The BigPint package can help examine any large multivariate dataset. However, we note that the example datasets and example code in this package consider RNA-sequencing datasets. If you are using this software for RNA-sequencing data, then it can help you confirm that the variability between your treatment groups is larger than that between your replicates and determine how various normalization techniques in popular RNA-sequencing analysis packages (such as edgeR, DESeq2, and limma) affect your dataset.
When performing canine operant conditioning studies, the delivery of the reward can be a limiting factor of the study. While there are a few commercially available options for automatically delivering rewards, they generally require manual input, such as using a remote control, in accordance with the experiment script.
ERPLAB Toolbox is a free, open-source Matlab package for analyzing ERP data. It is tightly integrated with EEGLAB Toolbox, extending EEGLAB’s capabilities to provide robust, industrial-strength tools for ERP processing, visualization, and analysis.
The PocketPCR is a so called thermocycler used to activate biological reactions. To do so the device raises and lowers the temperature of the liquid in the small tubes. The polymerase chain reaction (PCR) is a method widely used in molecular biology to make copies of a specific DNA segment.
The TGAC Browser is a Genomic Browser with novel rendering and annotation capabilities designed to overcome some shortcomings in available approaches. It was developed to visualize genome annotations from Ensembl Database Schema.
Easy whole-brain modeling for computational neuroscientists 👩🏿🔬💻🧠
In its essence, neurolib is a computational framework for simulating coupled neural mass models written in Python. It helps you to easily load structural brain scan data to construct brain networks where each node is a neural mass representing a single brain area.
With YAPiC you can make your own customized filter (also called model or classifier) to enhance a certain structure of your choice with a simple Python based command line interface, installable with pip.
This is the 4th edition of the online, freely available textbook, providing a complete, self-contained introduction to the field of Computational Cognitive Neuroscience, where computer models of the brain are used to understand a wide range of cognitive functions, including perception, attention, motor control, learning, memory, language, and executive function.
Neural network simulation software written in Go and Python, for developing biologically-based but also computationally functional neural models. Features an interactive 3D interface for visualizing networks and data, and has many implemented models of a wide range of cognitive phenomena.
Uncertainpy is a python toolbox for uncertainty quantification and sensitivity analysis tailored towards computational neuroscience.
Uncertainpy is model independent and treats the model as a black box where the model can be left unchanged.