Society for Neuroscience Annual Meeting. www.sfn.org
November 16-19, 2008
Washington, D.C.
The Society for Neuroscience (SfN) annual meeting has been
the showcase for R.C. Electronics’ electrophysiology
data acquisition and analysis products for over 20 years.
We’re proud to announce our continued presence at
the SfN at this year’s annual meeting, and to tell
you about the results of the 2007 show in San Diego.
2007 SfN Annual Meeting
San Diego, CA
R .C. Electronics Inc. was pleased to present the latest
NeuroMAX status at the Society for Neuroscience show, November
3-7 in San Diego, California. We presented the following
posters. If you’d like a copy of these posters, please
contact us.
Poster: Object oriented architecture for a spike sorting
and analysis software toolbox simplifies addition of new
tools
This poster presented a software architecture for a MATLAB-based
spike sorting and analysis toolbox. This architecture was
built to address the need of the neuroscience community
for an easily modifiable and expandable set of tools for
performing operations for spike sorting and analysis. The
“toolbox” concept allows a software developer
to implement a specific sub-operation of spike sorting and
analysis, leveraging the existing tools for other parts
of the analysis and visualization, saving time and effort.
Additionally, the standardized format of the toolbox modules
facilitates sharing of new algorithms.
Poster: MATLAB-based object oriented architecture for data
handling based on NeuroShare data format
This poster presented a MATLAB-based, object oriented framework
for data handling within a spike sorting and analysis software
toolbox. The basis for the data handling classes is the
NeuroShare data format. We utilize existing NeuroShare libraries
to load data into MATLAB. We have created a set of MATLAB
data classes that are based on the NeuroShare basic data
types. The key advantage of using MATLAB’s class constructs
for managing data is that the inherent complications in
the NeuroShare data format can be hidden and data fields
can be accessed in a more controlled manner. The analysis
result is a NeuroShare compatible data that can be easily
shared or ported to another application.
