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Understanding Neuronal Biophysics: The Human Brain's Complexity

This overview explores the intricate biophysics of the human brain, the most advanced information processing system in nature. It covers the various brain regions—forebrain, midbrain, cerebellum, hindbrain—and their functions, from cognition and emotion to involuntary controls and reflexes. The brain's remarkable capabilities, such as processing vast amounts of information and generating action potentials through neurons, are examined, alongside comparisons to computational machines. The discussion also touches on resting membrane potentials, synaptic transmission, and the perceptron model in neural calculations.

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Understanding Neuronal Biophysics: The Human Brain's Complexity

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  1. Elements of Neuronal Biophysics 2006

  2. The human brain Seat of consciousness and cognition Perhaps the most complex information processing machine in nature Historically, considered as a monolithic information processing machine

  3. Forebrain (Cerebral Cortex): Language, maths, sensation, movement, cognition, emotion Midbrain: Information Routing; involuntary controls Cerebellum: Motor Control Hindbrain: Control of breathing, heartbeat, blood circulation Spinal cord: Reflexes, information highways between body & brain Beginner’s Brain Map

  4. Brain : a computational machine? Information processing: brains vs computers - brains better at perception / cognition - slower at numerical calculations • Evolutionarily, brain has developed algorithms most suitable for survival • Algorithms unknown: the search is on • Brain astonishing in the amount of information it processes • Typical computers: 109 operations/sec • Housefly brain: 1011 operations/sec

  5. Brain facts & figures • Basic building block of nervous system: nerve cell (neuron) • ~ 1012 neurons in brain • ~ 1015 connections between them • Connections made at “synapses” • The speed: events on millisecondscale in neurons, nanosecond scale in silicon chips

  6. Neuron - “classical” • Dendrites • Receiving stations of neurons • Don't generate action potentials • Cell body • Site at which information received is integrated • Axon • Generate and relay action potential • Terminal • Relays information to next neuron in the pathway http://www.educarer.com/images/brain-nerve-axon.jpg

  7. Membrane Biophysics: OverviewPart 1: Resting membrane potential

  8. _ ECF -40 to -90 mV + ICF Resting Membrane Potential • Measurement of potential between ICF and ECF • Vm = Vi - Vo • ICF and ECF at isopotential separately. • ECF and ICF are different from each other.

  9. ECF -40 to -90 mV _ ICF + KCl Resting Membrane Potential - recording • Electrode wires can not be inserted in the cells without damaging them (cell membrane thickness: 7nm) • Solution: Glass microelectrodes (Tip diameter: 10 nm) • Glass  Non conductor • Therefore, while pulling a capillary after heating, it is filled with KCl and tip of electrode is open and KCl is interfaced with a wire.

  10. R.m.p. - towards a theory • Ionic concentration gradients across biological cell membrane

  11. Trans-membrane Ionic Distributions

  12. Resting potential as a K+ equilibrium (Nernst) potential

  13. Resting Membrane Potential: Nernst Eqn Consider values for typical concentration ratios EK = -90 mV ENa = +60 mV r.m.p. = {-60 to –80} mV

  14. Goldman-Hodgkin-Katz (GHK) eqn Taking values of R,T &F and dividing throughout by PK: Consider  = V. large, v. small, and intermediate

  15. Out g g Na K C V m m E E Na K In Equivalent Circuit Model: Resting Membrane

  16. Membrane Biophysics: OverviewPart 2: Action potential

  17. ACTION POTENTIAL

  18. ACTION POTENTIAL: Ionic mechanisms

  19. Action Potential: Na+ and K+ Conductance

  20. Membrane Biophysics: OverviewPart 3: Synaptic transmission & potentials

  21. “Canonical” neurons: Neuroscience

  22. Neurotransmitter Receptors Chemical Transmission

  23. Postsynaptic Electrical Effects

  24. Synaptic Integration: The Canonical Picture Action potential: Output signal Axon: Output line Action potential

  25. The Perceptron Model A perceptron is a computing element with input lines having associated weights and the cell having a threshold value. The perceptron model is motivated by the biological neuron. Output = y Threshold = θ w1 wn Wn-1 x1 Xn-1

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