Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species

So-called spontaneous activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its purpose remains unclear. We propose that self-initiated firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. To demonstrate the viability of our hypothesis, we built a set of models that incorporate recent experimental results indicating homeostatic control of metabolic products—Adenosine triphosphate (ATP), adenosine diphosphate (ADP), and reactive oxygen species (ROS)—by changes in firing. We explore the relationship of metabolic cost of spiking with its effect on the temporal patterning of spikes and reproduce experimentally observed changes in intrinsic firing in the fruitfly dorsal fan-shaped body neuron in a model with ROS-modulated potassium channels. We also show that metabolic spiking homeostasis can produce indefinitely sustained avalanche dynamics in cortical circuits. Our theory can account for key features of neuronal activity observed in many studies ranging from ion channel function all the way to resting state dynamics. We finish with a set of experimental predictions that would confirm an integrated, crucial role for metabolically regulated spiking and firmly link metabolic homeostasis and neuronal function

Modeling and analysis of extracellular potentials in multielectrode arrays.

Multielectrode array (MEA) measurements of neural activity in brain slices are becoming more and more ubiquitous. To properly interpret the results gathered with their help, it is important to understand the measurement physics - the link between the electrical potential recorded at the electrodes and the underlying neural activity. In this work, we modeled the generation of transmembrane currents in a slice preparation and the extracellular potentials they generate. We addressed this forward modeling problem using Finite Element Methods (FEM) and used these results as ground truth for comparison with simplified modeling schemes in particular, forward modeling by means of the analytical point- and line-source formulas amended by means of the “method of images” (MoI) . To compare the different schemes we tested them on current sources and tissue models of increasing complexity. We calculated the extracellular potential generated at the MEA plane while varying the following: 1) We placed a point current source inside the slice, and varied its position with respect to the MEA plane. We also varied the conductivity profile of the slice to include inhomogeneity, anisotropy, and a thin layer of saline beneath the slice. 2) We used a layer 5 pyramidal cell model (Hay et al., 2011) and tracked the transmembrane currents through its compartments during a spontaneous spike. We placed these transmembrane currents as line current sources on a reconstructed cell within the slice. We then varied the conductivity of the saline bath surrounding the slice. 3) For testing a population of cells, we used a thalamo-cortical column model (Traub et al., 2005), and tracked the transmembrane currents through all its cortical neurons during an evoked response. The MEA potentials were then calculated assuming point-like current sources positioned on the mid points of the compartments in the multicompartment neuron models. We varied the conductivity of the slice to include inhomogeneity, anisotropy, and a thin layer of saline beneath the slice . In all cases we found that the MoI gave quantitatively accurate results compared to FEM. We further investigate how the changes in the extracellular potential due to the variations in the conductivity profile of the slice affect the estimates of current source density (CSD) in a MEA setup. We used MoI to extend the kernel current source density method (Potworowski et al., 2012) to include the geometry of the slice and saline slab. We found that including the saline layer does not increase the accuracy of the estimated CSD

Corrected Four-Sphere Head Model for EEG Signals.

The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations

Corrected four-sphere head model for eeg signals

The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations

NeuroML/pyNeuroML: v1.1.0

What's Changed fix(plots): provide axis for colorbar by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/252 fix(vispy-morph): get title from cell object if no network is found by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/251 feat(morph-plots): improve colouring of cells/groups in schematic plot by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/248 feat(pynml): include more info in version by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/249 Feat/chan den analysis by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/242 Feat/nsgr integration by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/243 To v1.1.0; Update to jnml v0.12.3 jar by @pgleeson in https://github.com/NeuroML/pyNeuroML/pull/254 Full Changelog: https://github.com/NeuroML/pyNeuroML/compare/v1.0.10...v1.1.