Topic: feedback loop

Human decision-making biases are pervasive, influencing areas such as finance, law and medicine. Reliance on Artificial Intelligence (AI) systems has been offered as a solution to reduce such systematic errors. However, in judgments ranging from perception to emotion, AI systems can also exhibit biases themselves. For example, a hiring algorithm used by Amazon was inevitably trained to favor men over women and an algorithm used in US hospitals to estimate the need for additional medical care favored white patients over black patients. While the danger of biased algorithms automating and perpe

In the olfactory system, cholinergic modulation has been associated with contrast modulation and changes in receptive fields in the olfactory bulb, as well the learning of odor associations in the olfactory cortex. Computational modeling and behavioral studies suggest that cholinergic modulation could improve sensory processing and learning while preventing pro-active interference when task demands are high. However, how sensory inputs and/or learning regulate incoming modulation has not yet been elucidated. We here use a computational model of the olfactory bulb, piriform cortex (PC) and hori

Elements of neural networks (NNs), both biological and artificial, can be described by selectivity to specific cognitive features. Revealing such features is important for understanding NNs' inner working mechanisms. For living systems, where the neural response to a stimulus is not a known function, one needs to build a feedback loop of exposing it to a set of different stimuli. The properties of such stimuli should be iteratively varied aiming in the direction of neuronal maximal response. To utilize such a loop for a biological network, one should run it quickly and efficiently, reaching th