Causal pattern recovery from neural spike train data using the Snap Shot Score.
We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.
Echtermeyer
Christoph
C
School of Biology, University of St Andrews, St Andrews, KY16 9TS, UK.
Smulders
Tom V
TV
Institute of Neuroscience, The Henry Wellcome Building for Neuroecology, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK.
Smith
V Anne
VA
School of Biology, University of St Andrews, St Andrews, KY16 9TS, UK. vas1@st-andrews.ac.uk.
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D007255:Information Services; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated; D013997:Time Factors