PRANAS: A New Platform for Retinal Analysis and Simulation

Cessac, Bruno; Kornprobst, Pierre; Kraria, Selim; Nasser, Hassan; Pamplona, Daniela; Portelli, Geoffrey; Viéville, Thierry

doi:10.3389/fninf.2017.00049

Cited by 23 publications

(31 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stimfit, however, does not perform data acquisition. Other open-source packages like Spyke Viewer (Python; Pröpper and Obermayer, 2013 ), Elephant (Python; http://neuralensemble.org/elephant/ ) and sigTOOL (MATLAB; Lidierth, 2009 ) focus on providing tools for spike train analysis similar to those provided on NeuroMatic's Spike tab, while other packages like Chronux (MATLAB; Bokil et al, 2010 ), nSTAT (MATLAB; Cajigas et al, 2012 ), Tridesclous (Python; https://github.com/tridesclous/tridesclous ) and PRANAS (C++; Cessac et al, 2017 ) focus on providing more specialized tools for signal analysis, spike sorting and multi-electrode-array data. These latter spike analysis tools provide functionality that is currently outside the scope of NeuroMatic.…”

Section: Discussionmentioning

confidence: 99%

NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

Rothman

Silver

2018

Front. Neuroinform.

220

165

View full text Add to dashboard Cite

Acquisition, analysis and simulation of electrophysiological properties of the nervous system require multiple software packages. This makes it difficult to conserve experimental metadata and track the analysis performed. It also complicates certain experimental approaches such as online analysis. To address this, we developed NeuroMatic, an open-source software toolkit that performs data acquisition (episodic, continuous and triggered recordings), data analysis (spike rasters, spontaneous event detection, curve fitting, stationarity) and simulations (stochastic synaptic transmission, synaptic short-term plasticity, integrate-and-fire and Hodgkin-Huxley-like single-compartment models). The merging of a wide range of tools into a single package facilitates a more integrated style of research, from the development of online analysis functions during data acquisition, to the simulation of synaptic conductance trains during dynamic-clamp experiments. Moreover, NeuroMatic has the advantage of working within Igor Pro, a platform-independent environment that includes an extensive library of built-in functions, a history window for reviewing the user's workflow and the ability to produce publication-quality graphics. Since its original release, NeuroMatic has been used in a wide range of scientific studies and its user base has grown considerably. NeuroMatic version 3.0 can be found at http://www.neuromatic.thinkrandom.com and https://github.com/SilverLabUCL/NeuroMatic.

show abstract

Section: Discussionmentioning

confidence: 99%

NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

Rothman

Silver

2018

Front. Neuroinform.

220

165

View full text Add to dashboard Cite

show abstract

“…Additionally, this approach can be used in any branch of biology that wants to “confirm” the parameters or interactions of a candidate Hamiltonian from data. Unfortunately, inverse problems are usually hard to solve; in particular, the application of the MEP to biological data usually relies on sophisticated numerical algorithms and computational power [ 4 , 16 , 17 , 32 ].…”

Section: Maximum Entropy Principle: Preliminaries and Fundamentalsmentioning

confidence: 99%

A Comparison of the Maximum Entropy Principle Across Biological Spatial Scales

Cofré

Herzog

Corcoran

et al. 2019

Entropy

View full text Add to dashboard Cite

Despite their differences, biological systems at different spatial scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are often hard to grasp due to the highly specialized nature of modern science and the parcelled terminology employed by various scientific sub-disciplines. To explore these common organizational features, this paper provides a comparative study of diverse applications of the maximum entropy principle, which has found many uses at different biological spatial scales ranging from amino acids up to societies. By presenting these studies under a common approach and language, this paper aims to establish a unified view over these seemingly highly heterogeneous scenarios.

show abstract

“…Cessac et al. extended the range of retina simulation software with a new platform that integrates the retina simulator and a toolbox for the analysis of spike train population statistics [27]. Huth et al published Convis, a Python simulation toolbox for large-scale neural populations, which offers luminance gain control, contrast gain control and arbitrary receptive fields by 3D convolutions executed on a graphics card [28].…”

Section: Related Workmentioning

confidence: 99%

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

Crespo-Cano

Cuenca-Asensi

Fernández

et al. 2019

Sensors

View full text Add to dashboard Cite

A significant challenge in neuroscience is understanding how visual information is encoded in the retina. Such knowledge is extremely important for the purpose of designing bioinspired sensors and artificial retinal systems that will, in so far as may be possible, be capable of mimicking vertebrate retinal behaviour. In this study, we report the tuning of a reliable computational bioinspired retinal model with various algorithms to improve the mimicry of the model. Its main contribution is two-fold. First, given the multi-objective nature of the problem, an automatic multi-objective optimisation strategy is proposed through the use of four biological-based metrics, which are used to adjust the retinal model for accurate prediction of retinal ganglion cell responses. Second, a subset of population-based search heuristics—genetic algorithms (SPEA2, NSGA-II and NSGA-III), particle swarm optimisation (PSO) and differential evolution (DE)—are explored to identify the best algorithm for fine-tuning the retinal model, by comparing performance across a hypervolume metric. Nonparametric statistical tests are used to perform a rigorous comparison between all the metaheuristics. The best results were achieved with the PSO algorithm on the basis of the largest hypervolume that was achieved, well-distributed elements and high numbers on the Pareto front.

show abstract

PRANAS: A New Platform for Retinal Analysis and Simulation

Cited by 23 publications

References 65 publications

NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

A Comparison of the Maximum Entropy Principle Across Biological Spatial Scales

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

Contact Info

Product

Resources

About