We will emphasize the need of open-source real-time software technology for some of these interactions. In this tutorial, we will describe several tools and techniques to build such kind of open and closed-loop interactions: from basic dynamic-clamp approaches to build hybrid circuits to more complex configurations that can include several interacting living and artificial elements. However, protocols that simultaneously combine recordings from living neurons and input/outputs from computational models are not easy to design or implement. However, computational models can also be employed to interact directly with living nervous systems, which is a powerful way of unveiling key neural dynamics by combining experimental and theoretical efforts. T3: Models in computational neuroscience are typically used to reproduce and explain experimental findings, to draw new hypotheses from their predictive power, to undertake the low observability of the brain, etc. Pablo Varona, Manuel Reyes Sanchez, Rodrigo Amaducci TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. I recommend the following sites where is explained the installation of following packages that include a set of the named libraries and some additional tools: To be able to carry out the tutorial, students need a laptop with Linux and these libraries installed: In this mini-course we will study implementations of neural networks with Keras split into two sections: On one side we will introduce the main features of Keras, showcasing some examples and in then we will do a set of two guided on-line hands-on with exercises to strengthen the knowledge.įor this tutorial, you will need basic knowledge of NumPy, SciPy, and matplotlib. Keras is a framework that greatly simplifies the design and implementations of Neural Networks of many kinds (Regular classifiers, Convolutional Neural Networks, LSTM among others). The hands-on exercises will demonstrate how Keras can be used to rapidly explore the dynamics of the network. The tutorial will show how models can be built and explored using python. The tutorial will focus on using Keras which is an open-source framework to develop Neural Networks for rapid prototyping and simulation with TensorFlow as backend. It will include an introduction to modeling and hands-on exercises. T5: This tutorial will help participants implement and explore simple neural models using Keras as well as the implementation of neural networks to apply Deep learning tools for data analysis. Slides and excersices in the Tutorial Website Time: 08:00 PM Canberra, Melbourne, Sydneyĭiscussion/Questions in this neurostars thread Topic: CNS 2020 Tutorial: Characterizing neural dynamics using highly comparative time-series analysis
Highly comparative time-series analysis: the empirical structure of time series and their methods. hctsa: A computational framework for automated time-series phenotyping using massive feature extraction.
If you want to play along at home, you can read the README and install the hctsa software package (Matlab): We will work through a range of applications using fMRI (mouse and human) and EEG (human) time-series datasets, including how to: (i) determine the relationship between structural connectivity and fMRI dynamics in mouse and human (ii) understand the effects of targeted brain stimulation using DREADDs using mouse fMRI and (iii) classify seizure dynamics and extract sleep-stage information from EEG. I will demonstrate how hctsa can be used to extract useful information from various neural time-series datasets. In this tutorial, I will introduce highly comparative time-series analysis, implemented as the software package hctsa, which partially automates the selection of useful time-series analysis methods from an interdisciplinary library of over 7000 time-series features. However, the choice of analysis methods in any given study is typically subjective, leaving open the possibility that alternative methods might yield better understanding or performance for a given task. Across this interdisciplinary literature of thousands of time-series analysis methods, each method gives unique information about the measured dynamics. There are myriad ways to quantify different types of structure in the univariate dynamics of any individual component of a neural system, including methods from statistical time-series modeling, the physical nonlinear time-series analysis literature, and methods derived from information theory. T7: Massive open datasets of neural dynamics, from microscale neuronal circuits to macroscale population-level recordings, are becoming increasingly available to the computational neuroscience community.