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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of computational neuroscience 2 (1995), S. 63-82 
    ISSN: 1573-6873
    Keywords: voltage threshold ; current threshold ; action potential initiation ; integrate-and-fire models ; single cell models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Medicine , Physics
    Notes: Abstract The majority of neural network models consider the output of single neurons to be a continuous, positive, and saturating firing ratef(t), while a minority treat neuronal output as a series of delta pulses ∑δ (t — t i ). We here argue that the issue of the proper output representation relates to the biophysics of the cells in question and, in particular, to whether initiation of somatic action potentials occurs when a certain thresholdvoltage or a thresholdcurrent is exceeded. We approach this issue using numerical simulations of the electrical behavior of a layer 5 pyramidal cell from cat visual cortex. The dendritic tree is passive while the cell body includes eight voltage- and calcium-dependent membrane conductances. We compute both the steady-state (I ∞ static (V m )) and the instantaneous (I o (Vm)) I–V relationships and argue that the amplitude of the local maximum inI ∞ static (V m ) corresponds to the current thresholdI th for sustained inputs, while the location of the middle zero-crossing ofI o corresponds to a fixed voltage thresholdV th for rapid inputs. We confirm this using numerical simulations: for “rapid” synaptic inputs, spikes are initiated if the somatic potential exceedsV th, while for slowly varying inputI th must be exceeded. Due to the presence of the large dendritic tree, no charge thresholdQ th exists for physiological input. Introducing the temporal average of the somatic membrane potential 〈(V m)〉 while the cell is spiking repetitively, allows us to define a dynamic I-V relationship ∞ dynamic (〈(V m)〉). We find an exponential relationship between 〈(V m)〉 and the net current sunk by the somatic membrane during spiking (diode-like behavior). The slope ofI∞/dynamic(〈(V m))〉 allows us to define a dynamic input conductance and a time constant that characterizes how rapidly the cell changes its output firing frequency in response to a change in its input.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of computational neuroscience 9 (2000), S. 133-148 
    ISSN: 1573-6873
    Keywords: membrane noise ; active ion channels ; Markov kinetic models ; stochastic ion channels
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Medicine , Physics
    Notes: Abstract Voltage-gated ion channels in neuronal membranes fluctuate randomly between different conformational states due to thermal agitation. Fluctuations between conducting and nonconducting states give rise to noisy membrane currents and subthreshold voltage fluctuations and may contribute to variability in spike timing. Here we study subthreshold voltage fluctuations due to active voltage-gated Na+ and K+ channels as predicted by two commonly used kinetic schemes: the Mainen et al. (1995) (MJHS) kinetic scheme, which has been used to model dendritic channels in cortical neurons, and the classical Hodgkin-Huxley (1952) (HH) kinetic scheme for the squid giant axon. We compute the magnitudes, amplitude distributions, and power spectral densities of the voltage noise in isopotential membrane patches predicted by these kinetic schemes. For both schemes, noise magnitudes increase rapidly with depolarization from rest. Noise is larger for smaller patch areas but is smaller for increased model temperatures. We contrast the results from Monte Carlo simulations of the stochastic nonlinear kinetic schemes with analytical, closed-form expressions derived using passive and quasi-active linear approximations to the kinetic schemes. For all subthreshold voltage ranges, the quasi-active linearized approximation is accurate within 8% and may thus be used in large-scale simulations of realistic neuronal geometries.
    Type of Medium: Electronic Resource
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