1 / 20

Neuroprosthetics

Neuroprosthetics. Week 4 Neuron Modelling. Implants excite neurons. Retina – ganglion or bipolar cells Cochlea/Ear – spiral ganglion cells Motor prostheses – nerve-muscle junction In each example – interface between electrode and neuronal membrane. Passive properties of neuronal membrane.

terah
Download Presentation

Neuroprosthetics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Neuroprosthetics Week 4 Neuron Modelling

  2. Implants excite neurons • Retina – ganglion or bipolar cells • Cochlea/Ear – spiral ganglion cells • Motor prostheses – nerve-muscle junction • In each example – interface between electrode and neuronal membrane

  3. Passive properties of neuronal membrane • Resistance from intra and extra cellular fluids • Capacitance of membrane (like a cable) • Combination means spatial and temporal filtering of voltage signals • Typical low pass RC circuit – losses/fidelity • Spinal motor neurons or axons from retina ganglion to thalamus in brain must reliably carry signals with a frequency up to 4KHz/1KHz for up to 1 metre

  4. Passive limitations • Rise and fall of signals given by: • V(t) = V(0)exp(-t/T) where T = RC • Typical RC = 1 to 100msec – so voltage changes are slowed • Same equation for distance that a signal can be detected: • V(x) = V(0)exp(-x/X) where X = length constant • Typical X is a few hundred micrometers

  5. Passive response • Voltage profile for a constant current on peripheral nerve of KW

  6. Active Membranes • Active membranes overcome temporal and spatial degradations • Ionic gradients exist between the inside and outside of cells • Exchanges between sodium, calcium and potassium – ions driven in and out of cells • Action potential – brief, transient, regenerating depolarization • Resting potential typically -70mV. External stimulus brings membrane to threshold. Cell fires or not, peak amplitude may reach +40mV

  7. Ion channels • “Whole cell” currents represent the ensemble of thousands of individual channels • Thousands of individual ion channels are responsible for membrane conductance changes • Channels are selective for different types of ions

  8. Gating • Time dependence of the opening and closing of a channel • Probability of finding a channel in an open or closed state – as a function of: • membrane potential • the presence of a drug or neurotransmitter

  9. Permeation • Conductive properties of a channel in terms of its selectivity for specific ions • The rate at which ions can pass through the channel (hence max current) • Effects of blocking drugs

  10. Permeation • Conductive properties of a channel in terms of its selectivity for specific ions • The rate at which ions can pass through the channel (hence max current) • Effects of blocking drugs

  11. Nerve Tissue

  12. Membrane voltage • The main equation for stimulation of the Soma is always: • I(st) = I(io) + C dV/dt • One part of the current loads the cell membrane capacity and the other part passes through the ion channels • Alternatively: dV/dt = [ I(st) – I(io) ]/C • A positive stimulating current causes V to increase • To generate a spike this current must cause V to reach its threshold value

  13. Threshold • Once the threshold voltage is reached many of the (sodium) ion channels open • The voltage increases to an action potential without the need for further stimulation • Once the threshold is reached the stimulus can be switched off • Alternatively, once the threshold is reached increasing the stimulating current further has little/no effect • But different cells have different threshold values – depends on size of axons and somas

  14. Axon models • Operation of axons have been modelled extensively for e.g. squid, frogs, rabbits and rats • An expression for human nerve fibres is given by: • dV/dt = [ -I(Na)-I(K)-I(L)+I(st) ]/C • Where I(L) is a leakage current • Each current is then defined by means of a complex minimum (first) order equation

  15. Temperature effects • Usually membrane model data is gathered at low temperatures • Raising the temperature generally causes a shortening of the action potential and an increase in spike propagation velocity • For temperatures higher than 31 to 33 degC action potentials no longer propagate in squid axons • In warm blooded animals spike durations shorten considerably – but no heat block • Threshold levels change – warmer means easier to excite!

  16. Compartment models • Pieces of neuron can be treated as elements • A whole neuron is represented by an electrical network • Currents injected then can be treated with Kirchoffs law • Resistances become internal resistances of neighbouring compartments • Modeller must decide about degree of complexity • Much research in this area!

  17. Model variability • Large variability in neuron models due partly to the large variability in neurons • Example: absolute threshold current at the soma for a point source stimulation was: • Passive model 32.9 microA • Hodgkin-Huxley model 43 microA • FCM(5 ion channels) model 71 microA • Compare with our studies (human)80 to 100 microA • Passive (based on RC) – HH (based on squids)

  18. Problems • Selective stimulation of neural tissue is an enormous challenge • Example: in bladder control – activation of the detrusor muscle without activation of the urethal sphincter • Every type of neuron exhibits different operating characteristics – big problem in modelling/simulation • Neural geometry is complex, leading to complex models which require a high computational effort even for simple studies • External stimulation/monitoring very limited

More Related