552 research outputs found
Efficiency characterization of a large neuronal network: a causal information approach
When inhibitory neurons constitute about 40% of neurons they could have an
important antinociceptive role, as they would easily regulate the level of
activity of other neurons. We consider a simple network of cortical spiking
neurons with axonal conduction delays and spike timing dependent plasticity,
representative of a cortical column or hypercolumn with large proportion of
inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics
and it is interconnected randomly to other neurons. The network dynamics is
investigated estimating Bandt and Pompe probability distribution function
associated to the interspike intervals and taking different degrees of
inter-connectivity across neurons. More specifically we take into account the
fine temporal ``structures'' of the complex neuronal signals not just by using
the probability distributions associated to the inter spike intervals, but
instead considering much more subtle measures accounting for their causal
information: the Shannon permutation entropy, Fisher permutation information
and permutation statistical complexity. This allows us to investigate how the
information of the system might saturate to a finite value as the degree of
inter-connectivity across neurons grows, inferring the emergent dynamical
properties of the system.Comment: 26 pages, 3 Figures; Physica A, in pres
Multiscale relevance and informative encoding in neuronal spike trains
Neuronal responses to complex stimuli and tasks can encompass a wide range of
time scales. Understanding these responses requires measures that characterize
how the information on these response patterns are represented across multiple
temporal resolutions. In this paper we propose a metric -- which we call
multiscale relevance (MSR) -- to capture the dynamical variability of the
activity of single neurons across different time scales. The MSR is a
non-parametric, fully featureless indicator in that it uses only the time
stamps of the firing activity without resorting to any a priori covariate or
invoking any specific structure in the tuning curve for neural activity. When
applied to neural data from the mEC and from the ADn and PoS regions of
freely-behaving rodents, we found that neurons having low MSR tend to have low
mutual information and low firing sparsity across the correlates that are
believed to be encoded by the region of the brain where the recordings were
made. In addition, neurons with high MSR contain significant information on
spatial navigation and allow to decode spatial position or head direction as
efficiently as those neurons whose firing activity has high mutual information
with the covariate to be decoded and significantly better than the set of
neurons with high local variations in their interspike intervals. Given these
results, we propose that the MSR can be used as a measure to rank and select
neurons for their information content without the need to appeal to any a
priori covariate.Comment: 38 pages, 16 figure
Models wagging the dog: are circuits constructed with disparate parameters?
In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken
The what and where of adding channel noise to the Hodgkin-Huxley equations
One of the most celebrated successes in computational biology is the
Hodgkin-Huxley framework for modeling electrically active cells. This
framework, expressed through a set of differential equations, synthesizes the
impact of ionic currents on a cell's voltage -- and the highly nonlinear impact
of that voltage back on the currents themselves -- into the rapid push and pull
of the action potential. Latter studies confirmed that these cellular dynamics
are orchestrated by individual ion channels, whose conformational changes
regulate the conductance of each ionic current. Thus, kinetic equations
familiar from physical chemistry are the natural setting for describing
conductances; for small-to-moderate numbers of channels, these will predict
fluctuations in conductances and stochasticity in the resulting action
potentials. At first glance, the kinetic equations provide a far more complex
(and higher-dimensional) description than the original Hodgkin-Huxley
equations. This has prompted more than a decade of efforts to capture channel
fluctuations with noise terms added to the Hodgkin-Huxley equations. Many of
these approaches, while intuitively appealing, produce quantitative errors when
compared to kinetic equations; others, as only very recently demonstrated, are
both accurate and relatively simple. We review what works, what doesn't, and
why, seeking to build a bridge to well-established results for the
deterministic Hodgkin-Huxley equations. As such, we hope that this review will
speed emerging studies of how channel noise modulates electrophysiological
dynamics and function. We supply user-friendly Matlab simulation code of these
stochastic versions of the Hodgkin-Huxley equations on the ModelDB website
(accession number 138950) and
http://www.amath.washington.edu/~etsb/tutorials.html.Comment: 14 pages, 3 figures, review articl
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