• A Dendritic Model Approach to Modeling Visual Impairments in Schizophrenia

• Project Year: 2019
• REU Student (s):   Orren Ravid | Rutgers University-New Brunswick NJ  
• Student 1 Institution: Rutgers University-New Brunswick
• Project Mentor: Konstantinos Michmizos
• Project Mentor Area: Computer Science
• Project Abstract: The field of neuroscience has long been challenged in quantifying the nature of the neurological disorder of schizophrenia. With the recent introduction of computer modeling, researchers have been able to more formally examine the mechanisms which have been proposed to explain the symptoms and behaviors found in those with the disorder. A variety of biophysical models, often centered around certain neurotransmitter systems such as the dopamine or glutamate systems, have been produced and studied over the past few decades. While these models do often align with certain symptoms found in individuals with schizophrenia, their predictions often contradict certain other observed symptoms or behaviors. This offers a challenge to researchers to either find a different holistic representation of the entire disorder or focus on a particular behavior found frequently in the disorder and explain it in a way that corroborates the relevant preexisting scientific literature. In this work, we opt for the latter by examining the proposed mechanism of apical amplification and its potential role in schizophrenia through the use of a dendritic compartment model architecture. We demonstrate that a neuron model with dendritic compartments is able to mimic the behavior of a neurotypical population in a visual classification task involving distinguishing a contour made up of specifically oriented gratings from a surround of randomly oriented gratings. This neuron model utilizes apical amplification to learn and replicate when individuals in the target population are able to distinguish the contour from the surround and when they are not. This suggests that apical amplification is a plausible mechanism for understanding the underlying neuronal mechanisms used in visual classification tasks. We hope to further this hypothesis in later research by perturbing the network parameters trained on the neurotypical population to then mimic the behavior of the population of individuals with schizophrenia. The new parameters of the model and their distinct configuration in contrast to that of the neurotypical population should provide a biologically plausible insight into whether aberrant functioning of the apical compartment is a reasonable hypothesis for explaining the impairments observed in individuals with schizophrenia in visual classification tasks.