Name: Wisam Reid
Type: User
Bio: (1) PhD Candidate, Harvard Medical School (2023), SHBT
(2) MA, Stanford University (2017), Music, Science, & Technology
(3) BS, UC Berkeley (2014), EECS
Location: Harvard/MIT
Blog: http://wisamreid.com
Wisam Reid's Projects
Analysis code for the results of the spikefinder challenge
A Python-based module for creating flexible and robust spike sorting pipelines.
Python-based module with spike sorter wrappers and a simple API for running them.
Matlab code for population decoding model and neuron input population generator
Analyze dendritic activity in the web browser
Bayesian learning and inference for state space models
Spatio-temporal BP for SNNs
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Software for the paper "Fast and robust active neuron segmentation in two-photon calcium imaging using spatio-temporal deep learning," Proceedings of the National Academy of Sciences (PNAS), 2019.
Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model
cell detection in calcium imaging recordings
Tools for processing 2P recordings
Working with suite2p segmented functional images
suite2p post processing scripts
Results and reproduction code for SUNS paper
supersegger modified for cellpose/omnipose
code for Structured Variational Autoencoders
Toolkit for the generation and analysis of volume eletron microscopy based synaptic connectomes of brain tissue.
Simple and Distributed Machine Learning
A GUI to model synaptic recordings
Exploiting temporal context for 3D human pose estimation in the wild: 3D poses for the Kinetics dataset
Annotating tensor shapes using Python types
Feature and class visualization with deep neural networks in tensorflow. Contains deepdream, style transfer, receptive field visualization, convolutional filter visualization, etc.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
A single jupyter notebook multi gpu VAE-GAN example with latent space algebra and receptive field visualizations.
Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab.
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
A flexible, high-performance serving system for machine learning models
Generic U-Net Tensorflow implementation for image segmentation
A laboratory for experimenting with tensorflow abstraction