is finite, hence, the chain is finite in length and as the guest cavities process only the geometric information, total phase lag is always 2Pi, hence there is a time cycle. So, we convert a resonance chain into a wheel layer. Many many layers one above another make a frequency wheel that is the representative of a system. Then what is the brain hardware? A fusion of escape time fractal and iterative function system type fractals.

Every single element in the brain is a fractal cavity resonator that leaks. Why does it need to leak? For birefringence, the brain sends pairs of coherent signals in the inner cavities for the quantum beating. All over the brain quantum beating is the key, and this is also true for the connectome map.

How Quantum interference is possible at the large scale? Why does the states do not burn?  Fractal structure enables coherent sources to pass through topmost part of the structure to the smallest cavity, i.e. to the atomic scale. Moreover, the cavities run dynamic phase cycles to run the geometric phase cycles in a time loop. Hence, energy transmission is fractal like. For this very reason, there is neither coherence nor decoherence, it is fracto-herence that creates a virtual reaction point. For example, protein’s in the microtubule would exhibit de-coherence, but the interference occurs in the water channel, whereas the coherent sources are generated by the protein layer.

Is the brain a Quantum computer then? No. We have proposed a new class of computing, fractal tape based computing that would enable the system to process information and making decision in a very different way.















Why do we need a global database of biological rhythms?

Published April 1, 2016 by anirbanbandyo

“Consider the synchronous flash of the lustful firefly; or the lockstep of cheetah and gazelle; the ease with which millions of bats move together like living smoke in the night sky; the highly coordinated hunts of wolves and orcas; and the intricate mating dances of tropical birds. Clearly rhythm is fundamental to life”

Commonly, we believe that the biological rhythms are only designed to clock the vibrations and maintain the time of enzyme secretion etc. However, there are several research activities on the brain rhythms which suggests that maintaining time is not the only reason. Rhythms could be the fundamental elements of the brain’s information processing. It is shown that when people listen to tones played at regular intervals, certain neural circuits begin to oscillate in time with the tones [1]. It means whenever a rhythm is captured by the brain, there is another rhythm forms in the brain. Both the rhythms that enters into the brain and the rhythm that generates inside the brain are fundamentally different. Yet we can consider somehow both the rhythms represent the same information. It is also shown that the “neural oscillations in both human and multiple other animal brains — including those of monkeys and zebrafish — consistently synchronize with the auditory rhythms, including those that come from a simple metronome, classical music or human speech” [2].

Looking beyond the auditory rhythms, if we think of particulars in the music that haunts an animal or living species, it is the composition of frequencies. For example, a sea lion, it does not dance at all compositions of music, only if certain sets of frequencies are pumped [3]. This reminds us about the resonant oscillations of microtubule and proteins that we measured [4-6]. Even animals select the composition of rhythms more precisely. While preferring silence to music from the West, chimpanzees apparently like to listen to the different rhythms of music from Africa and India [7]. “When Japanese music was played, they were more likely to be found in spots where it was more difficult or impossible to hear the music. The African and Indian music in the experiment had extreme ratios of strong to weak beats, whereas the Japanese music had regular strong beats, which is also typical of Western music”. Therefore,  once again the complexity of scale change that is found in the neuron, microtubule and the protein’s vibrational research is profound in the living life forms. Strongly periodic and predictable vibrations contain zero information according our research on microtubule and when we see that Chimps dislike such music strongly there is no doubt that they find such strings of vibrations as empty.

Moment to moment variations of the neural correlates of behaviors:

Human connectomics project tries to understand how connections between different brain regions give rise to sophisticated behaviors. The reason this is interesting is because it’s a departure from the traditional cognitive neuroscience, where the goals were to map cognitive functions to individual brain structures, and infer what process each brain region was responsible for. The probability that several parts of the brain might interact together to generate a cognitive behavior that does not belong to any part of the brain. Recently, moment-to-moment variations of the insula and its subdivisions is studied in a method called, “dynamic functional network connectivity” (d-FNC). Whereas “static functional network connectivity” (s-FNC) provides researchers with an overall, average look at brain function throughout an fMRI scan, d-FNC allows for the identification of how connections between brain areas change over time [8]. Another finding is that the brain can only make sense of music (auditory domain) by relating it to rhythmic bodily movements (motor region), even if we aren’t moving at all. This is very interesting because our dance comes first before our sense of singing.

Both individual neurons and groups of brain cells display repetitive fluctuations in their electrical and chemical activity. But when scientists speak of neural oscillations, they are usually referring to cyclic changes in the strength of the electric fields generated by thousands or millions of interconnected brain cells. Bidirectional electromagnetic control of the hypothalamus regulates feeding and metabolism of a human, it is purely a dipolar control, an oscillatory rhythm [9]. The picture above is a complete model of a human brain or to say any advanced fractal cavity resonator model structure, built to date.

Memory could be recovered even if the brain cells are destroyed(!) 

Susumu Tonegawa, from the Picower Institute for Learning and Memory at the Massachusetts Institute of Technology (MIT)  has shown that by stimulating nerve cells with light, people with Alzheimer’s might be able to retrieve lost memories [10]. Tonegawa and his colleagues pulsed the light (mimicking processes that happen in the brain when it repeatedly calls up old memories), which helped foster new connections between the hippocampus and entorhinal cortex region of the brain. It shows that memory does not remain in the hardware, rather somewhere at a place that we cannot retrieve measuring a physical device. This is what we have been saying for all along. Lowest level vibrations are only output of a hardware, but when they couple, and create combined vibrations, they remain in the cavity, you cannot see them, or measure them. This is why in our fractal cavity resonator model, memory is invisible. Instead of neural correlates, we get frequency correlates. Early trials suggest that deep-brain stimulation of the hippocampus prompts the creation of neurons and improves memory in some Alzheimer’s patients. But no one knows how it works [11].

The interactive clocks in the hippocampus:

In rodents, interesting spatial correlates of neural firing found in and around the hippocampus, such as the ‘place cells’ [12]. An enormous amount of research has been done to correlate the motion of a living species and the rhythms observed in the EEG. Both types of oscillation, Gamma and theta and the phase relationships between them, appear to contribute to memory-relevant coding of information ranging from objects [13].  This paper worked on “phase-coupled replay of information during active maintenance over short time intervals. Remarkably, the replay, indexed in stimulus-specific neural firing patterns, mirrored the sequence in which information was encoded”. Therefore, the frequency coupled with phase plays a fundamental role in the understanding of the information. “Furthermore, by regulating the precise timing of presynaptic and postsynaptic neurons, theta and gamma oscillations also modulate spike-time dependent plasticity [14], a prerequisite for both short-term and long-term memory processes.”  The human spatial navigation in virtual environments is also accompanied by the presence of theta rhythmicity in intracranial recordings [15, 16]. How Hippocampus finds the location of the species with respect to its environment is an interesting hypothesis. The non-local interactions of theta rhythms between entorhinal cortex and Hippocampus and systematic phase modulation to the LFP theta rhythm. The cross frequency coupling could even modulate the phase [17]. Therefore, a frequency map is essential like the one shown above, which could map all kinds of frequency modulation and a nested time or rhythm map like the one below that could take into account all possible phase modulations.

Nested Rhythm model of a human brain

The correlation between the memory and the biological rhythms:

The studies of episodic memory have indicated an asymmetry in the prefrontal involvement, an asymmetry suggests a hemispheric encoding and retrieval asymmetry (HERA) for a particular memory is a fundamental property. If encoding activates the gamma EEG oscillation of the left parietal, then retrieval of this memory activates the gamma EEG oscillation of the right parietal. A spatial duality in the rhythms a large distance cooperation [18]. The cooperative coupling has been studied for the symbol memory retrieval [19]. Recent data also supports models showing how network and cellular theta rhythmicity allows neurons in the entorhinal cortex and hippocampus to code time and space as a possible substrate for encoding events in episodic memory [20]. Therefore, memory storage and retrieval is a process that starts from a single neuron cell and expands all over the brain.

There are different types of memories in a human behavior. Episodic memory is a person’s unique memory of a specific event, so it will be different from someone else’s recollection of the same experience. Episodic memory is sometimes confused with autobiographical memory, and while autobiographical memory involves episodic memory, it also relies on semantic memory. For example, you know the city you were born in and the date, although you don’t have specific memories of being born. Semantic memory refers to a portion of long-term memory that processes ideas and concepts that are not drawn from personal experience. Semantic memory includes things that are common knowledge, such as the names of colors, the sounds of letters, the capitals of countries and other basic facts acquired over a lifetime.

The structure and operational protocol for the global rhythm database:

It is possible to generate a frequency wheel from the raw frequency response data published in the papers. We have developed a platform wherein one could load a new frequency wheel that is blank, when an user gives input as the frequency range, wherein a particular system responds and which organs does it connect to, then, automatically the system would place the rhythm to the already connected fundamental rhythm structure of the brain-system.  Most of the biological rhythms originate in the brain, hence our biological rhythm database means in reality brain rhythm database too. “The engineers of the future will be poets.” –Terence McKenna

[1] Joel S. Snyder, Edward W. Large, Gamma-band activity reflects the metric structure of rhythmic tone sequences, Cognitive Brain Research, 24, 117–126 (2005).

[2] E. W. Large, J. A. Herrera, and M. J. Velasco Neural Networks for Beat Perception in Musical Rhythm, Front Syst Neurosciv.9; 2015

[3]  Peter Cook, Andrew Rouse, Margaret Wilson, and Colleen Reichmuth, A California Sea Lion (Zalophus californianus) Can Keep the Beat: Motor Entrainment to Rhythmic Auditory Stimuli in a Non Vocal Mimic, Journal of Comparative Psychology 2013, Vol. 127, No. 4, 412– 427

[4] S. Sahu, S. Ghosh, D. Fujita, A. Bandyopadhyay Live visualizations of single isolated tubulin protein self-assembly via tunneling current: effect of electromagnetic pumping during spontaneous growth of microtubule. Scientific Reports, 4, 7303 (2014)

[5] S. Sahu, S. Ghosh, K. Hirata, D. Fujita, A. Bandyopadhyay Multi-level memory-switching properties of a single brain microtubule. Applied Physics Letters 102, 123701 (2013)

[6] S. Sahu, S. Ghosh, B. Ghosh, K. Aswani, K. Hirata, D. Fujita, A. Bandyopadhyay Atomic water channel controlling remarkable properties of a single brain microtubule: Correlating single protein to its supramolecular assembly Biosensors and Bioelectronics 47(2013)141–148

[7] Chimpanzees Prefer African and Indian Music Over Silence,” Morgan E. Mingle, BA, Emory University and Southwestern University; Timothy M. Eppley, PhD, and Matthew W. Campbell, PhD, Emory University; Katie Hall, PhD, Emory University and University of St Andrews; Victoria Horner, PhD, and Frans B. M. de Waal, PhD, Emory University; Journal of Experimental Psychology: Animal Learning and Cognition, online June 23, 2014

[8] Jason S. Nomi, Kristafor Farrant, Eswar Damaraju, Srinivas Rachakonda, Vince D. Calhoun and Lucina Q. Uddin Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions, Human Brain Mapping, 16 Feb, 2016, DOI: 10.1002/hbm.23135

[9] Sarah A. Stanley, et al Bidirectional electromagnetic control of the hypothalamus regulates feeding and metabolism; Nature 531, 647–650 (31 March 2016)

[10] Dheeraj S. Roy, Autumn Arons, Teryn I. Mitchell, Michele Pignatelli, Tomás J. Ryan, & Susumu Tonegawa, Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease; Nature 531, 508–512 (24 March 2016).

[11] Sankar, T. et al. Brain Stim. 8, 645654 (2015)

[12] O’Keefe J: Hippocampal neurophysiology in the behaving animal. In The Hippocampus Book. Edited by Andersen P, Morris R, Amaral DG, Bliss TVP, O’Keefe J. Oxford Neuroscience; 2006

[13] Siegel M, Warden MR, Miller EK: Phase-dependent neuronal coding of objects in short-term memory. Proc Natl Acad Sci U S A 2009

[14] Markram H, Lubke J, Frotscher M, Sakmann B: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 1997, 275:213-215.

[15] Ekstrom AD, Kahana MJ, Caplan JB, Fields TA, Isham EA, Newman EL, Fried I: Cellular networks underlying human spatial navigation. Nature 2003, 425:184-188

[16] Cornwell BR, Johnson LL, Holroyd T, Carver FW, Grillon C: Human hippocampal and parahippocampal theta during goaldirected spatial navigation predicts performance on a virtual Morris water maze. J Neurosci 2008, 28:5983-5990

[17] Xiaxia Xu, Chenguang Zheng, and Tao Zhang, Reduction in LFP cross-frequency coupling between theta and gamma rhythms associated with impaired STP and LTP in a rat model of brain ischemia, Front Comput Neurosci. 2013; 7: 27

[18] Babiloni C et al, Human cortical EEG rhythms during long-term episodic memory task. A high-resolution EEG study of the HERA model. Neuroimage. 2004 Apr;21(4):1576-84

[19] Anderson, J.R., et al., 2003. An information-processing model of the BOLD response in symbol manipulation tasks. Psychon. Bull. Rev. 10, 241-261

[20] Hasselmo ME, Stern CE, Theta rhythm and the encoding and retrieval of space and time. Neuroimage. 2014 Jan 15;85 Pt 2:656-66. doi: 10.1016/j.neuroimage.2013.06.022. Epub 2013 Jun 14.