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Neurophysics
Neurophysics
Neurophysics
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Neurophysics (or neurobiophysics) is the branch of biophysics dealing with the development and use of physical methods to gain information about the nervous system. Neurophysics is an interdisciplinary science using physics and combining it with other neurosciences to better understand neural processes. The methods used include the techniques of experimental biophysics and other physical measurements such as EEG[1] mostly to study electrical, mechanical or fluidic properties, as well as theoretical and computational approaches.[2] The term "neurophysics" is a portmanteau of "neuron" and "physics".

Among other examples, the theorisation of ectopic action potentials in neurons using a Kramers-Moyal expansion[3] and the description of physical phenomena measured during an EEG using a dipole approximation[1] use neurophysics to better understand neural activity.

Another quite distinct theoretical approach considers neurons as having Ising model energies of interaction and explores the physical consequences of this for various Cayley tree topologies and large neural networks. In 1981, the exact solution for the closed Cayley tree (with loops) was derived by Peter Barth for an arbitrary branching ratio[4] and found to exhibit an unusual phase transition behavior[5] in its local-apex and long-range site-site correlations, suggesting that the emergence of structurally-determined and connectivity-influenced cooperative phenomena may play a significant role in large neural networks.

Recording techniques

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Old techniques to record brain activity using physical phenomena are already widespread in research and medicine. Electroencephalography (EEG) uses electrophysiology to measure electrical activity within the brain. This technique, with which Hans Berger first recorded brain electrical activity on a human in 1924,[6] is non-invasive and uses electrodes placed on the scalp of the patient to record brain activity. Based on the same principle, electrocorticography (ECoG) requires a craniotomy to record electrical activity directly on the cerebral cortex.

In the recent decades, physicists have come up with technologies and devices to image the brain and its activity. The Functional Magnetic Resonance Imaging (fMRI) technique, discovered by Seiji Ogawa in 1990,[7] reveals blood flow changes inside the brain. Based on the existing medical imaging technique Magnetic Resonance Imaging (MRI) and on the link between the neural activity and the cerebral blood flow, this tool enables scientists to study brain activities when they are triggered by a controlled stimulation. Another technique, the Two Photons Microscopy (2P), invented by Winfried Denk (for which he has been awarded the Brain Prize in 2015[8]), John H. Strickler and Watt W. Webb in 1990 at Cornell University,[9] uses fluorescent proteins and dyes to image brain cells. This technique combines the two-photon absorption, first theorized by Maria Goeppert-Mayer in 1931, with lasers. Today, this technique is widely used in research and often coupled with genetic engineering to study the behavior of a specific type of neuron.

Theories of consciousness

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Consciousness is still an unknown mechanism and theorists have yet to come up with physical hypotheses explaining its mechanisms. Some theories rely on the idea that consciousness could be explained by the disturbances in the cerebral electromagnetic field generated by the action potentials triggered during brain activity.[10] These theories are called electromagnetic theories of consciousness. Another group of hypotheses suggest that consciousness cannot be explained by classical dynamics but with quantum mechanics and its phenomena. These hypotheses are grouped into the idea of quantum mind and were first introduced by Eugene Wigner.

Neurophysics institutes

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Awards

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Among the list of prizes that reward neurophysicists for their contribution to neurology and related fields, the most notable one is the Brain Prize, whose last laureates are Adrian Bird and Huda Zoghbi for "their groundbreaking work to map and understand epigenetic regulation of the brain and for identifying the gene that causes Rett syndrome".[11] The other most relevant prizes that can be awarded to a neurophysicist are: the NAS Award in the Neurosciences, the Kavli Prize and to some extent the Nobel Prize in Physiology or Medicine. It can be noted that a Nobel Prize was awarded to scientists that developed techniques which contributed widely to a better understanding of the nervous system, such as Neher and Sakmann in 1991 for the patch clamp, and also to Lauterbur and Mansfield for their work on Magnetic resonance imaging (MRI) in 2003.

See also

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Books

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  • Wulfram Gerstner and Werner M. Kistler, Spiking Neuron Models, Single Neurons, Populations, Plasticity, Cambridge University Press (2002) Archived 2019-03-24 at the Wayback Machine ISBN 0-521-89079-9 ISBN 0-521-81384-0
  • Alwyn Scott, Neuroscience: A Mathematical Primer, Birkhäuser (2002) ISBN 0-387-95403-1
  • Graben, Peter; Zhou, Changsong; Thiel, Marco; Kurths, Jürgen (2008), "Foundations of Neurophysics", Lectures in Supercomputational Neurosciences, Berlin, Heidelberg: Springer, pp. 3–48, Bibcode:2008lsn..book.....G, doi:10.1007/978-3-540-73159-7, ISBN 978-3-540-73159-7

References

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Neurophysics

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Neurophysics is an interdisciplinary field that integrates principles and methods from physics with to investigate the fundamental mechanisms underlying neural processes and activity. It employs mathematical modeling, computational simulations, and theoretical frameworks to analyze complex systems in the , such as the of cognitive functions from the of billions of neurons. This approach shifts focus from isolated cellular or molecular events to the physical interactions and emergent properties of neural ensembles, treating the as a dynamic akin to a symphony of synchronized rhythms. Key concepts in neurophysics include the application of non-linear dynamics, , and to model phenomena like neural oscillations, , and information processing in networks. Researchers use techniques such as microelectrode recordings and simulations to capture patterns in hippocampal activity, revealing how place cells contribute to spatial and formation. These methods highlight the brain's capacity for emergent behaviors, where simple physical rules at the neuronal level give rise to complex cognitive outcomes, such as learning and . In practical applications, neurophysics addresses neurological disorders by exploring their physical underpinnings, including non-synaptic mechanisms in , the impacts of on epileptiform activity, and potential preventive strategies for (SUDEP). It also extends to neurodegenerative conditions like Alzheimer's and Parkinson's, aiming to decode disrupted neural rhythms and develop targeted interventions through computational predictions. Centers such as the Neurophysics Center “Professor Hiss Martins-Ferreira” in exemplify dedicated efforts, combining rigorous mathematics with clinical insights to advance treatments and train interdisciplinary experts. Overall, neurophysics bridges the gap between microscopic neural events and macroscopic function, offering a quantitative lens to unravel the physical basis of the mind.

Overview

Definition and Scope

Neurophysics is an interdisciplinary field that applies physical principles and methodologies to the study of neural processes within the , integrating concepts from physics such as , , and with to elucidate the underlying mechanisms of function and information processing. This approach focuses on the physical laws governing atomic and molecular interactions in neurons, enabling a quantitative understanding of how collections of atoms and molecules in the give rise to cognitive phenomena. For instance, synaptic transmission can be modeled using electrostatic forces, where repulsion between charged interfaces creates energy barriers that regulate release and prevent spontaneous activity. The scope of neurophysics emphasizes the analysis of neuron ensembles, neural rhythms, and emergent properties, such as synchronized oscillations and potentially , through the lens of physical laws like nonlinear dynamics and statistical physics. It investigates how collective behaviors in large-scale neural networks arise from individual neuronal interactions, excluding purely descriptive biological or psychological investigations that lack physical modeling or quantitative analysis. Tools like mathematical modeling of electromagnetic fields generated by synaptic currents help explain how these fields sharpen excitatory transmission and contribute to signal integration in neural circuits. The term "neurophysics" denotes this physics-centric approach to , distinguishing it from broader —which encompasses physical studies of all biological systems—and , which prioritizes functional descriptions over mechanistic physical modeling. Seminal works, such as those exploring the neurophysics of through synchronized neuronal discharges, highlight its focus on emergent phenomena driven by physical principles rather than isolated cellular or behavioral observations.

Interdisciplinary Connections

Neurophysics draws heavily from physics, particularly through the application of to model the collective behavior of neural networks, where principles like phase transitions and spin glasses help explain emergent properties in large-scale neuronal assemblies. Thermodynamic concepts are employed to analyze energy-efficient computation in the brain, revealing that communication between neurons consumes significantly more energy than local processing, with estimates indicating up to 35 times higher costs for synaptic transmission. further connects the fields by describing irregular neural firing patterns as deterministic yet unpredictable dynamics, enabling probabilistic computations in cortical circuits through sensitive dependence on initial conditions. In relation to neuroscience, neurophysics integrates with by developing physical models that simulate neural dynamics, such as and reaction-diffusion systems, to predict activity patterns grounded in biophysical constraints. Unlike , which addresses neurological disorders and therapeutic interventions, neurophysics emphasizes universal physical laws governing neural function, such as processes and force balances, without focusing on . Overlaps extend to , where dynamics are modeled using and gating to elucidate voltage-dependent conductance in neuronal membranes. Speculative proposals in , such as those exploring structures in neurons for potential quantum coherence and entanglement in information processing, represent a controversial intersection. In , neuromorphic hardware replicates physical properties like spiking dynamics and using analog circuits, achieving low-power emulation of neural computation. Neurophysics informs by translating brain physics—such as energy-minimizing network states—into silicon-based systems, inspiring efficient algorithms that mimic neural efficiency for tasks like . It also contributes to through holistic neural modeling that incorporates energy constraints and multiscale interactions, providing frameworks for understanding emergent cognition in biological networks.

Historical Development

Early Foundations

The foundations of neurophysics trace back to 19th-century , where early experiments revealed the electrical nature of biological tissues. In 1791, conducted pioneering studies on frog legs, observing that muscular contractions could be elicited by electrical discharges from or metal contacts, thereby demonstrating the existence of inherent bioelectricity in living organisms. These findings challenged prevailing views of and established as a fundamental physiological force, influencing subsequent inquiries into neural signaling. Building on this, advanced quantitative biophysical measurements in the 1850s by developing methods to determine ; using frog sciatic nerve-muscle preparations and mechanical chronoscopes, he calculated speeds of approximately 27 meters per second, applying physical principles of timing and distance to refute earlier assumptions of instantaneous neural transmission. Entering the early 20th century, the field progressed toward non-invasive techniques for monitoring neural electrical activity. In 1924, German psychiatrist achieved the first recording of electrical potentials using a connected to scalp electrodes, marking the invention of (EEG) and enabling the physical study of dynamics without surgical intervention. 's work laid essential groundwork for applying principles to , shifting focus from isolated preparations to holistic monitoring and highlighting rhythmic oscillations as quantifiable physical phenomena. A pivotal conceptual transition occurred mid-century, moving from descriptive anatomy to predictive physical modeling of neural processes. and , in their seminal 1952 studies on the , formulated a mathematical description of the action potential, attributing it to voltage-gated ionic currents through sodium and potassium channels. Their model integrated biophysical measurements of membrane conductance and , encapsulated in the core differential equation for dynamics: dVdt=I−gNam3h(V−ENa)−gKn4(V−EK)−gL(V−EL)Cm\frac{dV}{dt} = \frac{I - g_\mathrm{Na} m^3 h (V - E_\mathrm{Na}) - g_\mathrm{K} n^4 (V - E_\mathrm{K}) - g_\mathrm{L} (V - E_\mathrm{L})}{C_m} where VV is the , II is the applied current, gg terms represent conductances, gating variables (m,h,nm, h, n) describe channel states, EE values are reversal potentials, and CmC_m is membrane capacitance. This framework revolutionized neurophysics by enabling simulations of excitation and conduction. As foundational figures, Hodgkin and Huxley pioneered the application of —originally developed for telegraph lines—to neuronal geometry, modeling axons as distributed electrical circuits to explain signal propagation.

Modern Advances

In the mid-20th century, the patch-clamp technique revolutionized the study of neural channels by enabling the recording of electrical currents from single channels in s. Developed by Erwin Neher and Bert Sakmann in 1976, this method used a glass micropipette to form a high-resistance seal with the , allowing precise measurement of ionic currents at the level of individual channels. Their work earned the 1991 in Physiology or for demonstrating the function of channels fundamental to signaling. By the late , (fMRI) emerged as a non-invasive tool for mapping activity through blood-oxygen-level-dependent (BOLD) contrast. Introduced in 1990 by Seiji Ogawa and colleagues, fMRI detects changes in blood oxygenation levels, where deoxyhemoglobin acts as a paramagnetic agent that alters and shortens T2* relaxation times in MRI signals during neural activation. This technique provided spatiotemporal maps of brain function by leveraging the hemodynamic response to neuronal activity, without requiring exogenous contrast agents. Entering the 21st century, two-photon microscopy advanced deep-tissue neural imaging by minimizing photodamage and scattering in scattering tissues. Pioneered by Winfried Denk, James H. Strickler, and Watt W. Webb in 1990, the method employs infrared laser pulses to excite fluorescent indicators via , enabling high-resolution visualization of neural structures and activity up to several hundred micrometers deep in living brains. Complementing this, integrated optical physics with for precise control of neural activity. First demonstrated in 2005 by Edward S. Boyden, , and colleagues, it uses light-sensitive ion channels like channelrhodopsin-2—expressed via genetic targeting—to modulate neuron firing with millisecond precision using blue light illumination. Post-2010 developments in have enhanced the physical mapping of neural wiring through diffusion tensor imaging (DTI) combined with algorithms. DTI, which infers tract orientations from water anisotropy, has been refined by initiatives like the (launched 2010) to reconstruct at the millimeter scale. techniques, such as deep neural networks for fiber , have improved accuracy in resolving crossing fibers and reducing false positives in reconstructions, as shown in studies analyzing multi-shell data. Concurrently, the , launched in 2013, has driven AI integration for simulating large-scale brain physics. As of 2025, it has facilitated significant progress toward scalable models that incorporate biophysical dynamics across millions of neurons through advances in tools.

Core Concepts and Principles

Biophysical Properties of Neurons

Neuron membranes consist of bilayers that separate the intracellular and extracellular environments, exhibiting a specific of approximately 1 μF/cm² due to the properties of the lipid layer, which is typically 5-10 nm thick. This , along with the membrane's resistance to ion flow, forms the basis for the electrical excitability of neurons, allowing the storage and rapid discharge of charge during signaling events. The resting arises from unequal distributions across the bilayer, maintained by active pumps, creating electrochemical gradients essential for action potentials. Action potentials are enabled by these ion gradients, particularly for sodium (Na⁺) and potassium (K⁺), where the equilibrium potential for each ion species is described by the Nernst equation: Eion=RTzFln⁡([ion]out[ion]in)E_{\text{ion}} = \frac{RT}{zF} \ln \left( \frac{[\text{ion}]_{\text{out}}}{[\text{ion}]_{\text{in}}} \right) Here, RR is the gas constant, TT is the absolute temperature, zz is the ion valence, and FF is Faraday's constant; for mammalian neurons at 37°C, this simplifies to approximately Eion=58log⁡10([ion]out[ion]in)E_{\text{ion}} = 58 \log_{10} \left( \frac{[\text{ion}]_{\text{out}}}{[\text{ion}]_{\text{in}}} \right) mV. During depolarization, voltage-gated channels open, permitting ion influx that propagates the potential change, as modeled in foundational work like the Hodgkin-Huxley equations. In synaptic transmission, the physics of neurotransmitter release involves electrostatic repulsion arising from negatively charged in both the vesicle and presynaptic , which creates an energy barrier that prevents spontaneous fusion. This barrier is overcome by calcium influx triggering SNARE complex formation, which generates forces to drive vesicle docking and fusion with the presynaptic . These repulsive forces ensure that release is tightly controlled by action potential-triggered Ca²⁺ entry. Vesicle dynamics prior to release are governed by Brownian motion in the cytoplasm, where synaptic vesicles undergo diffusive transport with a diffusion coefficient given by the Stokes-Einstein relation for spherical particles: D=kT6πηrD = \frac{kT}{6\pi \eta r} where kk is Boltzmann's constant, TT is , η\eta is the cytoplasmic (approximately 2-5 times that of ), and rr is the vesicle radius (around 20-40 nm), yielding D≈0.1−1 μm2/sD \approx 0.1-1 \, \mu\text{m}^2/\text{s}. This diffusion allows vesicles to explore the presynaptic terminal and position for release, interspersed with active motor-driven transport along . The electrical properties of neurons facilitate signal propagation along axons via cable theory, which treats the axon as a cylindrical cable with distributed membrane resistance rmr_m (in Ω·cm) and axial resistance rir_i (in Ω·cm). The length constant λ\lambda, representing the distance over which a steady-state voltage decays to 1/e1/e of its initial value, is: λ=rmri\lambda = \sqrt{\frac{r_m}{r_i}}
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