Context-dependent modulation of functional connectivity: secondary somatosensory cortex to prefrontal cortex connections in two-stimulus-interval discrimination tasks.
In a complex world, a sensory cue may prompt different actions in different contexts. A laboratory example of context-dependent sensory processing is the two-stimulus-interval discrimination task. In each trial, a first stimulus (f1) must be stored in short-term memory and later compared with a second stimulus (f2), for the animal to come to a binary decision. Prefrontal cortex (PFC) neurons need to interpret the f1 information in one way (perhaps with a positive weight) and the f2 information in an opposite way (perhaps with a negative weight), although they come from the very same secondary somatosensory cortex (S2) neurons; therefore, a functional sign inversion is required. This task thus provides a clear example of context-dependent processing. Here we develop a biologically plausible model of a context-dependent signal transformation of the stimulus encoding from S2 to PFC. To ground our model in experimental neurophysiology, we use neurophysiological data recorded by R. Romo's laboratory from both cortical area S2 and PFC in monkeys performing the task. Our main goal is to use experimentally observed context-dependent modulations of firing rates in cortical area S2 as the basis for a model that achieves a context-dependent inversion of the sign of S2 to PFC connections. This is done without requiring any changes in connectivity (Salinas, 2004b). We (1) characterize the experimentally observed context-dependent firing rate modulation in area S2, (2) construct a model that results in the sign transformation, and (3) characterize the robustness and consequent biological plausibility of the model.
Chow
Stephanie S
SS
Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autonoma de Mexico, 04510 Mexico City, Mexico. stephanieschow@gmail.com
Romo
Ranulfo
R
Brody
Carlos D
CD
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D004193:Discrimination Learning / Q000502:physiology*; D000882:Haplorhini; D016014:Linear Models; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000145:classification / Q000502:physiology*; D010812:Physical Stimulation / Q000379:methods; D017397:Prefrontal Cortex / Q000502:physiology*; D013003:Somatosensory Cortex / Q000502:physiology*; D013569:Synapses / Q000502:physiology; D013997:Time Factors