Jeremy E. Adler
Sc.B. Brown University, 2006

1st Year Graduate Student

Advisor: Emilia Entcheva, Ph.D.

Department: Biomedical Engineering

Graduate Program: Applied Mathematics & Statistics,
Computational Biology


Abstract (rotation):

Advisor: Dr. Anthony Zador, Cold Spring Harbor Laboratory

Title: Engaging in an auditory task suppresses responses in rat auditory cortex

Gonzalo H. Otazu1, Lung-Hao Tai1,2 Jeremy Adler3 and Anthony M. Zador1
1. Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724
2. Graduate Program in Neuroscience, Stony Brook University, NY 11794
3. School of Medicine, Stony Brook University, NY 11794

Although the systems involved in attentional selection have been studied extensively, much less is known about the non-selective systems necessary to engage in a sensory task. To study these preparatory mechanisms, we compared neural activity in the auditory cortex elicited by sounds while rats performed a two-alternative choice auditory task (“engaged” condition) with those elicited by identical stimuli while subjects were awake but not performing a task (“idle” condition). Surprisingly, we found that engagement consistently suppressed cortical responses, an effect opposite in sign to that elicited by selective attention. In the auditory thalamus, engagement enhanced spontaneous firing rates but did not affect evoked responses, suggesting a simple model in which synaptic depression at the thalamocortical inputs attenuates the impact of the sensory stimulus. The cortical suppression associated with engagement might represent a switch from a neural representation optimized for signal detection to one better suited for the auditory discrimination needed for the task. Another topic of interest was the relative level of communication between areas of cortex in the various attentional states. To study this, we took the recordings at each node to construct a model of communication between them using a system identification algorithm. Within the context of each attentional state, we constructed a linear systems model to predict the output at a given node be considering the output at another node as input. Structure to the models was found solely in the evoked idle state, suggesting less cortical specialization when not prompted beforehand to focus on a task.