Simulation Neuroscience

I would like to talk a little about my field of study at MIT University, Harvard University, Postdoc in Boston University and EPFL University by edX.

Neuroscience can be divided into:

? Behavioral Neuroscience

Dedicated to understanding what lies behind human behavior, behavioral neuroscience focuses on phenomena of the mind, individualities such as personality and memory formation.

? Cognitive Neuroscience

Focused on the dynamics of thinking, memory and learning, cognitive neuroscience studies part of perception and sensation, following their registration and access by the conscious self.

? Neurophysiology

Combining neuroscience with the study of the parts of the organism and their interaction, neurophysiology includes investigations into the central and peripheral nervous system.

? Neuroanatomy

Integrated with anatomy, this subarea is dedicated to knowledge about the organization and structure of the brain, spinal cord, nerves and nerve endings.

? Neuropsychology

It consists of analyzing the brain basis for complex responses and attitudes of the organism, including cognitive, emotional and behavioral disorders.

? Simulation Neuroscience

It develops all the biological algorithms, scientific processes and software needed to digitally reconstruct and simulate everything from genes, proteins, cells, circuits, cognition, psychophysics, to behavior, to build a database of the brain.

Advances in alternative treatments and even curative procedures for neurodegenerative diseases such as Huntington's, Parkinson's and Alzheimer's are the result of research in molecular, cellular, systemic, behavioral and cognitive neuroscience.

The Human Brain Project aims to implement a research infrastructure in the areas of neuroscience, computing and medicine, related to the brain.

The most important thing in simulation neuroscience is really rebuilding, testing, validating, finding errors, rebuilding again and again and again and again until it becomes a digital copy of tissue.

I need neuronal morphologies to get an electrophysiological profile and build a circuit.

There are about 280 ion channels, special transmembrane proteins that allow ions to flow selectively, and 145 are voltage dependent and when the voltage changes, they open or close.

Synaptic connections are very elegant experiments where you place an electrode on the cell body and then an electrode on the dendrites and they evoke this action potential in the cell body.

And it travels backwards where we can see the delay in the dendrite, then it travels back and it's called the backpropagation action potential.

This is a signal that mediates plasticity.

You can do a much higher resolution view where you see what we call the presynaptic spike and you can look at the response and you can characterize that delay across many different connections.

Some cells will take a millisecond to communicate with another cell and other cells may take six milliseconds to communicate, in the same cell types we have this wide range of time delays between neurons.

The amplitude also varies greatly, up to about 12mV for this type of connection producing a depolarization around 1mV, which is what we call the excitatory postsynaptic potential.

Neurons communicate with a jitter, a range of 1ms to 6ms, and the amplitude can vary anything from 1mV to 12mV.

You can follow the axon and its collaterals here you can follow them, and you can identify where the axon is touching the dendrite of the other cell.

And those areas are what we call putative synapses, because at the light microscope level you can't identify, whether real synapses or not, you can tell that because the axon is passing through and touching a dendrite and the axon is forming what is called a button, this is very likely to be a synapse.

But this can be verified by looking at and doing an electron microscopy analysis.

In stochastic synaptic transmission, you look at how synapses work and you use methods called quantum analysis, where for example if just one vesicle or one synapse is fired you would see this kind of response and that would be called quantum size.

And on systems where these are very good and clearly separated you see these bumps.

But in the central nervous system, in the neocortex, because the synapses are in different places on the dendrites, it gets blurry it's very difficult to do what's called quantum analysis on the neurons of the central nervous system and it's important if you can do it, because you can identify how many vesicles, what is the amplitude, what we call the quantum size, how much impact does a single vesicle or a single synapse have on the postsynaptic side.

It is very difficult to get detailed characterizations of synaptic connections, you cannot always do electron microscopy.

Classical quantum analysis is quite difficult, because of the uncertainty about the locations distributed, the number of vesicles that are released, maybe one or two or three, and then you can have a lot of synapses.

Because of stochasticity, in both, latency and amplitude vary, with the action potential, and you have to keep in mind that it's a really noisy signal that's being transmitted from one neuron to another.

Experimentation, theory and knowledge generation is simulation science.

The rationale for the neuroscience of simulation is that it is a big data problem.

For brain simulation, finding shared MRI data, using KnowledgeGraph, a brain model was created, from extracted connectomes, and neural activity was simulated using brain network model simulators.

"Jupyter notebooks" were used on EBRAINS Collab platforms for front-end operations and supercomputers on the back-end for intensive number crunching.

In an artistic image of a synapse made in a neuroscience simulator called Jupyter that uses algorithms in Python language, we can see the extragalactic infinity of communications by synapses in natural neural networks with artificial ones in their very deep environment of quantum physics of particles.

The communication of synaptic transmission occurs through the conversion of electrical signals, which arrive through action potentials in the presynaptic button, into chemical signals, which consists of the release of neurotransmitters in the synaptic clefts, and, again, the conversion into electrical signals by through the formation of a new action potential in the postsynaptic neuron.

An example of how a Python analysis is built in the Yale Neuro simulator.

All morphologies and ontologies are used to model a conductance, for example, of the last extracellular layer in an artificial cell to simulate stimulation with extracellular electrodes with current flow in the space between the myelin and the axon.

Here, minimal calculations are made for neuroscience with Nernst and Hodgkin-Huxley equations.

The squid is a marine invertebrate that uses jet propulsion to move around.

By contractions of her mantle muscle, she controls an organ called a siphon, through which sea water enters or leaves in jets, causing it to move.

The mantle muscle is controlled by stellar ganglion neurons, which send long axons to innervate it.

The longest axon is also the thickest, which is why it is called a giant axon.

The squid's giant axon is an unmyelinated fiber with a diameter of around half a millimeter and several centimeters in length.

It is one of the largest known animal cells.

For comparison, vertebrate cells have diameters of a few micrometers.

Because of this, the squid's giant axon constitutes an ideal system for conducting experiments.

Researchers Hodgkin and Huxley won a Nobel Prize in Physiology or Medicine in 1962 for a mathematical model of making biological measurements, developed through his laboratory experiments with the "giant squid axon".

A conductance-based model is a mathematical model that describes how action potentials in neurons are initiated and propagated.

It is a set of nonlinear differential equations that approximates the electrical characteristics of excitable cells, such as neurons and cardiac myocytes, and is therefore a continuous-time model.

The axon of the squid, called the "giant axon", is a very large and thick axon, giant at the level of half a millimeter.

In our brain, our axons are very thin, something on the order of 1μm, a thousandth of a millimeter, very thin in diameter.

And that allowed Hodgkin and Huxley to record the electrical activity from inside this axon, because they were able to penetrate the axon with a wire and actually record the electrical activity or the voltage difference between the inside and the outside, directly, on this giant squid axon.

They actually used two tricks, one is called a "tension clamp" and the other is called a "space clamp".

So their idea was to take this wire, a long wire in the axon that becomes isopotential, because of this wire that is highly conductive.

There's no voltage drop between this place and then the whole elongated axon becomes electrically very compact because of this axial wire, but they also use tension pliers and that's the ingenious trick that, was invented by them and also by others, and the idea was to try to tighten, to determine the tension between the two sides of the membrane, to force the two sides of the membrane have a fixed tension: a tension clamp.

The lipid bilayer or bilipid layer is formed by the coupling of distinct, is represented as a capacitance, using a series of "voltage clamping" experiments and varying the concentrations of extracellular sodium and potassium, Hodgkin and Huxley developed a model in which the properties of an excitable cell are described by a set of four ordinary differential equations.

Because there are four state variables, visualizing the path in phase space can be difficult. Normally, two variables are chosen, the voltage and the potassium gating variable that allows visualizing the limit cycle.

This is an ad hoc method of visualizing the four-dimensional system.

This does not prove the existence of the limit cycle.

If the injected current is used as a bifurcation parameter, then the Hodgkin-Huxley model passes through a Hopf bifurcation.

As with most neuronal models, increasing the injected current will increase the neuron's firing rate.

A consequence of the Hopf bifurcation is that there is a minimum rate of fire.

This means that either the neuron is not firing at zero frequency, or it is firing at the minimum firing rate.

Because of the all-or-nothing law, there is not a gradual increase in the amplitude of the action potential, but a sudden "jump".

The resulting transition is known as a canard.

Several simplified neuronal models have also been developed, such as the FitzHugh-Nagumo model, neuronal firing models, a relaxation oscillator because, if the external stimulus exceeds a certain threshold value, the system will exhibit a characteristic path in phasic space, before the variables relax and return to their resting values, that is, one of the main mathematical models that describe the patterns with which action potentials are initiated and propagated in neurons.

The model suggested the creation of the system in 1961 and to J. Nagumo, who created the equivalent circuit, describing the prototype of an excitable system, for example, that of a neuron, facilitating the efficient "large-scale simulation" of groups of neurons, as well as a mathematical view on the dynamics of action potential generation.

But if I'm going to talk about quantum physics for neural network communication, then I'm going to talk about the process of releasing neurotransmitters and the driving force of synaptic channel closure in vesicles packed in traps called Quanta that, like a quantum trapping capacitor, generates a potential.

The brain circuit electronics is very similar to an industrial bridge rectifier circuit and the ions passing through the channel gates have analytical similarity to the ions from the electrodes and lasers being trapped in the quantum trap, so reasoning can navigate Mikowski's logic at Planck, on the workings of the subnetworks of the human brain, through the concept of the spatiotemporal structure of network couplings, presenting means for quantifiable coupling matrices within and between regions.

Ψ˙( x , t ) =N( Ψ ( x , t ) ) + ∫Γgl o c o l( x , x ′ ) S( Ψ ( x ′ , t ) ) dx ′ + ∫Γggl o b a lS( Ψ ( x ′ , t ?| x?x′ |ν) ) dx ′ + eu( x , t ) + ξ( x , t )

The equation describes the stochastic differential equation of a network of connected neural populations.

Ψ ( x , t ) is the activity vector of the neural population at location (x) in 3D physical space and time point (t).

It has as many state variables as are defined by the neural population model, which is specified by N( Ψ ( x , t ) ).

Connectivity distinguishes local and global connections, which are captured separately in two expressions.

Local network connectivity (g)local( x , x ' ) is described by connection weights between (x) and (t), while global connectivity is defined by (g)global( x , x ' ).

I'm still learning about NEURON which is a multi-compartmental and spatially extended neuroscientific modeling simulation environment with diverse populations of ion channels.

Quantifies similarity in kinetics between models using a series of standardized stress-fixation simulation and cluster analysis protocols, citation links and channel duplications, similarity of kinetics between channel models matched into family trees and dendrograms, which are searchable through the web interface.

I'm using "Yale's Neuron" for the first time, and I'm building this diagram myself.

I did a quick test, without worrying about exact values and I didn't find the sodium and potassium pumps, in the arrangement to be inserted, after the cytoplasmic and extracellular face.

I'm trying to build a series of single cell electrical models.

One of the big goals of Project Blue Brain is to work with many laboratories around the world to try to obtain single cell gene expression profiles.

In 3D simulation of a synapse, the reconstruction of a single neuron houses knowledge that is necessary for the Blue Gene technology, a computing architecture project, designed to produce several supercomputers capable of processing data at petaFLOPS speeds.

Supercomputers were designed and built for nuclear and particle physics simulations.

Simulation code, parallel code, algorithms are needed to use Yale's NEURON software and run large scale simulations on these supercomputers.

Neuron Atlas allows interactive 3D navigation where you can navigate through the entire structure with just one touch, similar to Neuro Morpho, Neuron Viewer.

General neuron locations are mapped in the Allen Reference Atlas.

Neuron Atlas is based on and adapted from the Allen Brain Institute's Brain Explorer app.

Contains a library of resources and tools for 3D reconstructions of neuronal morphology with metadata, morphometrics, research ontologies, literature and data.

Neuroinformatics and computational tools for neuroplasticity and neuroanatomy.

Researchers investigate the relationship between brain structure, activity and function from the subcellular to the network level, with a specific focus on the biophysical and biochemical mechanisms of learning and memory.

Blue Brain Project is a digital reconstruction of neocortical, presynaptic and postsynaptic microcircuits, focused on a circuit layer or morphological type of the anatomical and physiological properties of the somatosensory cortex of a juvenile "Rat" made by the Blue Brain Project.

In the mouse brain, you have about a thousand kilometers of fibers.

In the human brain about a million kilometers of fibers.

In the mouse brain, about a trillion synapses, in the human brain about a thousand trillion synapses.

Rat brain about one hundred million neurons, in the human brain, about one hundred billion neurons.

HBP Neuroinformatics is a large cognitive neuroscience and brain-inspired computing project, scientific research, based on exascale supercomputers, which aims to build a collaborative scientific research infrastructure, uses the HBP Core data model, simple and complex datasets of morph cells -electrical-molecular, after curating the data to make navigation available.

The Markram Lab captured traces that characterize the electrical behavior of single cells, synaptic connections and groups of cells and brain regions, data layout, and standardized electronic metadata.

The Electrophys Feature Extraction Library (IPython notebook - eFEL) allows neuroscientists to extract recorded time series data from neurons.

Data curation was done by BluePyOpt, a Python package, at many scales of neuroscience, mathematical models take the form of complex dynamical systems, aimed at the neuroscience community.

NeuroM provides morphometric analyzes to quantify the axonal and dendritic morphology properties of a neuron.

NeuroR identifies and repairs mandrels that were cut during slice preparation.

NeuroC generates clones of the repaired neurons by introducing statistical variations into the trees of each clone.

This project was carried out by the Backyard Brain's Neuron SpikerBox laboratory, from my first Neuroscience course, focused on Electrical Properties of the Neuron, Bioelectricity, at Harvard.

This laboratory allows communication through electrical experiments.

The live samples are anesthetized, doped, then the electrical stimulus is inserted and you can see the electromagnetic waves dancing on a computer and hear the sound of spikes, neural impulses, like music, from the live samples, plants, insects...

In this lab, I took classes in cadaver dissection, with electrical experiments, Frankenstein style.

This type of experiment is done, for example, to build technologies to cure diseases.

Underlying Principle of Deep Brain Stimulation in Parkinson's

The brain is an electrical device that generates its own electrical activity.

The principle is to electrically intervene with the erroneous activity in the Parkinsonian case.

Where the synchronous rhythm of activity is reflected in this disease.

Intervene in this region taking a battery, like the battery that people use to mark the heart, but in the Parkinsonian case, instead of connecting the battery wire to the heart, marking the heart, the wire now goes, as you can see in the figure , to the top and then to this specific region of the brain.

It's a relatively simple operation.

It's called Deep Brain Stimulation.

The battery, a set of stimuli, a set of pulses generated by the battery.

The doctor can manipulate the frequency and amplitude and so on of this stimulation.

It stimulates the Parkinsonian patient's brain deeply by electrical activity coming from the battery.

It is a very successful symptomatic treatment to repair Parkinson's disease or Parkinson's disease symptoms.

After being operated, and after being injected with electrical current, directly intervening in the electrical activity of the Parkinsonian patient's brain, inducing the stimuli.

This electrical stimulation of the battery in the head allows the patient to walk again almost normally, to be really balanced, to have hand movements, to drive, to come and go, to have free will to exist fully.

Simulation neuroscience develops all the biological algorithms, scientific processes and software needed to digitally reconstruct and simulate the brain.

The knowledge graph is built from the scientific activities that drive data science to overexpress electrophysiology, ion channel gene amplification, and validation of each cell line.

It is the integration of experimental data atlas literature, single cell electrical modeling, circuit construction and simulation.

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Since 2005

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