24,072 research outputs found
Action Potential Onset Dynamics and the Response Speed of Neuronal Populations
The result of computational operations performed at the single cell level are
coded into sequences of action potentials (APs). In the cerebral cortex, due to
its columnar organization, large number of neurons are involved in any
individual processing task. It is therefore important to understand how the
properties of coding at the level of neuronal populations are determined by the
dynamics of single neuron AP generation. Here we analyze how the AP generating
mechanism determines the speed with which an ensemble of neurons can represent
transient stochastic input signals. We analyze a generalization of the
-neuron, the normal form of the dynamics of Type-I excitable membranes.
Using a novel sparse matrix representation of the Fokker-Planck equation, which
describes the ensemble dynamics, we calculate the transmission functions for
small modulations of the mean current and noise noise amplitude. In the
high-frequency limit the transmission function decays as ,
where surprisingly depends on the phase at which APs are
emitted. In a physiologically plausible regime up to 1kHz the typical response
speed is, however, independent of the high-frequency limit and is set by the
rapidness of the AP onset, as revealed by the full transmission function. In
this regime modulations of the noise amplitude can be transmitted faithfully up
to much higher frequencies than modulations in the mean input current. We
finally show that the linear response approach used is valid for a large regime
of stimulus amplitudes.Comment: Submitted to the Journal of Computational Neuroscienc
Precision and neuronal dynamics in the human posterior parietal cortex during evidence accumulation
Primate studies show slow ramping activity in posterior parietal cortex (PPC) neurons during perceptual decision-making. These findings have inspired a rich theoretical literature to account for this activity. These accounts are largely unrelated to Bayesian theories of perception and predictive coding, a related formulation of perceptual inference in the cortical hierarchy. Here, we tested a key prediction of such hierarchical inference, namely that the estimated precision (reliability) of information ascending the cortical hierarchy plays a key role in determining both the speed of decision-making and the rate of increase of PPC activity. Using dynamic causal modelling of magnetoencephalographic (MEG) evoked responses, recorded during a simple perceptual decision-making task, we recover ramping-activity from an anatomically and functionally plausible network of regions, including early visual cortex, the middle temporal area (MT) and PPC. Precision, as reflected by the gain on pyramidal cell activity, was strongly correlated with both the speed of decision making and the slope of PPC ramping activity. Our findings indicate that the dynamics of neuronal activity in the human PPC during perceptual decision-making recapitulate those observed in the macaque, and in so doing we link observations from primate electrophysiology and human choice behaviour. Moreover, the synaptic gain control modulating these dynamics is consistent with predictive coding formulations of evidence accumulation
Spike Onset Dynamics and Response Speed in Neuronal Populations
Recent studies of cortical neurons driven by fluctuating currents revealed
cutoff frequencies for action potential encoding of several hundred Hz.
Theoretical studies of biophysical neuron models have predicted a much lower
cutoff frequency of the order of average firing rate or the inverse membrane
time constant. The biophysical origin of the observed high cutoff frequencies
is thus not well understood. Here we introduce a neuron model with dynamical
action potential generation, in which the linear response can be analytically
calculated for uncorrelated synaptic noise. We find that the cutoff frequencies
increase to very large values when the time scale of action potential
initiation becomes short
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
Two-photon imaging and analysis of neural network dynamics
The glow of a starry night sky, the smell of a freshly brewed cup of coffee
or the sound of ocean waves breaking on the beach are representations of the
physical world that have been created by the dynamic interactions of thousands
of neurons in our brains. How the brain mediates perceptions, creates thoughts,
stores memories and initiates actions remains one of the most profound puzzles
in biology, if not all of science. A key to a mechanistic understanding of how
the nervous system works is the ability to analyze the dynamics of neuronal
networks in the living organism in the context of sensory stimulation and
behaviour. Dynamic brain properties have been fairly well characterized on the
microscopic level of individual neurons and on the macroscopic level of whole
brain areas largely with the help of various electrophysiological techniques.
However, our understanding of the mesoscopic level comprising local populations
of hundreds to thousands of neurons (so called 'microcircuits') remains
comparably poor. In large parts, this has been due to the technical
difficulties involved in recording from large networks of neurons with
single-cell spatial resolution and near- millisecond temporal resolution in the
brain of living animals. In recent years, two-photon microscopy has emerged as
a technique which meets many of these requirements and thus has become the
method of choice for the interrogation of local neural circuits. Here, we
review the state-of-research in the field of two-photon imaging of neuronal
populations, covering the topics of microscope technology, suitable fluorescent
indicator dyes, staining techniques, and in particular analysis techniques for
extracting relevant information from the fluorescence data. We expect that
functional analysis of neural networks using two-photon imaging will help to
decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress
in Physic
High frequency oscillations as a correlate of visual perception
“NOTICE: this is the author’s version of a work that was accepted for publication in International journal of psychophysiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International journal of psychophysiology , 79, 1, (2011) DOI 10.1016/j.ijpsycho.2010.07.004Peer reviewedPostprin
Cortical Models for Movement Control
Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)
VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output
Neuronal network models and corresponding computer simulations are invaluable
tools to aid the interpretation of the relationship between neuron properties,
connectivity and measured activity in cortical tissue. Spatiotemporal patterns
of activity propagating across the cortical surface as observed experimentally
can for example be described by neuronal network models with layered geometry
and distance-dependent connectivity. The interpretation of the resulting stream
of multi-modal and multi-dimensional simulation data calls for integrating
interactive visualization steps into existing simulation-analysis workflows.
Here, we present a set of interactive visualization concepts called views for
the visual analysis of activity data in topological network models, and a
corresponding reference implementation VIOLA (VIsualization Of Layer Activity).
The software is a lightweight, open-source, web-based and platform-independent
application combining and adapting modern interactive visualization paradigms,
such as coordinated multiple views, for massively parallel neurophysiological
data. For a use-case demonstration we consider spiking activity data of a
two-population, layered point-neuron network model subject to a spatially
confined excitation originating from an external population. With the multiple
coordinated views, an explorative and qualitative assessment of the
spatiotemporal features of neuronal activity can be performed upfront of a
detailed quantitative data analysis of specific aspects of the data.
Furthermore, ongoing efforts including the European Human Brain Project aim at
providing online user portals for integrated model development, simulation,
analysis and provenance tracking, wherein interactive visual analysis tools are
one component. Browser-compatible, web-technology based solutions are therefore
required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table
A roadmap to integrate astrocytes into Systems Neuroscience.
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease
- …