Mutual Information-based RBM Neural Networks

(Deep) neural networks are increasingly being used for various computer vision and pattern recognition tasks due to their strong ability to learn highly discriminative features. However, quantitative analysis of their classication ability and design philosophies are still nebulous. In this work, we use information theory to analyze the concatenated restricted Boltzmann machines (RBMs) and propose a mutual information-based RBM neural networks (MI-RBM). We develop a novel pretraining algorithm to maximize the mutual information between RBMs. Extensive experimental results on various classication tasks show the eectiveness of the proposed approach.

The Impact of Prenatal Nicotine Exposure on Impulsivity and Neural Firing in the Medial Prefrontal Cortex

Prenatal nicotine exposure (PNE) is linked to a large number of psychiatric disorders, including attention deficit hyperactivity disorder (ADHD). Current literature suggests that core deficits observed in ADHD reflect abnormal inhibitory control governed by the prefrontal cortex (PFC) of the brain. The PFC is structurally altered by PNE, but it is still unclear how neural firing is affected during tasks that test behavioral inhibition, such as the stop-signal task, or if neural correlates related to inhibitory control are affected after PNE in awake behaving animals. To address these questions, we recorded from single medial PFC (mPFC) neurons in control rats and PNE rats as they performed our stopsignal task. We found that PNE rats were faster for all trial types and were less likely to inhibit the behavioral response on STOP trials. Neurons in mPFC fired more strongly on STOP trials and were correlated with accuracy and reaction time. Although the number of neurons exhibiting significant modulation during task performance did not differ between groups, overall activity in PNE was reduced. We conclude that PNE makes rats impulsive and reduces firing in mPFC neurons that carry signals related to response inhibition.