A stepwise neuron model fitting procedure designed for recordings with high spatial resolution : Application to layer 5 pyramidal cells

© 2017 The Author(s). Published by Elsevier B. V. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Background Recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. New method In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. Result We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. Comparison with existing methods Our approach combines features from several previously applied model-fitting strategies. The reduced-morphology neuron model obtained using our approach reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model. Conclusions The network models produced using our method are cost-efficient and predict that interconnected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies, largely due to the medium after hyperpolarization mediated by the Ca2+-activated SK current.Peer reviewedFinal Published versio

Layer 3 pyramidal neurons of rhesus monkeys in aging and after ischemic injury

Layer 3 (L3) pyramidal neurons are involved in intrinsic and extrinsic corticocortical communications that are integral to area specific cortical functions. The functional and morphological properties of these neurons are altered in the lateral prefrontal cortex (LPFC) of aged rhesus monkeys, changes which parallel the decline of working memory (WM) function. What is not yet understood is the time course of these neuronal alternations during the aging process, or the impact of neuronal changes on the function of local networks that underlie WM. By comparing the properties of L3 pyramidal neurons from the LPFC of behaviorally characterized rhesus monkeys over the adult lifespan using whole cell patch clamp recordings and neuronal reconstructions, the present dissertation demonstrates that WM impairment, neuronal hyperexcitabilty and spine loss begin in middle age. We use bump attractor models to predict how empirically observed changes affect performance on the Delayed Response Task and Delayed Recognition Span Task (spatial). The performance of both models is affected much more by neuronal hyperexcitability than by synapse loss. In a separate study, we examine pathological changes of L3 pyramidal neurons in the perilesional ventral premotor cortex following acute ischemic injury to the primary motor cortex. Neurons from lesioned monkeys exhibit hyperexcitability and changes the excitatory:inhibitory synaptic balance in favor of inhibition. As oxidative stress and inflammation are known to exacerbate both age-related and injury-induced neuronal pathology, we characterize neuronal properties in both conditions after administering therapeutic interventions which target inflammatory pathways and which have previously been shown to ameliorate behavioral deficits. Chronic dietary curcumin treatment dampens neuronal hyperexcitability in middle-aged subjects, but the neuronal changes are not correlated with WM improvements. Treatment with mesenchymal-derived extracellular vesicles lowers firing rates and restores excitatory:inhibitory synaptic balance, and importantly, these changes correlate significantly with motor function

Biophysical Characterization and Simulation of Neocortical Layer 2/3 Pyramidal Neurons during Postnatal Development

Pyramidal neurons in layer 2/3 of the mammalian neocortex constitute the most abundant neocortical cell type, yet their biophysical properties are still poorly understood. In this thesis, fundamental properties of layer 2/3 pyramidal neurons of 1-to-6-weeks old rats were investigated with an approach combining in vitro electrophysiological characterization, reconstruction of cell morphologies, and numerical computer simulations. A specific goal was to identify ion channel mechanisms underlying the sub-threshold integrative properties of these cells and to reveal the developmental profile of channel expression. A simulated annealing algorithm was employed to numerically simulate layer 2/3 neurons and to generate valid models of varying complexity and constrained by experimental data. At all ages, layer 2/3 pyramidal neurons showed prominent anomalous rectification which could be attributed to inward-rectifier potassium (KIR) channels based both on pharmacological experiments and modeling. In contrast to other types of pyramidal neurons little hyperpolarization-activated current (Ih) was found. While morphological development essentially was complete at postnatal week 2, biophysical properties continued to change until week 4-6. In particular, input resistance strongly decreased with age, rendering the cells less excitable as the cortical network matures. Computer simulations showed that these properties will have a large impact on the integration of synaptic inputs during ongoing spontaneous activity in vivo. It is concluded, that layer 2/3 pyramidal neurons possess biophysical properties distinct from other pyramidal cells and that the prolonged postnatal development is critical for shaping synaptic integration and neocortical circuit activity in vivo

NetPyNE, a tool for data-driven multiscale modeling of brain circuits

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena

NetPyNE, a tool for data-driven multiscale modeling of brain circuits.

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201