Large-scale biophysical parameter estimation in single neurons via constrained linear regression (original) (raw)
Realistic single-neuron modeling
Seminars in Neuroscience, 1992
Realistic single-neuron modeling organizes and clarifies physiological hypotheses. It extends the experimenter's intuition and leads to testable predictions. A powerful new algorithm, several user-friendly software packages and the advent of fast, cheap computers have together made this tool accessible to a broad range of neurobiologists. Equally dramatic advances in experimental findings have increased the level of sophistication of the models. Here we provide a guide to single-neuron modeling, illustrate its power with a few examples and speculate on possible future directions for this rapidly growing field. Author Keywords: computer model; compartmental model; cable model; channel kinetics
Parameter Estimation Methods for Single Neuron Models
2000
With the advancement in computer technology, it has become possible to fit complex models to neuronal data. In this work, we test how two methods can estimate parameters of simple neuron models (passive soma) to more complex ones (neuron with one dendritic cylinder and two active conductances). The first method uses classical voltage traces resulting from current pulses injection (time domain), while the second uses measures of the neuron's response to sinusoidal stimuli (frequency domain). Both methods estimate correctly the parameters in all cases studied. However, the time-domain method is slower and more prone to estimation errors in the cable parameters than the frequency-domain method. Because with noisy data the goodness of fit does not distinguish between different solutions, we suggest that running the estimation procedure a large number of times might help find a good solution and can provide information about the interactions between parameters. Also, because the formulation used for the model's response in the frequency domain is analytical, one can derive a local sensitivity analysis for each parameter. This analysis indicates how well a parameter is likely to be estimated and helps choose an optimal stimulation protocol. Finally, the tests suggest a strategy for fitting single-cell models using the two methods examined.
Estimating robustness and parameter distribution in compartmental models of neurons
Recent Advances in Stochastic Modeling and Data Analysis, 2007
Our understanding of the input-output function of single neurons has been advanced by biophysically accurate multi-compartmental models. The large number of free parameters in these models requires the use of automated approaches for finding their optimal values in a very multi dimensional parameter-plane. Due to inherent noise in measuring equipment and surroundings determination of the accuracy of the obtained parameters is near impossible. Here we show that finding the parameters distribution via Monte Carlo approach simulations reveals the sensitivity of each parameter to noise. This method allows for the reduction of the complexity of the parameter-plane which in turn enables finding the sensitivity of the model to each parameter and the sensitivity of each parameter to noise.
Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly importa...
Biological Cybernetics, 2008
Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm's function. When considering model-generated S. Druckmann (B) · I. Segev
Journal of …, 2003
Conventionally, the parameters of neuronal models are hand-tuned using trial-and-error searches to produce a desired behavior. Here, we present an alternative approach. We have generated a database of ~1.7 million single-compartment model neurons by independently varying eight maximal membrane conductances based on measurements from lobster stomatogastric neurons. We classified the spontaneous electrical activity of each model neuron and its responsiveness to inputs during runtime with an adaptive algorithm and saved a reduced version of each neuron's activity pattern. Our analysis of the distribution of different activity types (silent, spiking, bursting, irregular) in the eight-dimensional conductance space indicates that the coarse grid of conductance values we chose is sufficient to capture the salient features of the distribution. The database can be searched for different combinations of neuron properties such as activity type, spike or burst freque ncy, resting potential, frequency-current relation, and phase response curve. We demonstrate how the database can be screened for models that reproduce the behavior of a specific biological neuron and show that the contents of the database can give insight into the way a neuron's membrane conductances determine its activity pattern and response properties. Similar databases can be constructed to explore parameter spaces in multicompartmental models or small networks, or to examine the effects of changes in the voltage-dependence of currents. In all cases, database searches can provide insight into how neuronal and network properties depend on the values of the parameters in the models.
Probing the dynamics of identified neurons with a data-driven modeling approach
2008
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.
Multiple models to capture the variability in biological neurons and networks
Nature Neuroscience, 2011
How tightly tuned are the synaptic and intrinsic properties that give rise to neuron and circuit function? Experimental work shows that these properties vary considerably across identified neurons in different animals. Given this variability in experimental data, this review describes some of the complications of building computational models to aid in understanding how system dynamics arise from the interaction of system components. We argue that instead of trying to build a single model that captures the generic behavior of a neuron or circuit, it is beneficial to construct a population of models that captures the behavior of the population that provided the experimental data. Studying a population of models with different underlying structure and similar behaviors provides opportunities to discover unsuspected compensatory mechanisms that contribute to neuron and network function.
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
PLOS Computational Biology, 2015
Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrateand-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.