The Role of Ventral Tegmental Area and Nucleus Accumbens in the Kamin Blocking Effect

The overall aim of the research described in this thesis is to identify neural substrates underlying the Kamin blocking effect. This phenomenon is crucial for understanding of the neural mechanisms of associative learning. Kamin blocking refers to the finding that conditioned responding to a cue is attenuated when it is paired with a reinforcer in the presence of another cue which has previously been conditioned using that reinforcer. The blocking effect suggests that associative learning is driven by prediction errors, and is not based purely on temporal contiguity between events. In the context of appetitive classical conditioning, recent evidence suggests that the ventral tegmental area and the nucleus accumbens play a role in computing reward prediction error. The current study shows that blocking inhibition in the ventral tegmental area or inactivating the nucleus accumbens neurons during compound cue conditioning attenuates Kamin blocking. Inactivating the nucleus accumbens during single cue conditioning also attenuates Kamin blocking. Taken together, these findings suggest that inhibition in the ventral tegmental area, inhibitory output from the nucleus accumbens, and learning in the nucleus accumbens play crucial roles in the Kamin blocking effect. Previous studies show that dopamine transients track the theoretical reward prediction error during appetitive classical conditioning, and the reduction in the dopamine response evoked by the reward when it is expected has been suggested to play a role in the Kamin blocking effect. In support of this hypothesis, the current study also found that goal tracking rats, in which expected rewards have previously been shown to evoke a robust dopamine response, did not express the Kamin blocking effect. Conversely, sign trackers, in which expected rewards evoke a diminished dopamine response, expressed the blocking effect. These findings are discussed in relation to psychological theory of learning and the possible underlying neural mechanisms.Okinawa Institute of Science and Technology Graduate Universit

Android Based Smart Speech Recognition Application to Perform Various Tasks

A smart speech recognition application is theorized in this paper. User can control a variety of applications on an android based platform, which include native applications as well as user installed applications with voice commands. These include - calling, texting, switching on and off sensors (Wi-Fi, GPS, Bluetooth),setting alarms. The application provides online as well as offline services. The application also applies machine learning concepts to identify usage patterns and create an environment which anticipates user requirements. The tasks being performed repetitively are automated. Services of activity recognition, recognizing nearby friends using Bluetooth are performed. The importance of the project is that it provides visually challenged people as well as the general population an alternate and a very easy way to control applications on android smart phones

Detection of Fake News Using Machine Learning

For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news

Detection of Fake News Using Machine Learning

For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news

Impact of Cloud Computing in E-Learning: A Study

Objectives: Increase in the demand of e-learning initiate the need of cloud based e-learning. Review of all cloud based e-learning systems is provided which can be used to develop better e-learning systems. Method/Analysis: E-learning is less steep and easy way to study and discover but still due to need of lot of IT capabilities and infrastructure it is still not so famous in India, with the exploit of cloud computing it can be made more accessible and constructive. A wide survey has been carrying out with more than sixty papers. Each paper was studied in detail and their shortcomings were analysed to give a better idea what to do in future. Findings: It was pragmatic that e-learning solutions are several but cloud based solutions have many shortcomings like sanctuary, contents, services etc. which needs to be addressed. Improvement: While a lot of papers have been studied but still additional papers can be consider and a system can be created in future based on the interpretation of the systems

Conditioned approach behavior of SHR and SD rats during Pavlovian conditioning

Individual differences in reward-related learning are relevant to many behavioral disorders. Sensory cues that predict reward can become incentive stimuli that adaptively support behavior, or alternatively, cause maladaptive behaviors. The spontaneously hypertensive rat (SHR) expresses a genetically determined elevated sensitivity to delay of reward, and has been extensively studied as a behavioral model for attention deficit hyperactivity disorder (ADHD). We investigated reward-related learning in the SHR, comparing them to Sprague-Dawley (SD) rats as a reference strain. A standard Pavlovian conditioned approach task was used, in which a lever cue was followed by reward. Lever presses could occur while the lever was extended, but had no effect on reward delivery. The behavior of both the SHRs and the SD rats showed that they learnt that the lever cue predicted reward. However, the pattern of behavior differed between the strains. During lever cue presentation, SD rats pressed the lever more often and made fewer magazine entries than SHRs. When lever contacts that did not result in lever presses were analyzed, there was no significant difference between SHRs and SDs. These results suggest that the SHRs attributed less incentive value to the conditioned stimulus than the SD rats. During the presentation of the conditioned cue, cue directed responses are called sign tracking responses, whereas responses directed towards the food magazine are called goal tracking responses. Analysis of behavior using a standard Pavlovian conditioned approach index to quantify sign and goal tracking tendencies showed that both strains had a tendency towards goal tracking in this task. However, the SHRs showed a significantly greater goal tracking tendency than the SD rats. Taken together, these findings suggest that attribution of incentive value to reward predicting cues is attenuated in SHRs, which might explain their elevated sensitivity to delay of reward.journal articl