Flatiron Software

Project Image for CaImAn Python
CaImAn Python

Recent advances in calcium imaging acquisition techniques are creating datasets of the order of Terabytes/week. Memory and computationally efficient algorithms are required to analyze in reasonable amount of time terabytes of data. This project implements a set of essential methods required in the calcium imaging movies analysis pipeline. Fast and scalable algorithms are implemented for motion correction, movie manipulation, and source and spike extraction. CaImAn also contains some routines for the analyisis of behavior from video cameras. In summary, CaImAn provides a general purpose tool to handle large movies, with special emphasis on tools for two-photon and one-photon calcium imaging and behavioral datasets.

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Project Image for NeMoS

A statistical modeling framework for systems neuroscience. NeMos specializes in GPU-accelerated optimizations. Its current core functionality includes the implementation of the Generalized Linear Model (GLM) for spike train and calcium imaging analysis.

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Project Image for plenoptic

`plenoptic` is a python library for model-based stimulus synthesis. It provides tools to help researchers understand their model by synthesizing novel informative stimuli, which help build intuition for what features the model ignores and what it is sensitive to. These synthetic images can then be used in future perceptual or neural experiments for further investigation.

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Project Image for PYthon Neural Analysis Package (Pynapple)
PYthon Neural Analysis Package (Pynapple)

Pynapple is a light-weight python library for neurophysiological data analysis. The goal is to offer a versatile set of tools to study typical data in the field, i.e. time series (spike times, behavioral events, etc.) and time intervals (trials, brain states, etc.). It also provides users with generic functions for neuroscience such as tuning curves and cross-correlograms.

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Project Image for RealNeuralNetworks.jl

Due to the string-like nature of neurons and blood vessels, they could be abstracted as curved tubes with center lines and radii. This representation could be used for morphological analysis, such as path length and branching angle. Given an accurate voxel segmentation, the computation of object centerlines and radii is called skeletonization. RealNeuralNetworks.jl is developed to do that. Unlike most related packages, it combines the synaptic connectivity graph with morphological features and could be used to explore the relationship between synaptic connectivity and morphology. Recently, a new arising programing language, called Julia, is getting popular in data science. RealNeuralNetworks.jl is a Julia package and the algorithms are written from scratch for less dependency and efficiency.

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