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Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available at https://goo.gl/KdWAcV. [ABSTRACT FROM AUTHOR]

Basal ganglia circuit is an important subcortical system of the brain thought to be responsible for reward-based learning. Striatum, the largest nucleus of the basal ganglia, serves as an input port that maps cortical information. Microanatomical studies show that the striatum is a mosaic of specialized input-output structures called striosomes and regions of the surrounding matrix called the matrisomes. We have developed a computational model of the striatum using layered self-organizing maps to capture the center-surround structure seen experimentally and explain its functional significance. We believe that these structural components could build representations of state and action spaces in different environments. The striatum model is then integrated with other components of basal ganglia, making it capable of solving reinforcement learning tasks. We have proposed a biologically plausible mechanism of action-based learning where the striosome biases the matrisome activity toward a preferred action. Several studies indicate that the striatum is critical in solving context dependent problems. We build on this hypothesis and the proposed model exploits the modularity of the striatum to efficiently solve such tasks.

Basal ganglia circuit is an important subcortical system of the brain thought to be responsible for reward-based learning. Striatum, the largest nucleus of the basal ganglia, serves as an input port that maps cortical information. Microanatomical studies show that the striatum is a mosaic of specialized input-output structures called striosomes and regions of the surrounding matrix called the matrisomes. We have developed a computational model of the striatum using layered self-organizing maps to capture the center-surround structure seen experimentally and explain its functional significance. We believe that these structural components could build representations of state and action spaces in different environments. The striatum model is then integrated with other components of basal ganglia, making it capable of solving reinforcement learning tasks. We have proposed a biologically plausible mechanism of action-based learning where the striosome biases the matrisome activity toward a preferred action. Several studies indicate that the striatum is critical in solving context dependent problems. We build on this hypothesis and the proposed model exploits the modularity of the striatum to efficiently solve such tasks. [ABSTRACT FROM AUTHOR]

Abstract: Preclinical studies show that estradiol enhances sensitization to cocaine in females by mechanisms not fully understood. These studies consistently show that ovariectomized (OVX) rats exhibit little or no sensitization to cocaine compared to OVX rats administered estradiol. In this study we varied the dose of cocaine (10, 15, and 30mg/kg), the length of cocaine treatment (from 5 to 10days) and the context of cocaine injections to determine if these factors play a role on estradiol's effects on cocaine sensitization. Because OVX rats are hormonally compromised, they are not representative of the natural state of the animal, and thus the physiological context of these studies remains unclear. To address this issue, we blocked ERs in gonadally intact females by icv administration of the antiestrogen ICI-182,780. Varying the dose or length of exposure to cocaine does not alter estradiol's effect on cocaine sensitization. In contrast, a highly context-dependent sensitization protocol results in robust sensitization even in OVX rats. Interestingly, using this protocol, sensitization in OVX rats diminished with time, suggesting that estradiol is necessary for the maintenance of cocaine sensitization. Blocking brain ERs with ICI completely abolishes the development and expression of cocaine sensitization in gonadally intact female rats, even when tested in a highly context-dependent sensitization protocol. Given these findings, we propose that activation of brain ERs is required for the development and maintenance of sensitization and CPP. [Copyright &y& Elsevier]