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

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

Ascética y disciplina en la espiritualidad ignaciana

Hablar sobre ascética y disciplina desde el escenario de una civilización del consumo y del bienestar que sospecha de lo que pueda tener sabor a mortificación y penitencia, y bajo la presión de ideologías neoliberales que nos acosan por doquier resulta por lo menos extraño. Algo así, aunque a la inversa, como les resultaba chocante a los fariseos y letrados en casa de Leví la conducta de los seguidores de Jesús: "Los discípulos de Juan tienen sus ayunos frecuentes y sus rezos, y los de los fariseos también; en cambio los tuyos, a comer y a beber" (Le. 5,33)

Likelihood-free inference with emulator networks

Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data -- both local emulators which approximate the likelihood for specific observed data, as well as global ones which are applicable to a range of data. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on user-defined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on high-dimensional problems which are challenging for conventional ABC approaches

Inferring the parameters of neural simulations from high-dimensional observations

Many models in neuroscience, such as networks of spiking neurons or complex biophysical models, are defined as numerical simulators. This means one can simulate data from the model, but calculating the likelihoods associated with specific observations is hard or intractable, which in turn makes statistical inference challenging. So-called Approximate Bayesian Computation (ABC) aims to make Bayesian inference possible for likelihood-free models. However, standard ABC algorithms do not scale to high-dimensional observations, e.g. inference of receptive fields from high-dimensional stimuli. Here, we develop an approach to likelihood-free inference for high-dimensional data, where we train a neural network to perform statistical inference given adaptively simulated data sets. The network is composed of layers performing non-linear feature extraction, and fully connected layers for non-linear density estimation. Feature extraction layers are either convolutional or recurrent in structure, depending on whether the data is high-dimensional 138 COSYNE 2019 II-40 in space or time, respectively. This approach makes it possible to scale ABC to problems with high-dimensional inputs. We illustrate this method in two canonical examples in neuroscience. First, we infer receptive field parameters of a V1 simple cell model from neural activity resulting from white-noise stimulation, a high-dimensional stimulus in the space domain. Second, we perform Bayesian inference on a Hodgkin-Huxley model of a single neuron, given full voltage traces resulting from intracellular current stimulation. On both applications, we retrieve the posterior distribution over the parameters, i.e. the manifold of parameters for which the model exhibits the same behaviour as the observations. Our approach will allow neuroscientists to leverage the power of deep neural networks to link high-dimensional data to complex simulations of neural dynamics

Flexible Bayesian inference for mechanistic models of neural dynamics

One of the central goals of computational neuroscience is to understand the dynamics of single neurons and neural ensembles. However, linking mechanistic models of neural dynamics to empirical observations of neural activity has been challenging. Statistical inference is only possible for a few models of neural dynamics (e.g. GLMs), and no generally applicable, effective statistical inference algorithms are available: As a consequence, comparisons between models and data are either qualitative or rely on manual parameter tweaking, parameterfitting using heuristics or brute-force search. Furthermore, parameter-fitting approaches typically return a single best-fitting estimate, but do not characterize the entire space of models that would be consistent with data. We overcome this limitation by presenting a general method for Bayesian inference on mechanistic models of neural dynamics. Our approach can be applied in a ‘black box’ manner to a wide range of neural models without requiring model-specific modifications. In particular, it extends to models without explicit likelihoods (e.g. most spiking networks). We achieve this goal by building on recent advances in likelihood-free Bayesian inference (Papamakarios and Murray 2016, Moreno et al. 2016): the key idea is to simulate multiple data-sets from different parameters, and then to train a probabilistic neural network which approximates the mapping from data to posterior distribution. We illustrate this approach using Hodgkin-Huxley models of single neurons and models of spiking networks: On simulated data, estimated posterior distributions recover ground-truth parameters, and reveal the manifold of parameters for which the model exhibits the same behaviour. On in-vitro recordings of membrane voltages, we recover multivariate posteriors over biophysical parameters, and voltage traces accurately match empirical data. Our approach will enable neuroscientists to perform Bayesian inference on complex neural dynamics models without having to design model-specific algorithms, closing the gap between biophysical and statistical approaches to neural dynamics