I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

I do not recall having met an educated person who claims to know less about contemporary physics than I do. Nevertheless, it has been my contention that as physicians living in the early part of the second half of the twentieth century we have very strong obligations to understand as much as we can of the world we live in. Perhaps if this could be done on a large scale events might be bent somewhat to wise purposes rather than just letting them happen. The introduction of the uncertainty principle, for which Heisenberg received the Nobel Prize, may be of some comfort in its oversimplified form as applied to research in biology. As I understand it, his essential belief is that by the very act of measuring a variable factor is introduced which disqualifies the evidence by virtue of altering the thing measured. Certainly, in clinical investigation and in biology

Physics and Philosophy, the Revolution in Modern Science.

John S Bell 1987 Cambridge: Cambridge University Press vii + 212 pp price £25 ISBN 0 521 33495 0 This book is a collection of John Bell's papers dealing explicitly with the foundations of quantum mechanics (or, as he puts it, 'quantum philosophy'), plus one paper on the pedagogy of special relativity. The first two are the classic papers of 1 966 and 1 964, which respectively demolished the 'impossibility proofs' concerning hidden variables and proved the famous theorem on the incompatibility of the predictions of local hidden-variable theories with those of quantum mechanics.

https://iopscience.iop.org/article/10.1088/0031-9112/39/6/033/pdf

Speakable and Unspeakable in Quantum Mechanics: Collected Papers on Quantum Philosophy

Biofield science is an emerging field of study that aims to provide a scientific foundation for understanding the complex homeodynamic regulation of living systems. By furthering our scientific knowledge of the biofield, we arrive at a better understanding of the foundations of biology as well as the phenomena that have been described as "energy medicine." Energy medicine, the application of extremely low-level signals to the body, including energy healer interventions and bio-electromagnetic device-based therapies, is incomprehensible from the dominant biomedical paradigm of "life as chemistry." The biofield or biological field, a complex organizing energy field engaged in the generation, maintenance, and regulation of biological homeodynamics, is a useful concept that provides the rudiments of a scientific foundation for energy medicine and thereby advances the research and practice of it. An overview on the biofield is presented in this paper, with a focus on the history of the concept, related terminology, key scientific concepts, and the value of the biofield perspective for informing future research.

https://journals.sagepub.com/doi/pdf/10.7453/gahmj.2015.038.suppl

Biofield Science and Healing: History, Terminology, and Concepts

Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays) to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks.

/pdf/neural-substrate-expansion-for-the-restoration-of-brain-1bc2bmucqf.pdf

Neural Substrate Expansion for the Restoration of Brain Function.

In recent times the interest for quantum models of brain activity has rapidly grown. The Penrose-Hameroff model assumes that microtubules inside neurons are responsible for quantum computation inside brain. Several experiments seem to indicate that EPR-like correlations are possible at the biological level.
In the past year , a very intensive experimental work about this subject has been done at DiBit Labs in Milan, Italy by our research group. 
Our experimental set-up is made by two separated and completely shielded basins where two parts of a common human DNA neuronal culture are monitored by EEG. 
Our main experimental result is that, under stimulation of one culture by means of a 630 nm laser beam at 300 ms, the cross-correlation between the two cultures grows up at maximum levels. 
Despite at this level of understanding it is impossible to tell if the origin of this non-locality is a genuine quantum effect, our experimental data seem to strongly suggest that biological systems present non-local properties not explainable by classical models.

/pdf/nonlocal-correlations-between-separated-neural-networks-4wngep1x18.pdf

Nonlocal correlations between separated neural networks

The development of bio-electronic prostheses, hybrid human-electronics devices and bionic robots has been the aim of many researchers. Although neurophysiologic processes have been widely investigated and bio-electronics has developed rapidly, the dynamics of a biological neuronal network that receive sensory inputs, store and control information is not yet understood. Toward this end, we have taken an interdisciplinary approach to study the learning and response of biological neural networks to complex stimulation patterns. This paper describes the design, execution, and results of several experiments performed in order to investigate the behavior of complex interconnected structures found in biological neural networks. The experimental design consisted of biological human neurons stimulated by parallel signal patterns intended to simulate complex perceptions. The response patterns were analyzed with an innovative artificial neural network (ANN), called ITSOM (Inductive Tracing Self Organizing Map). This system allowed us to decode the complex neural responses from a mixture of different stimulations and learned memory patterns inherent in the cell colonies. In the experiment described in this work, neurons derived from human neural stem cells were connected to a robotic actuator through the ANN analyzer to demonstrate our ability to produce useful control from simulated perceptions stimulating the cells. Preliminary results showed that in vitro human neuron colonies can learn to reply selectively to different stimulation patterns and that response signals can effectively be decoded to operate a minirobot. Lastly the fascinating performance of the hybrid system is evaluated quantitatively and potential future work is discussed.

https://homes.di.unimi.it/pizzi/pubbl/2009%20biosystems%20secondo.pdf

A cultured human neural network operates a robotic actuator

This paper describes experiments involving the growth of human neural networks of stem cells on a MEA (microelectrode array) support. The microelectrode arrays (MEAs) are constituted by a glass support in which a set of tungsten electrodes are inserted. The artificial neural network (ANN) paradigm was used by stimulating the neurons in parallel with digital patterns distributed on eight channels, then by analyzing a parallel multichannel output. In particular, the microelectrodes were connected following two different architectures, one inspired by the Kohonen's SOM, the other by the Hopfield network. The output signals have been analyzed in order to evaluate the possibility of organized reactions by the natural neurons.f The results show that the network of human neurons reacts selectively to the subministered digital signals, i.e., it produces similar output signals referred to identical or similar patterns, and clearly differentiates the outputs coming from different stimulations. Analyses performed with a special artificial neural network called ITSOM show the possibility to codify the neural responses to different patterns, thus to interpret the signals coming from the network of biological neurons, assigning a code to each output. It is straightforward to verify that identical codes are generated by the neural reactions to similar patterns. Further experiments are to be designed that improve the hybrid neural networks' capabilities and to test the possibility of utilizing the organized answers of the neurons in several ways.

https://air.unimi.it/bitstream/2434/140067/5/biosystemprimo.pdf

Learning in human neural networks on microelectrode arrays.

This paper describes a computational approach to the theoretical problems involved in the Young's single-photon double-slit experiment, focusing on a simulation of this experiment in the absence of measuring devices. Specifically, the human visual system is used in place of a photomultiplier or similar apparatus. Beginning with the assumption that the human eye perceives light in the presence of very few photons, we measure human eye performance as a sensor in a double-slit one-photon-at-a-time experimental setup. To interpret the results, we implement a simulation algorithm and compare its results with those of human subjects under identical experimental conditions. In order to evaluate the perceptive parameters exactly, which vary depending on the light conditions and on the subject’s sensitivity, we first review the existing literature on the biophysics of the human eye in the presence of a dim light source, and then use the known values of the experimental variables to set the parameters of the computational simulation. The results of the simulation and their comparison with the experiment involving human subjects are reported and discussed. It is found that, while the computer simulation indicates that the human eye has the capacity to detect the corpuscular nature of photons under these conditions, this was not observed in practice. The possible reasons for the difference between theoretical prediction and experimental results are discussed.

/pdf/human-visual-system-as-a-double-slit-single-photon-polm3kfh79.pdf

Human Visual System as a Double-Slit Single Photon Interference Sensor: A Comparison between Modellistic and Biophysical Tests

Researchers of the Department of Information Technologies of the University of Milano and of the Stem Cells Research Institute of the DIBIT-S. Raffaele Milano are experimenting the growth of human neural networks of stem cells on a MEA (Microelectrode Array) support. The Microelectrode arrays (MEAs) are constituted by a glass support where a set of tungsten electrodes is inserted. We connected the microelectrodes following the architecture of classical artificial neural networks, in particular Kohonen and Hopfield networks. The neurons are stimulated following digital patterns and the output signals are analysed to evaluate the possibility of organized reactions by the natural neurons. The neurons reply selectively to different patterns and show similar reactions in front of the presentation of identical or similar patterns. These results allow to design further experiments that improve the neural networks capabilities and to test the possibility of utilizing the organized answers of the neurons in several ways.

/pdf/behavior-of-living-human-neural-networks-on-microelectrode-502kmuk7g0.pdf

Behavior of living human neural networks on microelectrode array support

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