99 research outputs found
Physics of Psychophysics: Stevens and Weber-Fechner laws are transfer functions of excitable media
Sensory arrays made of coupled excitable elements can improve both their
input sensitivity and dynamic range due to collective non-linear wave
properties. This mechanism is studied in a neural network of electrically
coupled (e.g. via gap junctions) elements subject to a Poisson signal process.
The network response interpolates between a Weber-Fechner logarithmic law and a
Stevens power law depending on the relative refractory period of the cell.
Therefore, these non-linear transformations of the input level could be
performed in the sensory periphery simply due to a basic property: the transfer
function of excitable media.Comment: 4 pages, 5 figure
Noise-assisted spike propagation in myelinated neurons
We consider noise-assisted spike propagation in myelinated axons within a
multi-compartment stochastic Hodgkin-Huxley model. The noise originates from a
finite number of ion channels in each node of Ranvier. For the subthreshold
internodal electric coupling, we show that (i) intrinsic noise removes the
sharply defined threshold for spike propagation from node to node, and (ii)
there exists an optimum number of ion channels which allows for the most
efficient signal propagation and it corresponds to the actual physiological
values.Comment: 8 pages, 12 figures, accepted for publication in Phys. Rev.
Multimodal transition and stochastic antiresonance in squid giant axons
The experimental data of N. Takahashi, Y. Hanyu, T. Musha, R. Kubo, and G.
Matsumoto, Physica D \textbf{43}, 318 (1990), on the response of squid giant
axons stimulated by periodic sequence of short current pulses is interpreted
within the Hodgkin-Huxley model. The minimum of the firing rate as a function
of the stimulus amplitude in the high-frequency regime is due to the
multimodal transition. Below this singular point only odd multiples of the
driving period remain and the system is highly sensitive to noise. The
coefficient of variation has a maximum and the firing rate has a minimum as a
function of the noise intensity which is an indication of the stochastic
coherence antiresonance. The model calculations reproduce the frequency of
occurrence of the most common modes in the vicinity of the transition. A linear
relation of output frequency vs. for above the transition is also
confirmed.Comment: 5 pages, 9 figure
On Dynamics of Integrate-and-Fire Neural Networks with Conductance Based Synapses
We present a mathematical analysis of a networks with Integrate-and-Fire
neurons and adaptive conductances. Taking into account the realistic fact that
the spike time is only known within some \textit{finite} precision, we propose
a model where spikes are effective at times multiple of a characteristic time
scale , where can be \textit{arbitrary} small (in particular,
well beyond the numerical precision). We make a complete mathematical
characterization of the model-dynamics and obtain the following results. The
asymptotic dynamics is composed by finitely many stable periodic orbits, whose
number and period can be arbitrary large and can diverge in a region of the
synaptic weights space, traditionally called the "edge of chaos", a notion
mathematically well defined in the present paper. Furthermore, except at the
edge of chaos, there is a one-to-one correspondence between the membrane
potential trajectories and the raster plot. This shows that the neural code is
entirely "in the spikes" in this case. As a key tool, we introduce an order
parameter, easy to compute numerically, and closely related to a natural notion
of entropy, providing a relevant characterization of the computational
capabilities of the network. This allows us to compare the computational
capabilities of leaky and Integrate-and-Fire models and conductance based
models. The present study considers networks with constant input, and without
time-dependent plasticity, but the framework has been designed for both
extensions.Comment: 36 pages, 9 figure
Modeling Quantum Mechanical Observers via Neural-Glial Networks
We investigate the theory of observers in the quantum mechanical world by
using a novel model of the human brain which incorporates the glial network
into the Hopfield model of the neural network. Our model is based on a
microscopic construction of a quantum Hamiltonian of the synaptic junctions.
Using the Eguchi-Kawai large N reduction, we show that, when the number of
neurons and astrocytes is exponentially large, the degrees of freedom of the
dynamics of the neural and glial networks can be completely removed and,
consequently, that the retention time of the superposition of the wave
functions in the brain is as long as that of the microscopic quantum system of
pre-synaptics sites. Based on this model, the classical information entropy of
the neural-glial network is introduced. Using this quantity, we propose a
criterion for the brain to be a quantum mechanical observer.Comment: 24 pages, published versio
Computational study of resting state network dynamics
Lo scopo di questa tesi è quello di mostrare, attraverso una simulazione con il software The Virtual Brain, le più importanti proprietà della dinamica cerebrale durante il resting state, ovvero quando non si è coinvolti in nessun compito preciso e non si è sottoposti a nessuno stimolo particolare. Si comincia con lo spiegare cos’è il resting state attraverso una breve revisione storica della sua scoperta, quindi si passano in rassegna alcuni metodi sperimentali utilizzati nell’analisi dell’attività cerebrale, per poi evidenziare la differenza tra connettività strutturale e funzionale. In seguito, si riassumono brevemente i concetti dei sistemi dinamici, teoria indispensabile per capire un sistema complesso come il cervello. Nel capitolo successivo, attraverso un approccio ‘bottom-up’, si illustrano sotto il profilo biologico le principali strutture del sistema nervoso, dal neurone alla corteccia cerebrale. Tutto ciò viene spiegato anche dal punto di vista dei sistemi dinamici, illustrando il pionieristico modello di Hodgkin-Huxley e poi il concetto di dinamica di popolazione. Dopo questa prima parte preliminare si entra nel dettaglio della simulazione. Prima di tutto si danno maggiori informazioni sul software The Virtual Brain, si definisce il modello di network del resting state utilizzato nella simulazione e si descrive il ‘connettoma’ adoperato. Successivamente vengono mostrati i risultati dell’analisi svolta sui dati ricavati, dai quali si mostra come la criticità e il rumore svolgano un ruolo chiave nell'emergenza di questa attività di fondo del cervello. Questi risultati vengono poi confrontati con le più importanti e recenti ricerche in questo ambito, le quali confermano i risultati del nostro lavoro. Infine, si riportano brevemente le conseguenze che porterebbe in campo medico e clinico una piena comprensione del fenomeno del resting state e la possibilità di virtualizzare l’attività cerebrale
Low-dimensional models of single neurons: A review
The classical Hodgkin-Huxley (HH) point-neuron model of action potential
generation is four-dimensional. It consists of four ordinary differential
equations describing the dynamics of the membrane potential and three gating
variables associated to a transient sodium and a delayed-rectifier potassium
ionic currents. Conductance-based models of HH type are higher-dimensional
extensions of the classical HH model. They include a number of supplementary
state variables associated with other ionic current types, and are able to
describe additional phenomena such as sub-threshold oscillations, mixed-mode
oscillations (subthreshold oscillations interspersed with spikes), clustering
and bursting. In this manuscript we discuss biophysically plausible and
phenomenological reduced models that preserve the biophysical and/or dynamic
description of models of HH type and the ability to produce complex phenomena,
but the number of effective dimensions (state variables) is lower. We describe
several representative models. We also describe systematic and heuristic
methods of deriving reduced models from models of HH type
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