Decoding The Neural Circuitry of Reward Behavior

Classical conditioning demonstrates that rewards can be used to train behavior by pairing a stimulus, known as a prompt, with reinforced behavior. At a neuronal level, this association strengthens the connections between the neurons involved, making communication easier the next time. Enhanced communication is identified with learning, allowing an organism to anticipate a reward with a prompt so that it can perform the desired behavior to successfully obtain the reward (Noonan et al., 2011). In this study, we created a computational model to represent a neural circuit with synaptic plasticity during reward, no-reward and anticipation states. Our results confirmed our hypothesis that the model would be able to differentiate between reward and no-reward stimuli and subsequently anticipate the likelihood of reward and no-reward states on ensuing trials

A Computational Perspective of Schizophrenia

The etiology of schizophrenia remains largely elusive, thus dampening the effectiveness of current treatment strategies. Abnormal neural migration and neurogenesis in the hippocampus have been suggested to be involved in schizophrenia (Jakob & Beckmann, 1994). A few approaches, including computational modeling, have investigated schizophrenia as a network disorder. Computational modeling uses mathematics to predict the behavior of biological systems based on the input of a set of parameters collected from laboratory experiments. In this study, we constructed a computational model to explore the ramifications of additional PV neurons migrating to an aberrant location in the hippocampus and interfering with a closed-loop circuit between a preexisting PV neuron and 10 pyramidal neurons. Evidence suggests that PV neurons provide GABAergic input and oscillating gamma rhythmicity (30-80 Hz) to pyramidal neurons in the CA1 region of the hippocampus (Tukker et al., 2007). We predict that asynchronous release of action potentials from a migratory PV neuron will decrease the level of excitation and reduce the gamma-band activity in our closed-loop computational circuit. If this computational model can make an accurate prediction, it may serve to be a reliable tool to probe the direction of future research in not only schizophrenia, but in a wide range of mental afflictions

McNair Research Journal - Summer 2015

Journal articles based on research conducted by undergraduate students in the McNair Scholars Program Table of Contents Biography of Dr. Ronald E. McNair Statements: Dr. Neal J. Smatresk, UNLV President Dr. Juanita P. Fain, Vice President of Student Affairs Dr. William W. Sullivan, Associate Vice President for Retention and Outreach Mr. Keith Rogers, Deputy Executive Director of the Center for Academic Enrichment and Outreach McNair Scholars Institute Staf

UNLV Title III AANAPISI & McNair Scholars Institute Research Journal

Journal articles based on research conducted by undergraduate students in the AANAPISI and McNair Scholars Programs Table of Contents About AANAPISI Biography of Dr. Ronald E. McNair Statements Dr. Len Jessup, UNLV President Dr. Juanita P. Fain, Vice President for Student Affairs Dr. William W. Sullivan, Associate Vice President for Retention and Outreach Mr. Keith Rogers, Deputy Executive Director of the Center for Academic Enrichment and Outreach Title III AANAPISI and McNair Scholars Institute Staff Ms. Terri Bernstein, Director for College Programs Dr. Matthew Della Sala, Assistant Director for Undergraduate Researc