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Neural Representation of Ambiguous Visual Objects in the Inferior Temporal Cortex

Neural Representation of Ambiguous Visual Objects in the Inferior Temporal Cortex | Social Foraging | Scoop.it

Inferior temporal (IT) cortex as the final stage of the ventral visual pathway is involved in visual object recognition. In our everyday life we need to recognize visual objects that are degraded by noise. Psychophysical studies have shown that the accuracy and speed of the object recognition decreases as the amount of visual noise increases. However, the neural representation of ambiguous visual objects and the underlying neural mechanisms of such changes in the behavior are not known. Here, by recording the neuronal spiking activity of macaque monkeys’ IT we explored the relationship between stimulus ambiguity and the IT neural activity. We found smaller amplitude, later onset, earlier offset and shorter duration of the response as visual ambiguity increased. All of these modulations were gradual and correlated with the level of stimulus ambiguity. We found that while category selectivity of IT neurons decreased with noise, it was preserved for a large extent of visual ambiguity. This noise tolerance for category selectivity in IT was lost at 60% noise level. Interestingly, while the response of the IT neurons to visual stimuli at 60% noise level was significantly larger than their baseline activity and full (100%) noise, it was not category selective anymore. The latter finding shows a neural representation that signals the presence of visual stimulus without signaling what it is. In general these findings, in the context of a drift diffusion model, explain the neural mechanisms of perceptual accuracy and speed changes in the process of recognizing ambiguous objects.

 

Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076856

 

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How the Brain Reconstructs Past Events

How the Brain Reconstructs Past Events | Social Foraging | Scoop.it
When someone tries to remember 1 aspect of an event, such as who they met yesterday, the representation of the entire event can be reactivated in the brain, including incidental information such as where they were and what they did.

When remembering something from our past, we often vividly re-experience the whole episode in which it occurred. New UCL research funded by the Medical Research Council and Wellcome Trust has now revealed how this might happen in the brain.

The study, published in Nature Communications, shows that when someone tries to remember one aspect of an event, such as who they met yesterday, the representation of the entire event can be reactivated in the brain, including incidental information such as where they were and what they did.
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cantatapledge's comment, July 3, 6:35 AM
Thats incredible
ed alvarado's comment, Today, 12:33 AM
Its useful
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Diffusion of innovations in Axelrod's model

Axelrod's model for the dissemination of culture contains two key factors required to model the process of diffusion of innovations, namely, social influence (i.e., individuals become more similar when they interact) and homophily (i.e., individuals interact preferentially with similar others). The strength of these social influences are controlled by two parameters: F, the number of features that characterizes the cultures and q, the common number of states each feature can assume. Here we assume that the innovation is a new state of a cultural feature of a single individual -- the innovator -- and study how the innovation spreads through the networks among the individuals. For infinite regular lattices in one and two dimensions, we find that initially the innovation spreads linearly with the time t and diffusively in the long time limit, provided its introduction in the community is successful. For finite lattices, the growth curves for the number of adopters are typically concave functions of t. For random graphs with a finite number of nodes N, we argue that the classical S-shaped growth curves result from a trade-off between the average connectivity K of the graph and the per feature diversity q. A large q is needed to reduce the pace of the initial spreading of the innovation and thus delimit the early-adopters stage, whereas a large K is necessary to ensure the onset of the take-off stage at which the number of adopters grows superlinearly with t. In an infinite random graph we find that the number of adopters of a successful innovation scales with tγ with γ=1 for K>2 and 1/2<γ<1 for K=2. We suggest that the exponent γ may be a useful index to characterize the process of diffusion of successful innovations in diverse scenarios.
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Network Hubs in the Brain Have the Biggest Impact on Behavior

Network Hubs in the Brain Have the Biggest Impact on Behavior | Social Foraging | Scoop.it

The most highly evolved brain region in mammals is the prefrontal cortex, which regulates our thoughts, actions, and emotions through extensive connections with other brain regions. Studies in humans have shown that multiple parts of the prefrontal cortex are activated during memory tasks, but patients with damage to some of these areas do not always have memory problems. As a result, researchers have disputed whether memory deficits are caused by damage to individual brain areas subserving specific cognitive functions or by an interruption in the flow of information among widely distributed areas in the prefrontal cortex.

A recently proposed hypothesis reconciles these views by suggesting that cortical areas form a highly ordered network containing hubs that play a critical role in information processing, such that damage to a hub results in severe cognitive impairment. However, most investigations of network structure have relied on either anatomical studies or functional neuroimaging of spontaneous activity at rest, ignoring brain activity related to specific cognitive tasks.

In a study published this week in PLOS Biology, Yasushi Miyashita of the University of Tokyo School of Medicine and his colleagues used functional magnetic resonance imaging (fMRI) and a novel simulated-lesion method in monkeys to show that virtual damage to a prefrontal cortex hub, which was the most highly interconnected with other brain areas activated during a memory task, was predicted to produce the most severe memory impairment. By contrast, virtual damage to a highly interconnected prefrontal cortex hub that was previously identified in anatomical tracer studies was not predicted to produce severe memory problems. According to the authors, these findings lay the foundation for precisely predicting the behavioral and cognitive impact of injuries or surgical interventions in the human brain.

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MetaMind: Computers Are Getting a Dose of Common Sense

MetaMind: Computers Are Getting a Dose of Common Sense | Social Foraging | Scoop.it
A startup called MetaMind has developed a new, improved algorithm for processing language.

 

Talking to a machine over the phone or through a chat window can be an infuriating experience. However, several research groups, including some at large technology companies like Facebook and Google, are making steady progress toward improving computers’ language skills by building upon recent advances in machine learning.

 

The latest advance in this area comes from a startup called MetaMind, which has published details of an algorithm that is more accurate than other techniques at answering questions about several lines of text that tell a story. MetaMind is developing technology designed to be capable of a range of different artificial-intelligence tasks and hopes to sell it to other companies. The startup was founded by Richard Socher, a prominent machine-learning expert who earned a PhD at Stanford.

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gelatinzoom's comment, June 26, 7:00 AM
Good
lackingingot's comment, June 30, 3:00 AM
Thats interesting
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Ongoing dynamics in large-scale functional connectivity predict perception

Ongoing dynamics in large-scale functional connectivity predict perception | Social Foraging | Scoop.it
Most brain activity is not directly evoked by specific external events. This ongoing activity is correlated across distant brain regions within large-scale networks. This correlation or functional connectivity may reflect communication across brain regions. Strength and spatial organization of functional connectivity changes dynamically over seconds to minutes. Using functional MRI, we show that these ongoing changes correlate with behavior. The connectivity state before playback of a faint sound predicted whether the participant was going to perceive the sound on that trial. Connectivity states preceding missed sounds showed weakened modular structure, in which connectivity was more random and less organized across brain regions. These findings suggest that ongoing brain connectivity dynamics contribute to explaining behavioral variability.
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Organize a walk around London with R

Organize a walk around London with R | Social Foraging | Scoop.it
The subtitle of this post can be “How to plot multiple elements on interactive web maps in R“.
In this experiment I will show how to include multiple elements in interactive maps created using both plotGoogleMaps and leafletR. To complete the work presented here you would need the following packages: sp, raster, plotGoogleMaps and leafletR.

I am going to use data from the OpenStreet maps, which can be downloaded for free from this website: weogeo.com
In particular I downloaded the shapefile with the stores, the one with the tourist attractions and the polyline shapefile with all the roads in London. I will assume that you want to spend a day or two walking around London, and for this you would need the location of some hotels and the locations of all the Greggs in the area, for lunch. You need to create a web map that you can take with you when you walk around the city with all these customized elements, that’s how you create it.
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Functioning brain follows famous sand pile model

Functioning brain follows famous sand pile model | Social Foraging | Scoop.it
One of the deep problems in understanding the brain is to understand how relatively simple computing units (the neurons), collectively perform extremely complex operations (thinking).
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A Novel Method for Tracking Individuals of Fruit Fly Swarms Flying in a Laboratory Flight Arena

A Novel Method for Tracking Individuals of Fruit Fly Swarms Flying in a Laboratory Flight Arena | Social Foraging | Scoop.it
The growing interest in studying social behaviours of swarming fruit flies, Drosophila melanogaster, has heightened the need for developing tools that provide quantitative motion data. To achieve such a goal, multi-camera three-dimensional tracking technology is the key experimental gateway. We have developed a novel tracking system for tracking hundreds of fruit flies flying in a confined cubic flight arena. In addition to the proposed tracking algorithm, this work offers additional contributions in three aspects: body detection, orientation estimation, and data validation. To demonstrate the opportunities that the proposed system offers for generating high-throughput quantitative motion data, we conducted experiments on five experimental configurations. We also performed quantitative analysis on the kinematics and the spatial structure and the motion patterns of fruit fly swarms. We found that there exists an asymptotic distance between fruit flies in swarms as the population density increases. Further, we discovered the evidence for repulsive response when the distance between fruit flies approached the asymptotic distance. Overall, the proposed tracking system presents a powerful method for studying flight behaviours of fruit flies in a three-dimensional environment.
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Can We Design Trust Between Humans and Artificial Intelligence?

Can We Design Trust Between Humans and Artificial Intelligence? | Social Foraging | Scoop.it
For many years, interacting with artificial intelligence has been the stuff of science fiction and academic projects, but as smart systems take over more and more responsibilities, replace jobs, and become involved with complex emotionally charged decisions, figuring out how to collaborate with these systems has become a pragmatic problem that needs pragmatic solutions.

Machine learning and cognitive systems are now a major part many products people interact with every day, but to fully exploit the potential of artificial intelligence, people need much richer ways of communicating with the systems they use. The role of designers is to figure out how to build collaborative relationships between people and machines that help smart systems enhance human creativity and agency rather than simply replacing them.
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The Sense of Confidence during Probabilistic Learning: A Normative Account

The Sense of Confidence during Probabilistic Learning: A Normative Account | Social Foraging | Scoop.it
Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable “feeling of knowing” or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process.
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Researchers develop method to recreate connections between neurons from different brain areas

Researchers develop method to recreate connections between neurons from different brain areas | Social Foraging | Scoop.it
Mapping the human brain's network of interconnections, known as the connectome is typically done with help from computational tools because recreating interconnections between different brain regions has been challenging in the lab. Researchers at the Okinawa Institute of Science and Technology Graduate University have developed a method to recreate connections between neurons from two different brain areas in a dish.
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Data Mining Reveals How Human Health Varies with City Size

Data Mining Reveals How Human Health Varies with City Size | Social Foraging | Scoop.it
The science of allometry, the study of the relationship between body size and shape, is more than 100 years old. It dates to the late 19th century, when anatomists became fascinated by the link between the size and strength of appendages such as arms and legs in creatures of varying size.

In recent years, various researchers have begun to think of cities as “living” entities in which activity patterns change over regular 24-hour periods and which also vary dramatically depending on city size. That’s lead to a new science of city-related allometry—how various aspects of life vary with the size of the conurbation they take place in.

Today, we get a new insight into this emerging science thanks to the work of Luis Rocha at the University of Namur in Belgium and a couple of pals who have studied the way health varies with city size. These guys have made some surprising discoveries.

It’s easy to imagine that a city is simply the sum of its parts. But economists, sociologists, and city planners have long known that many aspects of city life do not scale linearly with the size of a city.
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Twitter Acquires Machine Learning Startup Whetlab

Twitter Acquires Machine Learning Startup Whetlab | Social Foraging | Scoop.it
Twitter announced today that it has acquired Cambridge-based machine learning startup Whetlab in an effort to accelerate the company’s own in-house efforts on the matter. Deal terms were not immediately available, but Twitter will gain both access to Whetlab’s technology and small team following the acquisition, while Whetlab’s current product will be discontinued next month, the company notes.

Whetlab was developing A.I.-like technologies that would make machine learning easier for companies to implement. Its system had been designed to get a company’s internal systems off the ground automatically, and therefore, more quickly than before. This could potentially reduce the time it takes to train a new machine learning system from months to just days.
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Why Google's Neural Networks Look Like They're on Acid

Why Google's Neural Networks Look Like They're on Acid | Social Foraging | Scoop.it
Recently, a mysterious photo appeared on Reddit showing a monstrous mutant: an iridescent, multi-headed, slug-like creature covered with melting animal faces. Soon, the image’s true origins surfaced, in the form of a blog post by a Google research team. It turned out the otherworldly picture was, in fact, inhuman. It was the product of an artificial neural network—a computer brain—built to recognize images. And it looked like it was on drugs.

Many commenters on Reddit and Hacker News noticed immediately that the images produced by the neural network were strikingly similar to what one sees on psychedelic substances such as mushrooms or LSD. “The level of resemblance with a psychotropics trip is simply fascinating,” wrote Hacker News commenter joeyspn. User henryl agreed: “I'll be the first to say it... It looks like an acid/shroom trip.”
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Emilycanfield's comment, Today, 2:11 AM
Incredible...!!
Incredible...!!
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Human brain may contain a map for social navigation

Human brain may contain a map for social navigation | Social Foraging | Scoop.it
The brain region that helps people tell whether an object is near or far may also guide how emotionally close they feel to others and how they rank them socially, according to a study conducted at the Icahn School of Medicine at Mount Sinai and published today in the journal Neuron. The findings promise to yield new insights into the social deficits that accompany psychiatric disorders like schizophrenia and depression.


The study focused on evidence for the existence of a "social map" in the hippocampus, the part of the brain that remembers locations in physical space and the order in which events occur. While previous studies had suggested that the hippocampus records a 3-dimensional representation of our surroundings when a key set of nerve cells fires, how the hippocampus contributes to social behavior had not been previously described.
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Onion Omega: An invention platform for the Internet of Things

Omega is an invention platform for the Internet of Things. It comes WiFi-enabled and supports most of the popular languages such as Python and Node.JS. Omega makes hardware prototyping as easy as creating and installing software apps.

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15 Most Common Types of Data Visualisation — Datalabs

15 Most Common Types of Data Visualisation — Datalabs | Social Foraging | Scoop.it
With the growing amount and accessibility of data, data visualisation is becoming increasingly important. Not only does visualised data represent large quantities of data coherently, it doesn’t distort what the data has to say and helps the user discern relationships in the data. According to the writers of A Tour Through the Visualization Zoo, “The goal of visualization is to aid our understanding of data by leveraging the human visual system’s highly-tuned ability to see patterns, spot trends, and identify outliers.”
In general, there are two basic types of data visualisation: exploration, which helps find a story the data is telling you, and explanation, which tells a story to an audience. Both types of data visualisation must take into account the audience’s expectations.
Within these two basic categories, there are many different ways data can be made visual. In this article we’ll go through the 15 most common types of data visualisation that fall under the 2D area, temporal, multidimensional, hierarchical and network categories.
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ed alvarado's comment, Today, 12:33 AM
Thats astounding...
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University of Cambridge is Recruiting for a Professor of LEGO

University of Cambridge is Recruiting for a Professor of LEGO | Social Foraging | Scoop.it
The incoming applicant does not necessarily need to know their bricks from their baseplates. Instead, they will head a new research center that focuses on children’s relationships with play in education, development and learning. They will also investigate how unrestrictive play can help improve a child’s experience of education.

This unusually titled position was created by the university after receiving $6.2 million (£4 million) in donations from the Lego Foundation, which aims "to make children's lives better – and communities stronger – by making sure the fundamental value of play is understood, embraced and acted upon." The Lego Foundation owns 25% of the Danish toy company Lego.
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The Psychology Behind Wearables

The Psychology Behind Wearables | Social Foraging | Scoop.it
If you’re like me, you’ve probably bought a wearable that tracks your steps in hopes it will inspire you to walk more. And if you’re also like me, you learned quickly what it takes to reach your daily goal and started leaving your gadget collecting dust in a drawer.

This is not a unique scenario. According to Endeavor Partners, a market researcher, while at least 1 in 10 Americans over the age of 18 owns a tracking device like the Fitbit or Nike Fuelband, more than a third of those who get them abandon them within a few months. The reasons given include meaningless stats, poor design, and loss of interest.

Elizabeth Churchill, a specialist in user experience with a background in experimental psychology, is studying how to go beyond the data wearables gather to motivate people on a subconscious level to take charge of their health. She is coauthor of “Wellth Creation: Using Computer Science to Support Proactive Health,” published last November in IEEE’s Computer magazine, which can be downloaded from the IEEE Xplore Digital Library.
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Good Liars Are Neither ‘Dark’ Nor Self-Deceptive

Good Liars Are Neither ‘Dark’ Nor Self-Deceptive | Social Foraging | Scoop.it
Deception is a central component of the personality 'Dark Triad' (Machiavellianism, Psychopathy and Narcissism). However, whether individuals exhibiting high scores on Dark Triad measures have a heightened deceptive ability has received little experimental attention. The present study tested whether the ability to lie effectively, and to detect lies told by others, was related to Dark Triad, Lie Acceptability, or Self-Deceptive measures of personality using an interactive group-based deception task. At a group level, lie detection accuracy was correlated with the ability to deceive others—replicating previous work. No evidence was found to suggest that Dark Triad traits confer any advantage either to deceive others, or to detect deception in others. Participants who considered lying to be more acceptable were more skilled at lying, while self-deceptive individuals were generally less credible and less confident when lying. Results are interpreted within a framework in which repeated practice results in enhanced deceptive ability.
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Hallucinatory Portraits Show How Computers See Our Faces

Hallucinatory Portraits Show How Computers See Our Faces | Social Foraging | Scoop.it
Teaching computers how to interpret visual data is going to be essential to the development of things like self-driving cars and mood-sensing technologies? Humans assess the world around us mainly through vision, and computers are starting to do the same. Digital artist Adam Ferriss works with facial recognition software and pushes it to the extreme, creating portraits that reveal the strange beauty in a machine's search for meaning.

For his project, Ferriss took a facial recognition algorithm called SURF, which is used to identify "interesting" parts of an image, also known as feature detection. By turning up the settings so that the feature detection is way more sensitive than necessary, the portraits become a mere backdrop for thousands of lines and circles indicating features where none are apparent.
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Stochastic Dynamics Underlying Cognitive Stability and Flexibility

Stochastic Dynamics Underlying Cognitive Stability and Flexibility | Social Foraging | Scoop.it
Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.
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Posterior Probability Matching and Human Perceptual Decision Making

Posterior Probability Matching and Human Perceptual Decision Making | Social Foraging | Scoop.it
Author Summary Decision making is partly random: a person can make different decisions at different times based on the same information. The theory of probability matching says that one reason for this randomness is that people usually choose the response that they think is most likely to be correct, but they sometimes intentionally choose the response that they think is less likely to be correct. Probability matching is a theory that was developed to describe how people try to predict the ou
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Cortical information flow during flexible sensorimotor decisions

During flexible behavior, multiple brain regions encode sensory inputs, the current task, and choices. It remains unclear how these signals evolve. We simultaneously recorded neuronal activity from six cortical regions [middle temporal area (MT), visual area four (V4), inferior temporal cortex (IT), lateral intraparietal area (LIP), prefrontal cortex (PFC), and frontal eye fields (FEF)] of monkeys reporting the color or motion of stimuli. After a transient bottom-up sweep, there was a top-down flow of sustained task information from frontoparietal to visual cortex. Sensory information flowed from visual to parietal and prefrontal cortex. Choice signals developed simultaneously in frontoparietal regions and travelled to FEF and sensory cortex. This suggests that flexible sensorimotor choices emerge in a frontoparietal network from the integration of opposite flows of sensory and task information.
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Google DeepMind Teaches Artificial Intelligence Machines to Read

Google DeepMind Teaches Artificial Intelligence Machines to Read | Social Foraging | Scoop.it
A revolution in artificial intelligence is currently sweeping through computer science. The technique is called deep learning and it’s affecting everything from facial and voice to fashion and economics.

But one area that has not yet benefitted is natural language processing—the ability to read a document and then answer questions about it. That’s partly because deep learning machines must first learn their trade from vast databases that are carefully annotated for the purpose. However, these simply do not exist in sufficient size to be useful.

Today, that changes thanks to the work of Karl Moritz Hermann at Google DeepMind in London and a few pals. These guys say the special way that the Daily Mail and CNN write online news articles allows them to be used in this way. And the sheer volume of articles available online creates for the first time, a database that computers can use to learn and then answer related about. In other words, DeepMind is using Daily Mail and CNN articles to teach computers to read.

The deep learning revolution has come about largely because of two breakthroughs. The first is related to neural networks, where computer scientists have developed new techniques to train networks with many layers, a task that has been tricky because of the number of parameters that must be fine-tuned. The new techniques essentially produce “ready-made” nets that are ready to learn.
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