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An incredible map of which countries e-mail each other, and why

An incredible map of which countries e-mail each other, and why | Social Foraging | Scoop.it

The Internet was supposed to let us bridge continents and cultures like never before. But after analyzing more than 10 million e-mails from Yahoo! mail, a team of computer researchers noticed an interesting phenomenon: E-mails tend to flow much more frequently between countries with certain economic and cultural similarities.

 

Among the factors that matter are GDP, trade, language, non-Commonwealth colonial relations, and a couple of academic-sounding cultural metrics, like power-distance, individualism, masculinity and uncertainty. (More on those later.)

 

The findings were released in a paper titled “The Mesh of Civilizations and International Email Flows,” written by researchers at Stanford, Cornell, Yahoo! and Qatar’s Computational Research Institute.

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Social Foraging
Dynamics of Social Interaction
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Spatio-Temporal Techniques for User Identification by means of GPS Mobility Data

One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of "significant places", thus making it possible to identify a user from his/her mobility data.
In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.
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OnionBots: Subverting Privacy Infrastructure for Cyber Attacks

Over the last decade botnets survived by adopting a sequence of increasingly sophisticated strategies to evade detection and take overs, and to monetize their infrastructure. At the same time, the success of privacy infrastructures such as Tor opened the door to illegal activities, including botnets, ransomware, and a marketplace for drugs and contraband. We contend that the next waves of botnets will extensively subvert privacy infrastructure and cryptographic mechanisms. In this work we propose to preemptively investigate the design and mitigation of such botnets. We first, introduce OnionBots, what we believe will be the next generation of resilient, stealthy botnets. OnionBots use privacy infrastructures for cyber attacks by completely decoupling their operation from the infected host IP address and by carrying traffic that does not leak information about its source, destination, and nature. Such bots live symbiotically within the privacy infrastructures to evade detection, measurement, scale estimation, observation, and in general all IP-based current mitigation techniques. Furthermore, we show that with an adequate self-healing network maintenance scheme, that is simple to implement, OnionBots achieve a low diameter and a low degree and are robust to partitioning under node deletions. We developed a mitigation technique, called SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and discuss a set of techniques that can enable subsequent waves of Super OnionBots. In light of the potential of such botnets, we believe that the research community should proactively develop detection and mitigation methods to thwart OnionBots, potentially making adjustments to privacy infrastructure.
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How A Box Could Solve The Personal Data Conundrum

How A Box Could Solve The Personal Data Conundrum | Social Foraging | Scoop.it
One of the trickiest issues for anyone with an online presence is how to manage personal information. Almost any form of surfing leaves a data trail that advertisers, social networks and so on can use to their advantage.

This data gold rush is largely driven by the dominant online business model in which advertising is the primary source of revenue. The gathered data can sometimes be processed in a way that individuals find useful. But this information can also be abused, sometimes with severe consequences, as anyone who has suffered identity theft will testify.

What’s more, information can fall into the hands of companies almost by default, regardless of the wishes of the owner. For example, Google scans the contents of all emails on its Gmail service.

Of course, people can choose to use a different service if they object to this. But they will find it much harder to avoid other people with Gmail accounts. Send them an email and Google will scan the contents anyway.

The options for avoiding these scenarios are not good. The ultimate possibility is opting out of the online world but that is simply not viable for most people. So what to do?

Today, Hamed Haddadi from Queen Mary University of London and a few pals from the University of Cambridge put forward their own manifesto for solving this problem. These guys say the solution is a piece of software that collects personal data and then manages how the information is made available to third parties.

Haddadi and co call this software a Databox and suggest that it could kickstart a new generation of business models in which both individuals and companies profit from the personal data revolution.
Ashish Umre's insight:

Ref: arxiv.org/abs/1501.04737  Personal Data: Thinking Inside the Box

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Coaching by data : Maths over Mourinho? Analytics over Ancelotti? is analytics the future of management?

Coaching by data : Maths over Mourinho? Analytics over Ancelotti? is analytics the future of management? | Social Foraging | Scoop.it
The 2011 film Moneyball depicts the true story of baseball manager Billy Beane (played by Brad Pitt) who defies all odds by taking an underdog team to the playoffs and winning a record 20 consecutive games despite spending very little money on players. His killer weapon? Data. Indeed, by hiring a geek rather than experienced baseball scouts, and trusting computer-generated algorithms rather than common sense, Pitt’s character demonstrates that sporting success does not depend on common sense or intuition, but robust scientific principles and maths.

Unsurprisingly, the film has spurred a great deal of speculation about the idea that technology may eventually replace sports managers. The underlying logic to this idea is twofold. First, computers are able to gather and process much more data than we do, which enables them to better predict future performance; second, unlike humans, computers are not biased by emotions or subjectivity, so their decisions are bound to be more rational than ours.

Despite the appeal of this sports analytics, it is fairly unlikely that Jose Mourinho or Greg Popovich will be out of work soon. There are three main reasons for this.
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Pinterest Acquires Machine Learning Commerce Recommendation Engine Kosei

Pinterest Acquires Machine Learning Commerce Recommendation Engine Kosei | Social Foraging | Scoop.it
Facebook knows who you were. Google knows what you want now. But Pinterest yearns to know what you want next, which is “exactly” why it acquired recommendation engine startup Kosei, Pinterest’s head of engineering Michael Lopp tells me.

For an undisclosed figure, Pinterest gets Kosei’s tech that understands 400 million relationships between 30 million products, and the majority of its 10 person team including its co-founders. Lopp says the Kosei team will spend their first 90 days figuring out where to apply themselves across black ops spam deterrence, product discovery and recommendations, visual object recognition, ad click prediction for monetization, growth analytics, and building a machine learning system on spark for Pinterest’s data team.

Pinterest’s Lopp says its unclear exactly what they’ll work on as “they’re not really filling any gaps”. Instead, their goal is to help the company “hit internal metrics faster”. The company explains in an engineering blog post about the acquisition that “With the addition of the Kosei team, we can supercharge our existing graph to help brands reach people at the right moments, and improve content for Pinners.”
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The hidden beauty of pollination - Louie Schwartzberg

The hidden beauty of pollination - Louie Schwartzberg | Social Foraging | Scoop.it
Pollination: it's vital to life on Earth but largely unseen by the human eye. Filmmaker Louie Schwartzberg shows us the intricate world of pollen and pollinators with gorgeous high-speed images from his film "Wings of Life," inspired by the vanishing of one of nature's primary pollinators, the honeybee.
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Optimizely partnered with Stanford University statisticians to develop a revolutionary new approach to statistics for experience optimization.

Optimizely partnered with Stanford University statisticians to develop a revolutionary new approach to statistics for experience optimization. | Social Foraging | Scoop.it

Our new Stats Engine supports experimentation and decision-making on your terms—no precautions needed. We partnered with Stanford statisticians to develop a revolutionary new approach to statistics for experience optimization. The approach builds on everything we’ve learned from helping over 8,000 Optimizely customers optimize more than 7 billion experiences.

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Reputation drives cooperative behaviour and network formation in human groups

Reputation drives cooperative behaviour and network formation in human groups | Social Foraging | Scoop.it
Cooperativeness is a defining feature of human nature. Theoreticians have suggested several mechanisms to explain this ubiquitous phenomenon, including reciprocity, reputation, and punishment, but the problem is still unsolved. Here we show, through experiments conducted with groups of people playing an iterated Prisoner's Dilemma on a dynamic network, that it is reputation what really fosters cooperation. While this mechanism has already been observed in unstructured populations, we find that it acts equally when interactions are given by a network that players can reconfigure dynamically. Furthermore, our observations reveal that memory also drives the network formation process, and cooperators assort more, with longer link lifetimes, the longer the past actions record. Our analysis demonstrates, for the first time, that reputation can be very well quantified as a weighted mean of the fractions of past cooperative acts and the last action performed. This finding has potential applications in collaborative systems and e-commerce.
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The Tell-Tale Look: Viewing Time, Preferences, and Prices

The Tell-Tale Look: Viewing Time, Preferences, and Prices | Social Foraging | Scoop.it
Even the simplest choices can prompt decision-makers to balance their preferences against other, more pragmatic considerations like price. Thus, discerning people’s preferences from their decisions creates theoretical, empirical, and practical challenges. The current paper addresses these challenges by highlighting some specific circumstances in which the amount of time that people spend examining potential purchase items (i.e., viewing time) can in fact reveal their preferences. Our model builds from the gazing literature, in a purchasing context, to propose that the informational value of viewing time depends on prices. Consistent with the model’s predictions, four studies show that when prices are absent or moderate, viewing time provides a signal that is consistent with a person’s preferences and purchase intentions. When prices are extreme or consistent with a person’s preferences, however, viewing time is a less reliable predictor of either. Thus, our model highlights a price-contingent “viewing bias,” shedding theoretical, empirical, and practical light on the psychology of preferences and visual attention, and identifying a readily observable signal of preference.
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Astrophysicists Prove That Cities On Earth Grow in the Same Way As Galaxies in Space

Astrophysicists Prove That Cities On Earth Grow in the Same Way As Galaxies in Space | Social Foraging | Scoop.it
Urban sociologists have long known that a set of remarkable laws govern the large-scale interaction between individuals such as the probability that one person will befriend another and the size of the cities they live in.

The latter is an example of the Zipf’s law. If cities are listed according to size, then the rank of a city is inversely proportional to the number of people who live in it. For example, if the biggest city in the US has a population of 8 million people, the second-biggest city will have a population of 8 million divided by 2, the third biggest will have a population of 8 million divided by 3 and so on.

This simple relationship is known as a scaling law and turns out to fit the observed distribution of city sizes extremely well.

Another interesting example is the probability that one person will be friends with another. This turns out to be inversely proportional to the number of people who live closer to the first person than the second.

What’s curious about these laws is that although they are widely accepted, nobody knows why they are true. There is no deeper theoretical model from which these laws emerge. Instead, they come simply from the measured properties of cities and friendships.
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postitinning's comment, January 19, 2:34 AM
Too good
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Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution

It is tempting to treat frequency trends from Google Books data sets as indicators for the true popularity of various words and phrases. Doing so allows us to draw novel conclusions about the evolution of public perception of a given topic, such as time and gender. However, sampling published works by availability and ease of digitization leads to several important effects. One of these is the surprising ability of a single prolific author to noticeably insert new phrases into a language. A greater effect arises from scientific texts, which have become increasingly prolific in the last several decades and are heavily sampled in the corpus. The result is a surge of phrases typical to academic articles but less common in general, such as references to time in the form of citations. Here, we highlight these dynamics by examining and comparing major contributions to the statistical divergence of English data sets between decades in the period 1800--2000. We find that only the English Fiction data set from the second version of the corpus is not heavily affected by professional texts, in clear contrast to the first version of the fiction data set and both unfiltered English data sets. Our findings emphasize the need to fully characterize the dynamics of the Google Books corpus before using these data sets to draw broad conclusions about cultural and linguistic evolution.
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Study sheds light on chemicals that insects use to communicate and survive

Study sheds light on chemicals that insects use to communicate and survive | Social Foraging | Scoop.it
Most insects are covered with a thin layer of hydrocarbon molecules as a waterproofing barrier. Embedded in this layer are compounds that the insects use as chemical signals for a wide variety of functions such as communicating species and sex. In insects such as ants that live in colonies, they also differentiate the different castes (e.g., workers, queens, and drones).
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Representing “stuff” in visual cortex

Representing “stuff” in visual cortex | Social Foraging | Scoop.it
Despite decades of study, we do not understand the fundamental processes by which our brain encodes and represents incoming visual information and uses it to guide perception and action. A wealth of evidence suggests that visual recognition is mediated by a series of areas in primate cortex known as the ventral stream, including V1 (primary visual cortex), V2, and V4 (Fig. 1A) (1). The earliest stages are to some extent understood; Hubel and Wiesel famously discovered, for example, that neurons in V1 respond selectively to the orientation and direction of a moving edge (2). However, a vast gulf remains between coding for a simple edge and representing the full richness of our visual world. David Hubel himself observed in 2012 that we still “have almost no examples of neural structures in which we know the difference between the information coming in and what is going out—what the structure is for. We have some idea of the answer for the retina, the lateral geniculate body, and the primary visual cortex, but that’s about it” (3). In PNAS, Okazawa et al. (4) make significant headway in this quest by uncovering and characterizing a unique form of neural selectivity in area V4.
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Data Analysis Is Helping Farmers Increase Yields

Data Analysis Is Helping Farmers Increase Yields | Social Foraging | Scoop.it
Matt Schweigert owns 7,000 acres of corn and soybean fields in Cuba City, Wisconsin. His 25 tractors, combines, and other farm machinery are fitted with the latest in agricultural technology: sensors that track GPS location and measure the number of seeds planted, the volume of fertilizer sprayed, and the quantities of harvested ears and beans.

But to use the data, his employees typically glance at displays in the cabs (to spot problems such as poorly spaced seed planting); or they pull out thumb drives from onboard computers to analyze everything later.
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Cooperate without looking: Why we care what people think and not just what they do

Cooperate without looking: Why we care what people think and not just what they do | Social Foraging | Scoop.it
Evolutionary game theory typically focuses on actions but ignores motives. Here, we introduce a model that takes into account the motive behind the action. A crucial question is why do we trust people more who cooperate without calculating the costs? We propose a game theory model to explain this phenomenon. One player has the option to “look” at the costs of cooperation, and the other player chooses whether to continue the interaction. If it is occasionally very costly for player 1 to cooperate, but defection is harmful for player 2, then cooperation without looking is a subgame perfect equilibrium. This behavior also emerges in population-based processes of learning or evolution. Our theory illuminates a number of key phenomena of human interactions: authentic altruism, why people cooperate intuitively, one-shot cooperation, why friends do not keep track of favors, why we admire principled people, Kant’s second formulation of the Categorical Imperative, taboos, and love.
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Revealing Multiple Layers of Hidden Community Structure in Networks

We introduce a new conception of community structure, which we refer to as hidden community structure. Hidden community structure refers to a specific type of overlapping community structure, in which the detection of weak, but meaningful, communities is hindered by the presence of stronger communities. We present Hidden Community Detection HICODE, an algorithm template that identifies both the strong, dominant community structure as well as the weaker, hidden community structure in networks. HICODE begins by first applying an existing community detection algorithm to a network, and then removing the structure of the detected communities from the network. In this way, the structure of the weaker communities becomes visible. Through application of HICODE, we demonstrate that a wide variety of real networks from different domains contain many communities that, though meaningful, are not detected by any of the popular community detection algorithms that we consider. Additionally, on both real and synthetic networks containing a hidden ground-truth community structure, HICODE uncovers this structure better than any baseline algorithms that we compared against. For example, on a real network of undergraduate students that can be partitioned either by `Dorm' (residence hall) or `Year', we see that HICODE uncovers the weaker `Year' communities with a JCRecall score (a recall-based metric that we define in the text) of over 0.7, while the baseline algorithms achieve scores below 0.2.
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Scientists Use Thousands of Images to Study Genetics of the Brain

Scientists Use Thousands of Images to Study Genetics of the Brain | Social Foraging | Scoop.it
A large network of neuroscientists and doctors that compared over 30,000 brain images with people’s DNA says it’s found several genes that appear to influence the size of brain structures involved in intelligence and memory, as well as the volume of the brain itself.

Although the medical importance of these clues remains far from clear, the consortium, called Enigma, says its work demonstrates a novel distributed-computing strategy able to sort through vast numbers of MRI scans and DNA test results. “What Enigma is doing is combing through every pixel of every scan and comparing it to every genome,” says Paul Thompson, the neuroscientist who organized the research. “This is a roadmap to how you do this.”

Thompson, who is head of the Imaging Genetics Center at the University of Southern California, believes Enigma is the largest collaboration ever to combine efforts to study the brain. The new study, published today in the journal Nature, lists 287 authors and 193 institutions. The study involved the analysis of 30,717 brain scans as well as DNA information gathered by researchers in Cambodia, South Africa, the United States, and other countries.
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Memory and burstiness in dynamic networks

We introduce a class of complex network models which evolve through the addition of edges between nodes selected randomly according to their intrinsic fitness, and the deletion of edges according to their age. We add to this a memory effect where the attractiveness of a node is increased by the number of edges it is currently attached to, and observe that this creates burst-like activity in the attachment events of each individual node which is characterised by a power-law distribution of inter-event times. The fitness of each node depends on the probability distribution from which it is drawn; we find exact solutions for the expectation of the degree distribution for a variety of possible fitness distributions, and for both cases where the memory effect either is, or is not present. This work can potentially lead to methods to uncover hidden fitness distributions from fast changing, temporal network data such as online social communications and fMRI scans.
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Responsible Innovation - The Role of Ethics in an Increasingly Complex World


Via Complexity Digest
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A wireless neural recording system with a precision motorized microdrive for freely behaving animals

A wireless neural recording system with a precision motorized microdrive for freely behaving animals | Social Foraging | Scoop.it
The brain is composed of many different types of neurons. Therefore, analysis of brain activity with single-cell resolution could provide fundamental insights into brain mechanisms. However, the electrical signal of an individual neuron is very small, and precise isolation of single neuronal activity from moving subjects is still challenging. To measure single-unit signals in actively behaving states, establishment of technologies that enable fine control of electrode positioning and strict spike sorting is essential. To further apply such a single-cell recording approach to small brain areas in naturally behaving animals in large spaces or during social interaction, we developed a compact wireless recording system with a motorized microdrive. Wireless control of electrode placement facilitates the exploration of single neuronal activity without affecting animal behaviors. Because the system is equipped with a newly developed data-encoding program, the recorded data are readily compressed almost to theoretical limits and securely transmitted to a host computer. Brain activity can thereby be stably monitored in real time and further analyzed using online or offline spike sorting. Our wireless recording approach using a precision motorized microdrive will become a powerful tool for studying brain mechanisms underlying natural or social behaviors.
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The multilayer temporal network of public transport in Great Britain

The multilayer temporal network of public transport in Great Britain | Social Foraging | Scoop.it
Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.
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How America’s e-commerce giants compare across the desktop, mobile Web and mobile app arenas

How America’s e-commerce giants compare across the desktop, mobile Web and mobile app arenas | Social Foraging | Scoop.it
Games used to dominate the mobile app market, but as use of smartphones continues to grow and mobile devices are becoming bigger and with larger screens, apps are becoming a key driver for other sectors too. One such, rapidly growing, industy is online retail.

We’ve dug into our data to analyze the performance of the top five online retailers in the US, across the Web, mobile Web and Android mobile apps.

Rankings and ratings data used in this articles has been collected directly from app stores. Web and mobile Web and app data has been collected from our panel of millions of users who agree to share their data with us. All data is available in our competitive analysis platform – SimilarWeb PRO.

So, how are Amazon, eBay, Walmart, BestBuy and Target faring against each other at all three fronts?
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Baidu built a supercomputer for deep learning

Baidu built a supercomputer for deep learning | Social Foraging | Scoop.it
Chinese search engine company Baidu says it has built the world’s most-accurate computer vision system, dubbed Deep Image, which runs on a supercomputer optimized for deep learning algorithms. Baidu claims a 5.98 percent error rate on the ImageNet object classification benchmark; a team from Google won the 2014 ImageNet competition with a 6.66 percent error rate.
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tentuseful's comment, January 17, 4:22 AM
Thats awesome
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Slime mould foraging behaviour as optically coupled logical operations

Slime mould foraging behaviour as optically coupled logical operations | Social Foraging | Scoop.it
Physarum polycephalum is a macroscopic plasmodial slime mould whose apparently ‘intelligent’ behaviour patterns may be interpreted as computation. We employ plasmodial phototactic responses to construct laboratory prototypes of NOT and NAND logical gates with electrical inputs/outputs and optical coupling in which the slime mould plays dual roles of computing device and electrical conductor. Slime mould logical gates are fault tolerant and resettable. The results presented here demonstrate the malleability and resilience of biological systems and highlight how the innate behaviour patterns of living substrates may be used to implement useful computation.
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Sensory determinants of behavioral dynamics in Drosophila thermotaxis

Sensory determinants of behavioral dynamics in Drosophila thermotaxis | Social Foraging | Scoop.it
Complex animal behaviors are built from dynamical relationships between sensory inputs, neuronal activity, and motor outputs in patterns with strategic value. Connecting these patterns illuminates how nervous systems compute behavior. Here, we study Drosophila larva navigation up temperature gradients toward preferred temperatures (positive thermotaxis). By tracking the movements of animals responding to fixed spatial temperature gradients or random temperature fluctuations, we calculate the sensitivity and dynamics of the conversion of thermosensory inputs into motor responses. We discover three thermosensory neurons in each dorsal organ ganglion (DOG) that are required for positive thermotaxis. Random optogenetic stimulation of the DOG thermosensory neurons evokes behavioral patterns that mimic the response to temperature variations. In vivo calcium and voltage imaging reveals that the DOG thermosensory neurons exhibit activity patterns with sensitivity and dynamics matched to the behavioral response. Temporal processing of temperature variations carried out by the DOG thermosensory neurons emerges in distinct motor responses during thermotaxis.
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