Applied Deep Learning in Intracranial Neurophysiology Workshop | June 20-21, 2019

The SachsLab is preparing a short (2-day) workshop on practical deep learning (DL) applied to intracranial neurophysiology. The goal of the workshop is to help attendees gain familiarity with technologies commonly used in DL (e.g., tensorflow on GPU, jupyter notebooks), to understand DL programming paradigms (e.g., batch loading data), and to become proficient in the application of DL to intracranial neurophysiology. The workshop is intended for scientists and trainees who have a basic understanding of machine learning concepts, have basic familiarity with Python syntax, and are interested in applying deep learning to extracellular electrophysiology data. For learners who do not have an interest in these kinds of data but are interested in DL more generally, please feel free to reach out to Chad and he will be happy to direct you to some wonderful resources that are better suited to your interests.

In the workshop, attendees will learn how to run and interact with keras/tensorflow on a GPU either locally or on a remote server. They will learn how to load and process electrophysiology datasets (1 open ECoG dataset, 1 single-channel deep brain microelectrode dataset, and 3 multichannel (~192) intracortical microelectrode array datasets). After an introduction to DL, attendees will learn how to apply several DL algorithms and architectures to these types of data, and finally they will explore different ways of using deep learning to advance neuroscientific endeavours. Some of the algorithms we aim to cover include convolutional neural nets (CNN), several flavours of recurrent neural nets (RNN), autoencoders, and transformer models. For each topic, attendees will work through prepared examples using real data and thus are expected to bring their own laptop and have configured their deep learning environment (instructions will be provided in the week before the workshop).

Click here to register.

View the workshop program here.

Dr. Adam SachsComment