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There’s a new BOSS in town

BOSS is a standalone software package that facilitates offline spike sorting. It enables you to read continuous and neural-event data contained in a variety of file formats. Features include preprocessing signals, extracting action potentials from continuous data, calculating waveform features and sorting spikes according to state-of-the-art classification algorithms. Researchers can manually edit results and calculate statistics.


Neural signal data analysis has never been so simple and intuitive

The primary goal when developing BOSS was to make it intuitive and easy to understand for novice users. Even those new to spike sorting will find BOSS’ stepwise process to spike sorting gets them quickly up to speed due to its process-oriented organization and prioritization of relevant information at each process stage. The customizable user interface enables multiple researchers to share a system, each using a configuration most comfortable for her or him.


Greater speed, enhanced workflow

BOSS significantly expedites workflow because large files do not need to be split into pieces to work with. Close the program at anytime and continue right where you left off. BOSS saves the session state, enabling you to take breaks in sorting or work on another data set.

BOSS makes full use of the latest multi-core processors and performs many features in the background. This means you can move swiftly from loading an extremely large file to extracting spikes and sorting them without delay. BOSS easily loads any file size with no impact on front-end performance, an unparalleled capability.


BOSS key features & benefits

  • Intuitive and user friendly

  • Wizard-assisted sorting and extraction

  • Fully customizable user interface

  • Multi-platform (OS X, Windows, Linux)

  • Automatically saves file editing sessions for easy data analysis continuation after interruptions

  • Background thread for detection and sorting keeps user interface performance uncompromised

  • Background organization of data increases processing speed

  • Large file support

  • Manual and automated sorting algorithms including T-Distribution EM, K-Means and Manual Editing

  • Automatic estimation of most relevant feature spaces for faster and more accurate sorting

  • Allows saving and loading detection and sorting parameters to easily set up the environment for similar experiments across files


Minimum system requirements

OS Mac OS10, Windows® 7
CPU ≥ 4 physical cores at 2GHz
RAM 4 GB
Hard drive 1TB 3Gbit/s SATA II HDD
Display Video card with full OpenGL compatibility

Instruction Manual

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