Why do we need a global database of biological rhythms?

“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.

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

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[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

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[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 http://www.apa.org/news/press/releases/2014/06/chimps-music.aspx

[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).

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[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

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