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

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

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

Collection of simulated data from a thalamocortical network model

A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. In some sciences, this could be a mathematical proof, yet biological systems usually do not satisfy assumptions of mathematical theorems. One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. Moreover, these data can be used to study the effects of different tissue models on the measurement. The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al., Journal of Neurophysiology, 93(4), 2194-2232 (Traub et al. 2005)), with activity evoked by different thalamic stimuli

Collection of simulated data from a thalamocortical network model

A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. In some sciences, this could be a mathematical proof, yet biological systems usually do not satisfy assumptions of mathematical theorems. One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. Moreover, these data can be used to study the effects of different tissue models on the measurement. The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al., Journal of Neurophysiology, 93(4), 2194-2232 (Traub et al. 2005)), with activity evoked by different thalamic stimuli

Amortised inference for mechanistic models of neural dynamics

Bayesian statistical inference provides a principled framework for linking mechanistic models of neural dynamics with empirical measurements. However, for many models of interest, in particular those relying on numerical simulations, statistical inference is difficult and requires bespoke and expensive inference algorithms. Furthermore, even within the same model class, each new measurement requires a full new inference – one can not leverage knowledge from past inferences to facilitate new ones. This limits the use of Bayesian inference in time-critical, large-scale, or fully-automated applications. We overcome these limitations by presenting a method for statistical inference on simulation-based models which can be applied in a ’black box’ manner to a wide range of models in neuroscience. The key idea is to generate a large number of simulations from the model of interest and use them to train a neural network to perform statistical inference. Once the network is trained, performing inference given any observed data is very fast, requiring only a single-forward pass through the network, i.e. inference is amortised. We explain how our approach can be used to perform parameter-estimation, and illustrate it in the context of ion channel models. We train a network on a large diversity of simulated current responses to voltage-clamp protocols. After training, the network is able to instantaneously provide the posterior distribution over the channel model parameters given current responses from a publicly available database of ion channel models. The approach will enable neuroscientists to perform scalable Bayesian inference on large-scale data sets and complex models without having to design model-specific algorithms, closing the gap between mechanistic and statistical approaches to neural dynamics

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.

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

NeuroML/pyNeuroML: v1.1.2

What's Changed Reduce memory usage when generating morph plots and a few minor fixes by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/258 To 1.1.2 by @sanjayankur31 in https://github.com/NeuroML/pyNeuroML/pull/259 Full Changelog: https://github.com/NeuroML/pyNeuroML/compare/v1.1.1...v1.1.