2,456 research outputs found
Adaptive Transition Rates in Excitable Membranes
Adaptation of activity in excitable membranes occurs over a wide range of timescales. Standard computational approaches handle this wide temporal range in terms of multiple states and related reaction rates emanating from the complexity of ionic channels. The study described here takes a different (perhaps complementary) approach, by interpreting ion channel kinetics in terms of population dynamics. I show that adaptation in excitable membranes is reducible to a simple Logistic-like equation in which the essential non-linearity is replaced by a feedback loop between the history of activation and an adaptive transition rate that is sensitive to a single dimension of the space of inactive states. This physiologically measurable dimension contributes to the stability of the system and serves as a powerful modulator of input–output relations that depends on the patterns of prior activity; an intrinsic scale free mechanism for cellular adaptation that emerges from the microscopic biophysical properties of ion channels of excitable membranes
The impact of spike timing variability on the signal-encoding performance of neural spiking models
It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance
Toward a dynamical systems analysis of neuromodulation
This work presents some first steps toward a
more thorough understanding of the control systems
employed in evolutionary robotics. In order
to choose an appropriate architecture or to construct
an effective novel control system we need
insights into what makes control systems successful,
robust, evolvable, etc. Here we present analysis
intended to shed light on this type of question
as it applies to a novel class of artificial neural
networks that include a neuromodulatory mechanism:
GasNets.
We begin by instantiating a particular GasNet
subcircuit responsible for tuneable pattern generation
and thought to underpin the attractive
property of “temporal adaptivity”. Rather than
work within the GasNet formalism, we develop an
extension of the well-known FitzHugh-Nagumo
equations. The continuous nature of our model
allows us to conduct a thorough dynamical systems
analysis and to draw parallels between this
subcircuit and beating/bursting phenomena reported
in the neuroscience literature.
We then proceed to explore the effects of different
types of parameter modulation on the system
dynamics. We conclude that while there are
key differences between the gain modulation used
in the GasNet and alternative schemes (including
threshold modulation of more traditional synaptic
input), both approaches are able to produce
tuneable pattern generation. While it appears, at
least in this study, that the GasNet’s gain modulation
may not be crucial to pattern generation ,
we go on to suggest some possible advantages it
could confer
Channel noise in excitable neuronal membranes
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
Does Corticothalamic Feedback Control Cortical Velocity Tuning?
The thalamus is the major gate to the cortex and its contribution to cortical
receptive field properties is well established. Cortical feedback to the
thalamus is, in turn, the anatomically dominant input to relay cells, yet its
influence on thalamic processing has been difficult to interpret. For an
understanding of complex sensory processing, detailed concepts of the
corticothalamic interplay need yet to be established. To study
corticogeniculate processing in a model, we draw on various physiological and
anatomical data concerning the intrinsic dynamics of geniculate relay neurons,
the cortical influence on relay modes, lagged and nonlagged neurons, and the
structure of visual cortical receptive fields. In extensive computer
simulations we elaborate the novel hypothesis that the visual cortex controls
via feedback the temporal response properties of geniculate relay cells in a
way that alters the tuning of cortical cells for speed.Comment: 31 pages, 7 figure
Does Corticothalamic Feedback Control Cortical Velocity Tuning?
The thalamus is the major gate to the cortex and its contribution to cortical receptive field properties is well established. Cortical feedback to the thalamus is, in turn, the anatomically dominant input to relay cells, yet its influence on thalamic processing has been difficult to interpret. For an understanding of complex sensory processing, detailed concepts of the corticothalamic interplay need yet to be established. To study corticogeniculate processing in a model, we draw on various physiological and anatomical data concerning the intrinsic dynamics of geniculate relay neurons, the cortical influence on relay modes, lagged and nonlagged neurons, and the structure of visual cortical receptive fields. In extensive computer simulations we elaborate the novel hypothesis that the visual cortex controls via feedback the temporal response properties of geniculate relay cells in a way that alters the tuning of cortical cells for speed
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