30,960 research outputs found
A black-box model for neurons
We explore the identification of neuronal voltage traces by artificial neural networks based on wavelets (Wavenet). More precisely, we apply a modification in the representation of dynamical systems by Wavenet which decreases the number of used functions; this approach combines localized and global scope functions (unlike Wavenet, which uses localized functions only). As a proof-of-concept, we focus on the identification of voltage traces obtained by simulation of a paradigmatic neuron model, the Morris-Lecar model. We show that, after training our artificial network with biologically plausible input currents, the network is able to identify the neuron's behaviour with high accuracy, thus obtaining a black box that can be then used for predictive goals. Interestingly, the interval of input currents used for training, ranging from stimuli for which the neuron is quiescent to stimuli that elicit spikes, shows the ability of our network to identify abrupt changes in the bifurcation diagram, from almost linear input-output relationships to highly nonlinear ones. These findings open new avenues to investigate the identification of other neuron models and to provide heuristic models for real neurons by stimulating them in closed-loop experiments, that is, using the dynamic-clamp, a well-known electrophysiology technique.Peer ReviewedPostprint (author's final draft
Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground
In Astrophysics, the identification of candidate Globular Clusters through
deep, wide-field, single band HST images, is a typical data analytics problem,
where methods based on Machine Learning have revealed a high efficiency and
reliability, demonstrating the capability to improve the traditional
approaches. Here we experimented some variants of the known Neural Gas model,
exploring both supervised and unsupervised paradigms of Machine Learning, on
the classification of Globular Clusters, extracted from the NGC1399 HST data.
Main focus of this work was to use a well-tested playground to scientifically
validate such kind of models for further extended experiments in astrophysics
and using other standard Machine Learning methods (for instance Random Forest
and Multi Layer Perceptron neural network) for a comparison of performances in
terms of purity and completeness.Comment: Proceedings of the XIX International Conference "Data Analytics and
Management in Data Intensive Domains" (DAMDID/RCDL 2017), Moscow, Russia,
October 10-13, 2017, 8 pages, 4 figure
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
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