The use of automated parameter searches to improve ion channel kinetics for neural modeling

Hendrickson, Eric B.; Edgerton, Jeremy R.; Jaeger, Dieter

doi:10.1007/s10827-010-0312-x

Cited by 18 publications

(21 citation statements)

References 41 publications

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“…The leech heartbeat system has made the specification of input and output, while difficult, at least feasible, but determining motor neuron intrinsic properties in voltage clamp will be another matter. Every system is likely to come up against such walls; perhaps optimization and/or database techniques (Prinz, 2010) using models can help surmount such walls (Hendrickson et al, 2011). …”

Section: Discussionmentioning

confidence: 99%

Patterns of Presynaptic Activity and Synaptic Strength Interact to Produce Motor Output

Wright¹,

Calabrese²

2011

J. Neurosci.

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Motor neuron activity is coordinated by premotor networks into a functional motor pattern by complex patterns of synaptic drive. These patterns combine both the temporal pattern of spikes of the premotor network and the profiles of synaptic strengths (i.e., conductances). Given the complexity of premotor networks in vertebrates, it has been difficult to ascertain the relative contributions of temporal patterns and synaptic strength profiles to the motor patterns observed in these animals. Here, we use the leech (Hirudo sp.) heartbeat central pattern generator (CPG), in which we can measure both the temporal pattern and the synaptic strength profiles of the entire premotor network and the motor outflow in individual animals. In this system, a series of motor neurons all receive input from the same premotor interneurons of the CPG but must be coordinated differentially to produce a functional pattern. These properties allow a theoretical and experimental dissection of the rules that govern how temporal patterns and synaptic strength profiles are combined in motor neurons so that functional motor patterns emerge, including an analysis of the impact of animal-to-animal variation in input to such variation in output. In the leech, segmental heart motor neurons are coordinated alternately in a synchronous and peristaltic pattern. We show that synchronous motor patterns result from a nearly synchronous premotor temporal pattern produced by the leech heartbeat CPG. For peristaltic motor patterns, the staggered premotor temporal pattern determines the phase range over which segmental motor neurons can fire while synaptic strength profiles define the intersegmental motor phase progression realized.

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Section: Discussionmentioning

confidence: 99%

Patterns of Presynaptic Activity and Synaptic Strength Interact to Produce Motor Output

Wright¹,

Calabrese²

2011

J. Neurosci.

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“…In most cases, the relationship between the values of the parameters and the output of the model is nonlinear (for an interesting exception, see Huys et al, 2006) and often rather complex. Accordingly, the task of finding the optimal parameter values is highly non-trivial, and has been the subject of extensive research (Vanier and Bower, 1999; Keren et al, 2005; Huys et al, 2006; Druckmann et al, 2007, 2008; Gurkiewicz and Korngreen, 2007; Van Geit et al, 2007, 2008; Huys and Paninski, 2009; Rossant et al, 2010, 2011; Eichner and Borst, 2011; Hendrickson et al, 2011; Bahl et al, 2012; Svensson et al, 2012; Vavoulis et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

A flexible, interactive software tool for fitting the parameters of neuronal models

Friedrich

Vella

Gulyás

et al. 2014

Front. Neuroinform.

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The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

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“…Svensson et al (2012) fit a nine-parameter model of a filter-based visual neuron to experimental data using both gradient following (GF) methods and EAs. Some groups have used optimization techniques to tune ion channels kinetics for compartmental neurons (Hendrickson et al, 2011; Ben-Shalom et al, 2012) while other groups have used quantum optimization techniques and EAs to tune more abstract networks of neurons (Schliebs et al, 2009, 2010). Additionally, brute force methods of searching the parameter space were used to examine a three-neuron model of a lobster stomatogastric circuit by creating large databases of compartmental neurons with varying membrane conductance values and testing the resulting functional behavior of this circuit (Prinz et al, 2003, 2004).…”

Section: Introductionmentioning

confidence: 99%

An efficient automated parameter tuning framework for spiking neural networks

Carlson

Nageswaran²,

Dutt

et al. 2014

Front. Neurosci.

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As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

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The use of automated parameter searches to improve ion channel kinetics for neural modeling

Cited by 18 publications

References 41 publications

Patterns of Presynaptic Activity and Synaptic Strength Interact to Produce Motor Output

Patterns of Presynaptic Activity and Synaptic Strength Interact to Produce Motor Output

A flexible, interactive software tool for fitting the parameters of neuronal models

An efficient automated parameter tuning framework for spiking neural networks

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