The neural decoding toolbox

Population decoding is a powerful way to analyze neural data, however, currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have created the Neural Decoding Toolbox (NDT) which is a Matlab package that makes it easy to apply population decoding analyses to neural activity. The design of the toolbox revolves around four abstract object classes which enables users to interchange particular modules in order to try different analyses while keeping the rest of the processing stream intact. The toolbox is capable of analyzing data from many different types of recording modalities, and we give examples of how it can be used to decode basic visual information from neural spiking activity and how it can be used to examine how invariant the activity of a neural population is to stimulus transformations. Overall this toolbox will make it much easier for neuroscientists to apply population decoding analyses to their data, which should help increase the pace of discovery in neuroscience.

[1]  Stefan Pollmann,et al.  PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data , 2009, Neuroinformatics.

[2]  Thomas Serre,et al.  Object decoding with attention in inferior temporal cortex , 2011, Proceedings of the National Academy of Sciences.

[3]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[4]  N. Kriegeskorte,et al.  Revealing representational content with pattern-information fMRI--an introductory guide. , 2009, Social cognitive and affective neuroscience.

[5]  Dawn M. Taylor,et al.  Extraction algorithms for cortical control of arm prosthetics , 2001, Current Opinion in Neurobiology.

[6]  Barry J. Richmond,et al.  Information flow and temporal coding in primate pattern vision , 1995, Journal of Computational Neuroscience.

[7]  Michael J. Berry,et al.  Synergy, Redundancy, and Independence in Population Codes , 2003, The Journal of Neuroscience.

[8]  B L McNaughton,et al.  Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. , 1998, Journal of neurophysiology.

[9]  Christos Constantinidis,et al.  Incorporation of new information into prefrontal cortical activity after learning working memory tasks , 2012, Proceedings of the National Academy of Sciences.

[10]  Parag A. Pathak,et al.  Massachusetts Institute of Technology , 1964, Nature.

[11]  William Bialek,et al.  Reading a Neural Code , 1991, NIPS.

[12]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.

[13]  R. Kass,et al.  Multiple neural spike train data analysis: state-of-the-art and future challenges , 2004, Nature Neuroscience.

[14]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[15]  Alice J. O'Toole,et al.  Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data , 2007, Journal of Cognitive Neuroscience.

[16]  Ethan M. Meyers,et al.  Examining high level neural representations of cluttered scenes , 2010 .

[17]  D. Perrett,et al.  The `Ideal Homunculus': decoding neural population signals , 1998, Trends in Neurosciences.

[18]  Tomaso Poggio,et al.  Preliminary MEG decoding results , 2012 .

[19]  T. Carlson,et al.  High temporal resolution decoding of object position and category. , 2011, Journal of vision.

[20]  David J. Freedman,et al.  Dynamic population coding of category information in inferior temporal and prefrontal cortex. , 2008, Journal of neurophysiology.

[21]  Terence D Sanger,et al.  Neural population codes , 2003, Current Opinion in Neurobiology.

[22]  Leonardo Franco,et al.  The use of decoding to analyze the contribution to the information of the correlations between the firing of simultaneously recorded neurons , 2004, Experimental Brain Research.

[23]  Gabriel Kreiman,et al.  Tutorial on Pattern Classification in Cell Recording , 2011 .

[24]  P. Latham,et al.  Synergy, Redundancy, and Independence in Population Codes, Revisited , 2005, The Journal of Neuroscience.

[25]  J. Haynes.  Brain Reading: Decoding Mental States From Brain Activity In Humans , 2011 .

[26]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[27]  T. Poggio,et al.  The Mathematics of Learning: Dealing with Data , 2005, 2005 International Conference on Neural Networks and Brain.

[28]  R. Quiroga,et al.  Extracting information from neuronal populations : information theory and decoding approaches , 2022 .

[29]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[30]  Michael S. Pratte,et al.  Decoding patterns of human brain activity. , 2012, Annual review of psychology.

[31]  Bruno B Averbeck,et al.  Rapid Sequences of Population Activity Patterns Dynamically Encode Task-Critical Spatial Information in Parietal Cortex , 2010, The Journal of Neuroscience.

[32]  John P. Donoghue,et al.  Connecting cortex to machines: recent advances in brain interfaces , 2002, Nature Neuroscience.

[33]  Britt Anderson,et al.  Joint decoding of visual stimuli by IT neurons’ spike counts is not improved by simultaneous recording , 2006, Experimental Brain Research.

[34]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[35]  Kenneth D. Harris,et al.  Data Sharing for Computational Neuroscience , 2008, Neuroinformatics.