Neural activity in the mammalian CNS is determined by both observable processes, such as sensory stimuli or motor output, and covert, internal cognitive processes that cannot be directly observed. We propose methods to identify these cognitive processes by examining the covert structure within the apparent 'noise' in spike trains. Contemporary analyses of neural codes include encoding (tuning curves derived from spike trains and behavioral, sensory or motor variables), decoding (reconstructing behavioral, sensory or motor variables from spike trains and hypothesized tuning curves) and generative models (predicting the spike trains from hypothesized encoding models and decoded variables). We review examples of each of these processes in hippocampal activity, and propose a general methodology to examine cognitive processes via the identification of dynamic changes in covert variables.
Bibliographical noteFunding Information:
We thank Matthijs van der Meer, Anoopum Gupta, Jadin Jackson, David Crowe, Matt Chafee, Dave Rowland, Aldis Weible and the Cognitive Sciences Center at the University of Minnesota for helpful discussions. This work was supported by NIH grant MH080318 (A.D.R.), NSF grant IOS0725001 (A.A.F.), NIH training grant (T32 HD007151) (A.J.) and a Dissertation Fellowship from the Graduate School at the University of Minnesota (A.J.).