27,095 research outputs found
Homeostatic plasticity and external input shape neural network dynamics
In vitro and in vivo spiking activity clearly differ. Whereas networks in
vitro develop strong bursts separated by periods of very little spiking
activity, in vivo cortical networks show continuous activity. This is puzzling
considering that both networks presumably share similar single-neuron dynamics
and plasticity rules. We propose that the defining difference between in vitro
and in vivo dynamics is the strength of external input. In vitro, networks are
virtually isolated, whereas in vivo every brain area receives continuous input.
We analyze a model of spiking neurons in which the input strength, mediated by
spike rate homeostasis, determines the characteristics of the dynamical state.
In more detail, our analytical and numerical results on various network
topologies show consistently that under increasing input, homeostatic
plasticity generates distinct dynamic states, from bursting, to
close-to-critical, reverberating and irregular states. This implies that the
dynamic state of a neural network is not fixed but can readily adapt to the
input strengths. Indeed, our results match experimental spike recordings in
vitro and in vivo: the in vitro bursting behavior is consistent with a state
generated by very low network input (< 0.1%), whereas in vivo activity suggests
that on the order of 1% recorded spikes are input-driven, resulting in
reverberating dynamics. Importantly, this predicts that one can abolish the
ubiquitous bursts of in vitro preparations, and instead impose dynamics
comparable to in vivo activity by exposing the system to weak long-term
stimulation, thereby opening new paths to establish an in vivo-like assay in
vitro for basic as well as neurological studies.Comment: 14 pages, 8 figures, accepted at Phys. Rev.
Propagation of chaos in neural fields
We consider the problem of the limit of bio-inspired spatially extended
neuronal networks including an infinite number of neuronal types (space
locations), with space-dependent propagation delays modeling neural fields. The
propagation of chaos property is proved in this setting under mild assumptions
on the neuronal dynamics, valid for most models used in neuroscience, in a
mesoscopic limit, the neural-field limit, in which we can resolve the quite
fine structure of the neuron's activity in space and where averaging effects
occur. The mean-field equations obtained are of a new type: they take the form
of well-posed infinite-dimensional delayed integro-differential equations with
a nonlocal mean-field term and a singular spatio-temporal Brownian motion. We
also show how these intricate equations can be used in practice to uncover
mathematically the precise mesoscopic dynamics of the neural field in a
particular model where the mean-field equations exactly reduce to deterministic
nonlinear delayed integro-differential equations. These results have several
theoretical implications in neuroscience we review in the discussion.Comment: Updated to correct an erroneous suggestion of extension of the
results in Appendix B, and to clarify some measurability questions in the
proof of Theorem
Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception
Choosing an appropriate set of stimuli is essential to characterize the
response of a sensory system to a particular functional dimension, such as the
eye movement following the motion of a visual scene. Here, we describe a
framework to generate random texture movies with controlled information
content, i.e., Motion Clouds. These stimuli are defined using a generative
model that is based on controlled experimental parametrization. We show that
Motion Clouds correspond to dense mixing of localized moving gratings with
random positions. Their global envelope is similar to natural-like stimulation
with an approximate full-field translation corresponding to a retinal slip. We
describe the construction of these stimuli mathematically and propose an
open-source Python-based implementation. Examples of the use of this framework
are shown. We also propose extensions to other modalities such as color vision,
touch, and audition
Diluted neural networks with adapting and correlated synapses
We consider the dynamics of diluted neural networks with clipped and adapting
synapses. Unlike previous studies, the learning rate is kept constant as the
connectivity tends to infinity: the synapses evolve on a time scale
intermediate between the quenched and annealing limits and all orders of
synaptic correlations must be taken into account. The dynamics is solved by
mean-field theory, the order parameter for synapses being a function. We
describe the effects, in the double dynamics, due to synaptic correlations.Comment: 6 pages, 3 figures. Accepted for publication in PR
Spatial and Temporal Sensing Limits of Microtubule Polarization in Neuronal Growth Cones by Intracellular Gradients and Forces
Neuronal growth cones are the most sensitive amongst eukaryotic cells in
responding to directional chemical cues. Although a dynamic microtubule
cytoskeleton has been shown to be essential for growth cone turning, the
precise nature of coupling of the spatial cue with microtubule polarization is
less understood. Here we present a computational model of microtubule
polarization in a turning neuronal growth cone (GC). We explore the limits of
directional cues in modifying the spatial polarization of microtubules by
testing the role of microtubule dynamics, gradients of regulators and
retrograde forces along filopodia. We analyze the steady state and transition
behavior of microtubules on being presented with a directional stimulus. The
model makes novel predictions about the minimal angular spread of the chemical
signal at the growth cone and the fastest polarization times. A regulatory
reaction-diffusion network based on the cyclic
phosphorylation-dephosphorylation of a regulator predicts that the receptor
signal magnitude can generate the maximal polarization of microtubules and not
feedback loops or amplifications in the network. Using both the
phenomenological and network models we have demonstrated some of the physical
limits within which the MT polarization system works in turning neuron.Comment: 7 figures and supplementary materia
Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex
How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852
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