Many social animals cooperatively process information during decision making, allowing them to concentrate on the best of several options. However, positive feedback created by information sharing can also lock the group into a suboptimal outcome if option quality changes over time. This creates a trade-off between consensus and flexibility, whose resolution depends on the information-sharing mechanisms groups employ. We investigated the influence of communication behavior on decision flexibility in nest site choice by colonies of the ant Temnothorax rugatulus. These ants divide their emigration into two distinct phases separated by a quorum rule. In the first phase, scouts recruit nestmates to promising sites using the slow method of tandem running. Once a site's population surpasses a quorum, they switch to the faster method of social transport. We gave colonies a choice between two sites of different quality, and then switched site quality at different points during the emigration. Before the quorum was met, colonies were able to switch their choice to the newly superior site, but once they began to transport, their flexibility dropped significantly. Close observation of single ants revealed that transporters were more likely than tandem leaders to continue recruiting to a site even after its quality was diminished. That is, tandem leaders continued to monitor the quality of the site, while transporters instead fully committed to the site without further assessment. We discuss how this change in commitment with quorum attainment may enhance the rapid achievement of consensus needed for nest site selection, but at a cost in flexibility once the quorum is met.
In many real-world complex systems, the time-evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here, we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the co-evolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we show that in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability play a crucial role for the sustainability of the system's equilibrium state. We derive a macroscopic description of the system which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network and is applicable to many fields of study, such as epidemic spreading or social modeling.
We investigate a stochastic search process in one dimension under the competing roles of mortality, redundancy, and diversity of the searchers. This picture represents a toy model for the fertilization of an oocyte by sperm. A population of N independent and mortal diffusing searchers all start at x=L and attempt to reach the target at x=0. When mortality is irrelevant, the search time scales as τD/lnN for lnN≫1, where τD∼L2/D is the diffusive time scale. Conversely, when the mortality rate μ of the searchers is sufficiently large, the search time scales as τD/μ‾‾‾‾‾√, independent of N. When searchers have distinct and high mortalities, a subpopulation with a non-trivial optimal diffusivity are most likely to reach the target. We also discuss the effect of chemotaxis on the search time and its fluctuations.
Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.
A handful of low-budget innovators are working to reinvent rigid, heavy machines and robots with materials that are soft, light, cheap and squeezable.
In a converted pipe organ factory in the city’s Mission District, Saul Griffith works on products that are smarter, cheaper and, above all, squiggly.
Inside the cavernous building and a nearby garage occupied by Mr. Griffith’s research company Otherlab, small teams gather around laser cutters and machining tools. Some work on arrays of solar panels that follow the sun, guided by what look like ribbed soda bottles and powered by pneumatic pressure. Others fiddle with inflatable exoskeletons intended to help soldiers run far with heavy loads or to help paraplegics walk.
These are the kinds of futuristic products promised for years by conventional engineering that are now being made real by a handful of low-budget inventors with an unusual vision: They want to replace traditional brawn and metal with unconventional materials to create cheaper and more effective soft machines.
Back-propagation is the most common algorithm used to train neural networks. There are many ways that back-propagation can be implemented. This article presents a code implementation, using C#, which closely mirrors the terminology and explanation of back-propagation given in the Wikipedia entry on the topic.
You can think of a neural network as a complex mathematical function that accepts numeric inputs and generates numeric outputs. The values of the outputs are determined by the input values, the number of so-called hidden processing nodes, the hidden and output layer activation functions, and a set of weights and bias values.
You’ve likely heard that multitasking is problematic, but new studies show that it kills your performance and may even damage your brain. Research conducted at Stanford University found that multitasking is less productive than doing a single thing at a time. The researchers found that people who are regularly bombarded with several streams of electronic information cannot pay attention, recall information, or switch from one job to another as well as those who complete one task at a time.
Although price and quality play important roles in our decision to purchase a product, science suggests that the appearance of the person attempting to sell the goods also has a hand.
What goes through your head when deciding whether or not to purchase an item? It’s likely that quality and price are important factors, but what about the appearance of the model advertising the product or the salesperson attempting to sell it? Science suggests that these factors may be just as important as the product itself when it comes to the consumer’s decision to purchase.
We have a limited understanding of the factors that make people influential and topics popular in social media. Are users who comment on a variety of matters more likely to achieve high influence than those who stay focused? Do general subjects tend to be more popular than specific ones? Questions like these demand a way to detect the topics hidden behind messages associated with an individual or a keyword, and a gauge of similarity among these topics. Here we develop such an approach to identi
For infants, the first problem in learning a word is to map the word to its referent; a second problem is to remember that mapping when the word and/or referent are again encountered. Recent infant studies suggest that spatial location plays a key role in how infants solve both problems. Here we provide a new theoretical model and new empirical evidence on how the body – and its momentary posture – may be central to these processes. The present study uses a name-object mapping task in which names are either encountered in the absence of their target (experiments 1–3, 6 & 7), or when their target is present but in a location previously associated with a foil (experiments 4, 5, 8 & 9). A humanoid robot model (experiments 1–5) is used to instantiate and test the hypothesis that body-centric spatial location, and thus the bodies’ momentary posture, is used to centrally bind the multimodal features of heard names and visual objects. The robot model is shown to replicate existing infant data and then to generate novel predictions, which are tested in new infant studies (experiments 6–9). Despite spatial location being task-irrelevant in this second set of experiments, infants use body-centric spatial contingency over temporal contingency to map the name to object. Both infants and the robot remember the name-object mapping even in new spatial locations. However, the robot model shows how this memory can emerge –not from separating bodily information from the word-object mapping as proposed in previous models of the role of space in word-object mapping – but through the body’s momentary disposition in space.
We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.
Earlier this week, we went up to Boston to see something new from Rethink Robotics. They wouldn’t tell us what (not even a hint), but we bought plane tickets anyway, because Rodney Brooks told us that it wasn’t just some slightly different version of Baxter. And it wasn’t: it’s a completely different robot, stuffing all of the adaptive, collaborative technology that makes Baxter unique into a form factor that’s smaller, faster, stronger, and more precise.
Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g.current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition.
Understanding how the brain processes sensory information is often complicated by the fact that neurons exhibit trial-to-trial variability in their responses to stimuli. Indeed, the role of variability in sensory coding is still highly debated. Here, we examined how variability influences neural responses to naturalistic stimuli consisting of a fast time-varying waveform (i.e., carrier or first order) whose amplitude (i.e., envelope or second order) varies more slowly. Recordings were made from fish electrosensory and monkey vestibular sensory neurons. In both systems, we show that correlated but not single-neuron activity can provide detailed information about second-order stimulus features. Using a simple mathematical model, we made the strong prediction that such correlation-based coding of envelopes requires neural variability. Strikingly, the performance of correlated activity at predicting the envelope was similarly optimally tuned to a nonzero level of variability in both systems, thereby confirming this prediction. Finally, we show that second-order sensory information can only be decoded if one takes into account joint statistics when combining neural activities. Our results thus show that correlated but not single-neural activity can transmit information about the envelope, that such transmission requires neural variability, and that this information can be decoded. We suggest that envelope coding by correlated activity is a general feature of sensory processing that will be found across species and systems.
The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable.
Neural correlations during a cognitive task are central to study brain information processing and computation. However, they have been poorly analyzed due to the difficulty of recording simultaneous single neurons during task performance. In the present work, we quantified neural directional correlations using spike trains that were simultaneously recorded in sensory, premotor, and motor cortical areas of two monkeys during a somatosensory discrimination task. Upon modeling spike trains as binary time series, we used a nonparametric Bayesian method to estimate pairwise directional correlations between many pairs of neurons throughout different stages of the task, namely, perception, working memory, decision making, and motor report. We find that solving the task involves feedforward and feedback correlation paths linking sensory and motor areas during certain task intervals. Specifically, information is communicated by task-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor, and motor areas when decision making takes place. Crucially, when sensory comparison is no longer requested for task performance, a major proportion of directional correlations consistently vanish across all cortical areas.
More news from the AWS Summit being held in San Francisco today as AWS announces a machine learning service for its customers. The need for this is obvious: it is becoming increasingly easy for organizations to collect vast amounts of data. In doing so they can create what is known as a data lake, a vast pool of undifferentiated data that can then be analyzed.
The issue is that the tools to actually analyze data are often expensive and complex – what organizations need is a simple set of tools to do this analytics and machine learning heavy lifting.
A novel twist on the young field of optogenetics may provide a new way to study living human brains as well as offering innovative therapeutic uses.
From time immemorial, philosophers, anatomists and scientists have pondered the inner workings of the brain. Efforts to look inside the black box consistently yielded far more questions than answers. After all, the alchemists of the 16th century no more found actual homunculi residing inside our heads than the anatomists of Descartes’ day found the gears of an intricate clock.
Galvanometers and electroencephalograms (EEGs) opened the way to exploring the brain’s electrical activity, but they mostly told us how much we didn’t understand about the brain’s workings. Subsequent study revealed thousands of types of neurons intricately organized and interconnected into a vast network of roughly 100 billion cells in the average adult. Individual neurons are activated based on the outputs of thousands of upstream cells and then contribute to the activation of thousands of downstream neurons. Even with the improved spatial and temporal resolutions offered by later technologies such as fMRI and MEG, the language of the brain continued to remain a mystery.
The widespread distribution of smartphones, with their integrated sensors and communication capabilities, makes them an ideal platform for point-of-care (POC) diagnosis, especially in resource-limited settings. Molecular diagnostics, however, have been difficult to implement in smartphones. We herein report a diffraction-based approach that enables molecular and cellular diagnostics. The D3 (digital diffraction diagnosis) system uses microbeads to generate unique diffraction patterns which can be acquired by smartphones and processed by a remote server. We applied the D3 platform to screen for precancerous or cancerous cells in cervical specimens and to detect human papillomavirus (HPV) DNA. The D3 assay generated readouts within 45 min and showed excellent agreement with gold-standard pathology or HPV testing, respectively. This approach could have favorable global health applications where medical access is limited or when pathology bottlenecks challenge prompt diagnostic readouts.
The family of animal robots created by German robotics company Festo is growing. As part of its Bionic Learning Network, the company has introduced two new robots: a swarm of ants that can operate cooperatively, and a butterfly robot that leverages the insect's lightness.
The ant robots -- called BionicANTS -- are not just inspired by the insect's physical body, but by its swarm intelligence.
Inspiration for artificial biologically inspired computing is often drawn from neural systems. This article shows how to analyze neural systems using information theory with the aim of obtaining constraints that help to identify the algorithms run by neural systems and the information they represent. Algorithms and representations identified this way may then guide the design of biologically inspired computing systems. The material covered includes the necessary introduction to information theory and to the estimation of information-theoretic quantities from neural recordings. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is partitioned into component processes of information storage, transfer, and modification – locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems.
A team of researchers has found that posture is critical in the early stages of acquiring new knowledge. Using both robots and infants, researchers examined the role bodily position played in the brain's ability to "map" names to objects. They found that consistency of the body's posture and spatial relationship to an object as an object's name was shown and spoken aloud were critical to successfully connecting the name to the object. The study offers a new approach to studying the way "objects of cognition," such as words or memories of physical objects, are tied to the position of the body. The new insights stem from the field of epigenetic robotics, in which researchers are working to create robots that learn and develop like children, through interaction with their environment.
Alex Endert's dissertation "Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering" described semantic interaction, a user interaction methodology for visual analytics (VA). It showed that user interaction embodies users' analytic process and can thus be mapped to model-steering functionality for "human-in-the-loop" system design. The dissertation contributed a framework (or pipeline) that describes such a process, a prototype VA system to test semantic interaction, and a user evaluation to demonstrate semantic interaction's impact on the analytic process. This research is influencing current VA research and has implications for future VA research.
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