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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 15:36:32 +0000Synaptic Transmission: An Information-Theoretic Perspective
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103246428
Authors: {'items': [{'id': 'Manwani-A', 'name': {'family': 'Manwani', 'given': 'Amit'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}]}
Year: 1998
DOI: 10.48550/arXiv.0309030
Here we analyze synaptic transmission from an information-theoretic perspective. We derive close-form expressions for the lower-bounds on the capacity of a simple model of a cortical synapse under two explicit coding paradigms. Under the "signal estimation" paradigm, we assume the signal to be encoded in the mean firing rate of a Poisson neuron. The performance of an optimal linear estimator of the signal then provides a lower bound on the capacity for signal estimation. Under the "signal detection" paradigm, the presence or absence of the signal has to be detected. Performance of the optimal spike detector allows us to compute a lower bound on the capacity for signal detection. WE find that single synapses (for empirically measured parameter values) transmit information poorly but significant improvement can be achieved with a small amount of redundancy.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/esyn8-v6t81Journal of Near-Death Studies
https://resolver.caltech.edu/CaltechAUTHORS:20160229-162534873
Authors: {'items': [{'id': 'Manwani-A', 'name': {'family': 'Manwani', 'given': 'Amit'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}]}
Year: 1999
Here we derive measures quantifying the information loss of a synaptic signal due to the presence of neuronal noise sources, as it electrotonically propagates along a weakly-active dendrite. We model the dendrite as an infinite linear cable, with noise sources distributed along its length. The noise sources we consider are thermal noise, channel noise arising from the stochastic nature of voltage-dependent ionic channels (K^+ and Na^+) and synaptic noise due to spontaneous background activity. We assess the
efficacy of information transfer using a signal detection paradigm where the objective is to detect the presence/absence of a presynaptic spike from
the post-synaptic membrane voltage. This allows us to analytically assess the role of each of these noise sources in information transfer. For our choice of parameters, we find that the synaptic noise is the dominant
noise source which limits the maximum length over which information be reliably transmitted.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/f2mjj-tn076Channel noise in excitable neuronal membranes
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103247343
Authors: {'items': [{'id': 'Manwani-A', 'name': {'family': 'Manwani', 'given': 'Amit'}}, {'id': 'Steinmetz-P-N', 'name': {'family': 'Steinmetz', 'given': 'Peter N.'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}]}
Year: 2000
Stochastic fluctuations of voltage-gated ion channels generate current
and voltage noise in neuronal membranes. This noise may be a critical
determinant of the efficacy of information processing within neural
systems. Using Monte-Carlo simulations, we carry out a systematic investigation
of the relationship between channel kinetics and the resulting
membrane voltage noise using a stochastic Markov version of the
Mainen-Sejnowski model of dendritic excitability in cortical neurons.
Our simulations show that kinetic parameters which lead to an increase
in membrane excitability (increasing channel densities, decreasing temperature)
also lead to an increase in the magnitude of the sub-threshold
voltage noise. Noise also increases as the membrane is depolarized from
rest towards threshold. This suggests that channel fluctuations may interfere
with a neuron's ability to function as an integrator of its synaptic
inputs and may limit the reliability and precision of neural information
processing.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/q9yge-sw084Subthreshold Voltage Noise Due to Channel Fluctuations in Active Neuronal Membranes
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103229674
Authors: {'items': [{'id': 'Steinmetz-P-N', 'name': {'family': 'Steinmetz', 'given': 'Peter N.'}}, {'id': 'Manwani-A', 'name': {'family': 'Manwani', 'given': 'Amit'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}, {'id': 'London-M', 'name': {'family': 'London', 'given': 'Michael'}}, {'id': 'Segev-I', 'name': {'family': 'Segev', 'given': 'Idan'}}]}
Year: 2000
DOI: 10.1023/A:1008967807741
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.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/x5gnf-vsz41Variability and coding efficiency of noisy neural spike encoders
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103229923
Authors: {'items': [{'id': 'Steinmetz-P-N', 'name': {'family': 'Steinmetz', 'given': 'Peter N.'}}, {'id': 'Manwani-A', 'name': {'family': 'Manwani', 'given': 'Amit'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}]}
Year: 2001
DOI: 10.1016/S0303-2647(01)00139-3
Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders — an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na+ and K+ channels obeying Hodgkin–Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/zmc2s-bag65