computational neuroscience
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How should I feel about sentiment analysis?

How should I feel about sentiment analysis? | computational neuroscience | Scoop.it

Sentiment analysis is all about figuring out the attitude of someone speaking or writing. With so much being expressed through various social media across the Web, knowing the actual attitude behind the words is more important than what’s being transmitted.

 

We do this with software applications that allow us to use automation to track sentiments about products, brands and individuals and to understand whether they’re viewed positively or negatively. We analyze blogs, reviews, tweets and comments as broadly as they’re available.

 

Find Out More: http://successfulworkplace.com/2012/11/04/how-should-i-feel-about-sentiment-analysis/


Via Antonino Militello
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Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning

Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning | computational neuroscience | Scoop.it

In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural plasticity for a very long time. Recently, neuronal intrinsic plasticity (IP) has become a hot topic in this area. IP is sometimes thought to be an information-maximization mechanism. However, it is still unclear how IP affects the performance of artificial neural networks in supervised learning applications. From an information-theoretical perspective, the error-entropy minimization (MEE) algorithm has newly been proposed as an efficient training method. In this study, we propose a synergistic learning algorithm combining the MEE algorithm as the synaptic plasticity rule and an information-maximization algorithm as the intrinsic plasticity rule. We consider both feedforward and recurrent neural networks and study the interactions between intrinsic and synaptic plasticity. Simulations indicate that the intrinsic plasticity rule can improve the performance of artificial neural networks trained by the MEE algorithm.


Via Ashish Umre
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Brain mapping reveals neurological basis of decision-making in rats

Brain mapping reveals neurological basis of decision-making in rats | computational neuroscience | Scoop.it
Scientists at UC San Francisco have discovered how memory recall is linked to decision-making in rats, showing that measurable activity in one part of the brain occurs when rats in a maze are playing out memories that help them decide which way to turn. The more they play out these memories, the more likely they are to find their way correctly to the end of the maze.

Via Ashish Umre
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Information Driven Self-Organization of Complex Robotic Behaviors

Information Driven Self-Organization of Complex Robotic Behaviors | computational neuroscience | Scoop.it

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.


Via Ashish Umre
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Origin of a giant synapse discovered

Origin of a giant synapse discovered | computational neuroscience | Scoop.it

Humans and most mammals can determine the spatial origin of sounds with remarkable acuity. We use this ability all the time—crossing the street; locating an invisible ringing cell phone in a cluttered bedroom. To accomplish this small daily miracle, the brain has developed a circuit that's rapid enough to detect the tiny lag that occurs between the moment the auditory information reaches one of our ears, and the moment it reaches the other. The mastermind of this circuit is the "Calyx of Held," the largest known synapse in the brain. EPFL scientists have revealed the role that a certain protein plays in initiating the growth of these giant synapses. The discovery, published in Nature Neuroscience, could also help shed light on a number of neuropsychiatric disorders.


Via Ashish Umre
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