2,670 research outputs found
A statistical model for in vivo neuronal dynamics
Single neuron models have a long tradition in computational neuroscience.
Detailed biophysical models such as the Hodgkin-Huxley model as well as
simplified neuron models such as the class of integrate-and-fire models relate
the input current to the membrane potential of the neuron. Those types of
models have been extensively fitted to in vitro data where the input current is
controlled. Those models are however of little use when it comes to
characterize intracellular in vivo recordings since the input to the neuron is
not known. Here we propose a novel single neuron model that characterizes the
statistical properties of in vivo recordings. More specifically, we propose a
stochastic process where the subthreshold membrane potential follows a Gaussian
process and the spike emission intensity depends nonlinearly on the membrane
potential as well as the spiking history. We first show that the model has a
rich dynamical repertoire since it can capture arbitrary subthreshold
autocovariance functions, firing-rate adaptations as well as arbitrary shapes
of the action potential. We then show that this model can be efficiently fitted
to data without overfitting. Finally, we show that this model can be used to
characterize and therefore precisely compare various intracellular in vivo
recordings from different animals and experimental conditions.Comment: 31 pages, 10 figure
A Spatial Agent-Based Model to Explore Scenarios of Adaptation to Climate Change in an Alpine Tourism Destination
A vast body of literature suggests that the European Alpine region may be one of the most sensitive to climate change impacts. Adaptation to climate change of Alpine socio-ecosystems is increasingly becoming an issue of interest for the scientific community while the people of the Alps are often unaware of or simply ignore the problem. ClimAlpTour is a European research project of the Alpine Space Programme, bringing together institutions and scholars from all countries of the Alpine arch, in view of dealing with the expected decrease in snow and ice cover, which may lead to a rethinking of tourism development beyond the traditional vision of winter sports. The research reported herein analyses the municipality of Auronzo di Cadore (22,000 ha) in the Dolomites under the famous peaks of the “Tre Cime di Lavaredo”. The local economy depends on tourism which is currently focused on the summer season, while the winter season is weak. As a whole, the destination receives approximately 65,000 guests per year with a resident population of 3,600 inhabitants. Since recently the Community Council is considering options on how to stimulate a further development of the winter tourism. This paper refers to a prototype agent-based model, called AuronzoWinSim, for the assessment of alternative scenarios of future local development strategies, taking into account complex spatial and social dynamics and interactions. Different typologies of winter tourists compose the set of human agents. Climate change scenarios are used to produce temperature and snow cover projections. The model is mainly informed by secondary sources, including demographic and economic time series, and biophysical data which feed-in its spatial dimension. Primary data from field surveys are used to calibrate the main parameters. AuronzoWinSim is planned for use in a participatory context with groups of local stakeholders.Alpine Winter Tourism, Spatial Agent-Based Model, Climate Change Adaptation
The Hitchhiker's Guide to Nonlinear Filtering
Nonlinear filtering is the problem of online estimation of a dynamic hidden
variable from incoming data and has vast applications in different fields,
ranging from engineering, machine learning, economic science and natural
sciences. We start our review of the theory on nonlinear filtering from the
simplest `filtering' task we can think of, namely static Bayesian inference.
From there we continue our journey through discrete-time models, which is
usually encountered in machine learning, and generalize to and further
emphasize continuous-time filtering theory. The idea of changing the
probability measure connects and elucidates several aspects of the theory, such
as the parallels between the discrete- and continuous-time problems and between
different observation models. Furthermore, it gives insight into the
construction of particle filtering algorithms. This tutorial is targeted at
scientists and engineers and should serve as an introduction to the main ideas
of nonlinear filtering, and as a segway to more advanced and specialized
literature.Comment: 64 page
The Neural Particle Filter
The robust estimation of dynamically changing features, such as the position
of prey, is one of the hallmarks of perception. On an abstract, algorithmic
level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing
signals based on the history of observations, provides a mathematical framework
for dynamic perception in real time. Since the general, nonlinear filtering
problem is analytically intractable, particle filters are considered among the
most powerful approaches to approximating the solution numerically. Yet, these
algorithms prevalently rely on importance weights, and thus it remains an
unresolved question how the brain could implement such an inference strategy
with a neuronal population. Here, we propose the Neural Particle Filter (NPF),
a weight-less particle filter that can be interpreted as the neuronal dynamics
of a recurrently connected neural network that receives feed-forward input from
sensory neurons and represents the posterior probability distribution in terms
of samples. Specifically, this algorithm bridges the gap between the
computational task of online state estimation and an implementation that allows
networks of neurons in the brain to perform nonlinear Bayesian filtering. The
model captures not only the properties of temporal and multisensory integration
according to Bayesian statistics, but also allows online learning with a
maximum likelihood approach. With an example from multisensory integration, we
demonstrate that the numerical performance of the model is adequate to account
for both filtering and identification problems. Due to the weightless approach,
our algorithm alleviates the 'curse of dimensionality' and thus outperforms
conventional, weighted particle filters in higher dimensions for a limited
number of particles
Stall Pattern Avoidance in Polynomial Product Codes
Product codes are a concatenated error-correction scheme that has been often
considered for applications requiring very low bit-error rates, which demand
that the error floor be decreased as much as possible. In this work, we
consider product codes constructed from polynomial algebraic codes, and propose
a novel low-complexity post-processing technique that is able to improve the
error-correction performance by orders of magnitude. We provide lower bounds
for the error rate achievable under post processing, and present simulation
results indicating that these bounds are tight.Comment: 4 pages, 2 figures, GlobalSiP 201
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes
We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter. The estimation is to be done online, i.e. the parameter estimate should be updated recursively based on the observation filtration. Here, we use an old but under-exploited representation of the incomplete-data log-likelihood function in terms of the filter of the hidden state from the observations. By performing a stochastic gradient ascent, we obtain a fully recursive algorithm for the time evolution of the parameter estimate. We prove the convergence of the algorithm under suitable conditions regarding the ergodicity of the process consisting of state, filter, and tangent filter. Additionally, our parameter estimation is shown numerically to have the potential of improving suboptimal filters, and can be applied even when the system is not identifiable due to parameter redundancies. Online parameter estimation is a challenging problem that is ubiquitous in fields such as robotics, neuroscience, or finance in order to design adaptive filters and optimal controllers for unknown or changing systems
GRB 980425, SN1998bw and the EMBH model
The EMBH model, previously developed using GRB 991216 as a prototype, is here
applied to GRB 980425. We fit the luminosity observed in the 40-700 keV, 2-26
keV and 2-10 keV bands by the BeppoSAX satellite. In addition we present a
novel scenario in which the supernova SN1998bw is the outcome of an ``induced
gravitational collapse'' triggered by GRB 980425, in agreement with the
GRB-Supernova Time Sequence (GSTS) paradigm (Ruffini et al. 2001c). A further
outcome of this astrophysically exceptional sequence of events is the formation
of a young neutron star generated by the SN1998bw event. A coordinated
observational activity is recommended to further enlighten the underlying
scenario of this most unique astrophysical system.Comment: 10 pages, 3 figures, in the Proceedings of the 34th COSPAR scientific
assembly, Elsevier. Fixed some typos in this new versio
Giant edge state splitting at atomically precise zigzag edges
Zigzag edges of graphene nanostructures host localized electronic states that
are predicted to be spin-polarized. However, these edge states are highly
susceptible to edge roughness and interaction with a supporting substrate,
complicating the study of their intrinsic electronic and magnetic structure.
Here, we focus on atomically precise graphene nanoribbons whose two short
zigzag edges host exactly one localized electron each. Using the tip of a
scanning tunneling microscope, the graphene nanoribbons are transferred from
the metallic growth substrate onto insulating islands of NaCl in order to
decouple their electronic structure from the metal. The absence of charge
transfer and hybridization with the substrate is confirmed by scanning
tunneling spectroscopy (STS), which reveals a pair of occupied / unoccupied
edge states. Their large energy splitting of 1.9 eV is in accordance with ab
initio many-body perturbation theory calculations and reflects the dominant
role of electron-electron interactions in these localized states.Comment: 14 pages, 4 figure
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