Dynamic afferent synapses to decision - making networks improve performance in 2 tasks requiring stimulus associations and discriminations
نویسندگان
چکیده
1 1 Dynamic afferent synapses to decision-making networks improve performance in 2 tasks requiring stimulus associations and discriminations. 3 4 Mark A. Bourjaily and Paul Miller* 5 *Corresponding author Paul Miller 6 7 Author Contributions: MAB and PM conceived, designed and performed the 8 experiments, analyzed the data, and wrote the paper. 9 10 Department of Biology, Neuroscience Program, and 11 Volen Center for Complex Systems, Brandeis University, Waltham MA 02454 12 Running head: Short-term plasticity improves decision-making performance 13 Department of Biology and Volen Center for Complex Systems, 14 Brandeis University, 415 South St, Waltham, MA 02454-9110. Ms 013 15 Tel: (781) 736-2890 Fax: (781) 736-3142 16 Email: [email protected] 17 18
منابع مشابه
Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations.
Animals must often make opposing responses to similar complex stimuli. Multiple sensory inputs from such stimuli combine to produce stimulus-specific patterns of neural activity. It is the differences between these activity patterns, even when small, that provide the basis for any differences in behavioral response. In the present study, we investigate three tasks with differing degrees of over...
متن کاملComputational modeling of dynamic decision making using connectionist networks
In this research connectionist modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decisio...
متن کاملAn Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
متن کاملDynamic Interactions between Large-Scale Brain Networks Predict Behavioral Adaptation after Perceptual Errors
Failures to perceive visual stimuli lead to errors in decision making. Different theoretical accounts implicate either medial frontal (MF) cognitive control processes or prestimulus occipital (OC) cortical oscillatory dynamics in errors during perceptual tasks. Here, we show that these 2 previously unconnected theoretical accounts can be reconciled, and the brain regions described by the 2 theo...
متن کاملA DSS-Based Dynamic Programming for Finding Optimal Markets Using Neural Networks and Pricing
One of the substantial challenges in marketing efforts is determining optimal markets, specifically in market segmentation. The problem is more controversial in electronic commerce and electronic marketing. Consumer behaviour is influenced by different factors and thus varies in different time periods. These dynamic impacts lead to the uncertain behaviour of consumers and therefore harden the t...
متن کامل