The emotional gatekeeper: a computational model of attentional selection and suppression through the pathway from the amygdala to the inhibitory thalamic reticular nucleus

In a complex environment that contains both opportunities and threats, it is important for an organism to flexibly direct attention based on current events and prior plans. The amygdala, the hub of the brain's emotional system, is involved in forming and signaling affective associations between stimuli and their consequences. The inhibitory thalamic reticular nucleus (TRN) is a hub of the attentional system that gates thalamo-cortical signaling. In the primate brain, a recently discovered pathway from the amygdala sends robust projections to TRN. Here we used computational modeling to demonstrate how the amygdala-TRN pathway, embedded in a wider neural circuit, can mediate selective attention guided by emotions. Our Emotional Gatekeeper model demonstrates how this circuit enables focused top-down, and flexible bottom-up, allocation of attention. The model suggests that the amygdala-TRN projection can serve as a unique mechanism for emotion-guided selection of signals sent to cortex for further processing. This inhibitory selection mechanism can mediate a powerful affective 'framing' effect that may lead to biased decision-making in highly charged emotional situations. The model also supports the idea that the amygdala can serve as a relevance detection system. Further, the model demonstrates how abnormal top-down drive and dysregulated local inhibition in the amygdala and in the cortex can contribute to the attentional symptoms that accompany several neuropsychiatric disorders.R01MH057414 - NIMH NIH HHS; R01 MH057414 - NIMH NIH HHS; R01 MH101209 - NIMH NIH HHS; R01NS024760 - NINDS NIH HHS; R01MH101209 - NIMH NIH HHS; R01 NS024760 - NINDS NIH HH

AI Dining Suggestion App

Trying to decide what to eat can sometimes be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as Application Program Interfaces (APIs) for using them. This restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an app that eases the process of finding a restaurant to eat. This app has a Tinder-like user friendly User Interface (UI) design to change the common way that lists of restaurants are presented to users on mobile apps. It also uses the help of Artificial Intelligence (AI) with neural networks to train both supervised and unsupervised learning models that can learn from one\u27s dining pattern over time to make better suggestions at any time