1,304 research outputs found
Probing the dynamics of identified neurons with a data-driven modeling approach
In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach
Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.
We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight
Fitting Neuron Models to Spike Trains
Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model
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
Trial matching: capturing variability with data-constrained spiking neural networks
Simultaneous behavioral and electrophysiological recordings call for new
methods to reveal the interactions between neural activity and behavior. A
milestone would be an interpretable model of the co-variability of spiking
activity and behavior across trials. Here, we model a cortical sensory-motor
pathway in a tactile detection task with a large recurrent spiking neural
network (RSNN), fitted to the recordings via gradient-based optimization. We
focus specifically on the difficulty to match the trial-to-trial variability in
the data. Our solution relies on optimal transport to define a distance between
the distributions of generated and recorded trials. The technique is applied to
artificial data and neural recordings covering six cortical areas. We find that
the resulting RSNN can generate realistic cortical activity and predict jaw
movements across the main modes of trial-to-trial variability. Our analysis
also identifies an unexpected mode of variability in the data corresponding to
task-irrelevant movements of the mouse.Comment: 11 pages of main text, 4 figures in main, 5 pages of appendix, 4
figures in appendi
Predicting single spikes and spike patterns with the Hindmarsh-Rose model
Most simple neuron models are only able to model traditional spiking behavior. As physiologists discover and classify different electrical phenotypes, computational neuroscientists become interested in using simple phenomenological models that can exhibit these different types of spiking patterns. The Hindmarsh-Rose model is a three-dimensional relaxation oscillator which can show both spiking and bursting patterns and has a chaotic regime. We test the predictive powers of the Hindmarsh-Rose model on two different test databases. We show that the Hindmarsh-Rose model can predict the spiking response of rat layer 5 neocortical pyramidal neurons on a stochastic input signal with a precision comparable to the best known spiking models. We also show that the Hindmarsh-Rose model can capture qualitatively the electrical footprints in a database of different types of neocortical interneurons. When the model parameters are fit from sub-threshold measurements only, the model still captures well the electrical phenotype, which suggests that the sub-threshold signals contain information about the firing patterns of the different neuron
Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell
Biologically relevant large-scale computational models currently represent one of the
main methods in neuroscience for studying information processing primitives of brain
areas. However, biologically realistic neuron models tend to be computationally heavy
and thus prevent these models from being part of brain-area models including thousands
or even millions of neurons. The cerebellar input layer represents a canonical example
of large scale networks. In particular, the cerebellar granule cells, the most numerous
cells in the whole mammalian brain, have been proposed as playing a pivotal role in
the creation of somato-sensorial information representations. Enhanced burst frequency
(spiking resonance) in the granule cells has been proposed as facilitating the input signal
transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell
feature in the operation of the granular layer remains largely unclear. This study aims to
develop a methodological pipeline for creating neuron models that maintain biological
realism and computational efficiency whilst capturing essential aspects of single-neuron
processing. Therefore, we selected a light computational neuron model template (the
adaptive-exponential integrate-and-fire model), whose parameters were progressively
refined using an automatic parameter tuning with evolutionary algorithms (EAs). The
resulting point-neuron models are suitable for reproducing the main firing properties
of a realistic granule cell from electrophysiological measurements, including the spiking
resonance at the theta-frequency band, repetitive firing according to a specified intensityfrequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly,
the proposed model also reproduced some other emergent properties (namely, silent at
rest, rheobase and negligible adaptation under depolarizing currents) even though these
properties were not set in the EA as a target in the fitness function (FF), proving that
these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are
appropriate for future research of the functional implication of bursting resonance at
the theta band in large-scale granular layer network models.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento under the EmbBrain project
A-TIC-276-UGR18University of Granada under the Young Researchers FellowshipMinisterio de Economia y Competitividad (MINECO)-FEDER
TIN2016-81041-REuropean Human Brain Project SGA2 ( H2020-RIA)
785907European Human Brain Project SGA3 (European Commission) ( H2020-RIA)
945539CEREBIO
P18-FR-237
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