Ant-inspired density estimation via random walks.

Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter rates: The higher the density, the more often the ants bump into each other. We study distributed density estimation from a theoretical perspective. We prove that a group of anonymous agents randomly walking on a grid are able to estimate their density within a small multiplicative error in few steps by measuring their rates of encounter with other agents. Despite dependencies inherent in the fact that nearby agents may collide repeatedly (and, worse, cannot recognize when this happens), our bound nearly matches what would be required to estimate density by independently sampling grid locations. From a biological perspective, our work helps shed light on how ants and other social insects can obtain relatively accurate density estimates via encounter rates. From a technical perspective, our analysis provides tools for understanding complex dependencies in the collision probabilities of multiple random walks. We bound the strength of these dependencies using local mixing properties of the underlying graph. Our results extend beyond the grid to more general graphs, and we discuss applications to size estimation for social networks, density estimation for robot swarms, and random walk-based sampling for sensor networks.

Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits.

Winner-take-all (WTA) refers to the neural operation that selects a (typically small) group of neurons from a large neuron pool. It is conjectured to underlie many of the brain's fundamental computational abilities. However, not much is known about the robustness of a spike-based WTA network to the inherent randomness of the input spike trains. In this work, we consider a spike-based k-WTA model wherein n randomly generated input spike trains compete with each other based on their underlying firing rates and k winners are supposed to be selected. We slot the time evenly with each time slot of length 1 ms and model the n input spike trains as n independent Bernoulli processes. We analytically characterize the minimum waiting time needed so that a target minimax decision accuracy (success probability) can be reached. We first derive an information-theoretic lower bound on the waiting time. We show that to guarantee a (minimax) decision error ≤δ (where δ∈(0,1)), the waiting time of any WTA circuit is at least [Formula: see text]where R⊆(0,1) is a finite set of rates and TR is a difficulty parameter of a WTA task with respect to set R for independent input spike trains. Additionally, TR is independent of δ, n, and k. We then design a simple WTA circuit whose waiting time is [Formula: see text]provided that the local memory of each output neuron is sufficiently long. It turns out that for any fixed δ, this decision time is order-optimal (i.e., it matches the above lower bound up to a multiplicative constant factor) in terms of its scaling in n, k, and TR.

Costs of task allocation with local feedback: Effects of colony size and extra workers in social insects and other multi-agent systems.

Adaptive collective systems are common in biology and beyond. Typically, such systems require a task allocation algorithm: a mechanism or rule-set by which individuals select particular roles. Here we study the performance of such task allocation mechanisms measured in terms of the time for individuals to allocate to tasks. We ask: (1) Is task allocation fundamentally difficult, and thus costly? (2) Does the performance of task allocation mechanisms depend on the number of individuals? And (3) what other parameters may affect their efficiency? We use techniques from distributed computing theory to develop a model of a social insect colony, where workers have to be allocated to a set of tasks; however, our model is generalizable to other systems. We show, first, that the ability of workers to quickly assess demand for work in tasks they are not currently engaged in crucially affects whether task allocation is quickly achieved or not. This indicates that in social insect tasks such as thermoregulation, where temperature may provide a global and near instantaneous stimulus to measure the need for cooling, for example, it should be easy to match the number of workers to the need for work. In other tasks, such as nest repair, it may be impossible for workers not directly at the work site to know that this task needs more workers. We argue that this affects whether task allocation mechanisms are under strong selection. Second, we show that colony size does not affect task allocation performance under our assumptions. This implies that when effects of colony size are found, they are not inherent in the process of task allocation itself, but due to processes not modeled here, such as higher variation in task demand for smaller colonies, benefits of specialized workers, or constant overhead costs. Third, we show that the ratio of the number of available workers to the workload crucially affects performance. Thus, workers in excess of those needed to complete all tasks improve task allocation performance. This provides a potential explanation for the phenomenon that social insect colonies commonly contain inactive workers: these may be a 'surplus' set of workers that improves colony function by speeding up optimal allocation of workers to tasks. Overall our study shows how limitations at the individual level can affect group level outcomes, and suggests new hypotheses that can be explored empirically.

Repair of horizontal meniscus tears: a systematic review.

PURPOSE: Despite the well-documented advantages of meniscal repair over meniscectomy, horizontal cleavage tears (HCTs) are often not repaired. Reported reasons include difficulty performing the repair, potential suture failure due to mechanical stresses, and poor healing rates. In addition, many surgeons have the perception that debriding the tear until the superior and inferior laminae are stable results in a good clinical outcome. Furthermore, many of the tears occur in patients who are older than the generally accepted indicated age for repair and may also have a degenerative component, making them potentially less likely to benefit from repair. This review was performed to evaluate the published outcomes of HCT repairs and test the hypothesis that surgically repaired HCTs have an unacceptably low rate of success. METHODS: A systematic search of the PubMed and Embase databases was performed in December 2013 to identify studies in which meniscal HCTs were repaired. Inclusion criteria for the analysis were English language, reference to a patient with an HCT repaired by any method, and a report of at least 1 postoperative outcome. For the purposes of this review, a failed outcome was defined as the need for reoperation. RESULTS: More than 16,000 abstracts were returned in the search. From these abstracts, we identified 210 articles for further review, of which 9 met the inclusion criteria. A total of 98 repairs of horizontal tears were evaluated in these studies. By use of reoperation as the criterion for treatment failure, 77 of the repairs were successful, for an overall success rate of 77.8%. CONCLUSIONS: The literature does not support the hypothesis that surgically repaired HCTs have an unacceptably low rate of success. Rather, our results show that existing studies of repaired HCTs show a comparable success rate to repairs of other types of meniscal tears. LEVEL OF EVIDENCE: Level IV, systematic review of Level IV studies.

Emergence of Direction-Selective Retinal Cell Types in Task-Optimized Deep Learning Models.

Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. Previous research has shown task-optimized CNNs to be capable of providing explanations as to why the retina efficiently encodes natural stimuli and how certain retinal cell types are involved in efficient encoding. In our work, we sought to use task-optimized CNNs as a means of explaining computational mechanisms responsible for motion-selective retinal circuits. We designed a biologically constrained CNN and optimized its performance on a motion-classification task. We drew inspiration from psychophysics, deep learning, and systems neuroscience literature to develop a toolbox of methods to reverse engineer the computational mechanisms learned in our model. Through reverse engineering our model, we proposed a computational mechanism in which direction-selective ganglion cells and starburst amacrine cells, both experimentally observed retinal cell types, emerge in our model to discriminate among moving stimuli. This emergence suggests that direction-selective circuits in the retina are ecologically designed to robustly discriminate among moving stimuli. Our results and methods also provide a framework for how to build more interpretable deep learning models and how to understand them.

The Power of Population Effect in Temnothorax Ant House-Hunting: A Computational Modeling Approach.

The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bioinspired design. In this study, we investigated the house-hunting algorithm used by emigrating colonies of Temnothorax ants to reach consensus on a new nest. We developed a tractable model that encodes accurate individual behavior rules, and estimated our parameter values by matching simulated behaviors with observed ones on both the individual and group levels. We then used our model to explore a potential, but yet untested, component of the ants' decision algorithm. Specifically, we examined the hypothesis that incorporating site population (the number of adult ants at each potential nest site) into individual perceptions of nest quality can improve emigration performance. Our results showed that attending to site population accelerates emigration and reduces the incidence of split decisions. This result suggests the value of testing empirically whether nest site scouts use site population in this way, in addition to the well-demonstrated quorum rule. We also used our model to make other predictions with varying degrees of empirical support, including the high cognitive capacity of colonies and their rational time investment during decision-making. In addition, we provide a versatile and easy-to-use Python simulator that can be used to explore other hypotheses or make testable predictions. It is our hope that the insights and the modeling tools can inspire further research from both the biology and computer science community.

An Upper and Lower Bound for the Convergence Time of House-Hunting in Temnothorax Ant Colonies.

We study the problem of house-hunting in ant colonies, where ants reach consensus on a new nest and relocate their colony to that nest, from a distributed computing perspective. We propose a house-hunting algorithm that is biologically inspired by Temnothorax ants. Each ant is modeled as a probabilistic agent with limited power, and there is no central control governing the ants. We show an Ω(logn) lower bound on the running time of our proposed house-hunting algorithm, where n is the number of ants. Furthermore, we show a matching upper bound of expected O(logn) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the house-hunting process, giving a perspective on how environmental factors such as nest quality or a quorum rule can affect the emigration process.

Learning hierarchically-structured concepts.

We use a recently developed synchronous Spiking Neural Network (SNN) model to study the problem of learning hierarchically-structured concepts. We introduce an abstract data model that describes simple hierarchical concepts. We define a feed-forward layered SNN model, with learning modeled using Oja's local learning rule, a well known biologically-plausible rule for adjusting synapse weights. We define what it means for such a network to recognize hierarchical concepts; our notion of recognition is robust, in that it tolerates a bounded amount of noise. Then, we present a learning algorithm by which a layered network may learn to recognize hierarchical concepts according to our robust definition. We analyze correctness and performance rigorously; the amount of time required to learn each concept, after learning all of the sub-concepts, is approximately O1ηkℓmaxlog(k)+1ɛ+blog(k), where k is the number of sub-concepts per concept, ℓmax is the maximum hierarchical depth, η is the learning rate, ɛ describes the amount of uncertainty allowed in robust recognition, and b describes the amount of weight decrease for "irrelevant" edges. An interesting feature of this algorithm is that it allows the network to learn sub-concepts in a highly interleaved manner. This algorithm assumes that the concepts are presented in a noise-free way; we also extend these results to accommodate noise in the learning process. Finally, we give a simple lower bound saying that, in order to recognize concepts with hierarchical depth two with noise-tolerance, a neural network should have at least two layers. The results in this paper represent first steps in the theoretical study of hierarchical concepts using SNNs. The cases studied here are basic, but they suggest many directions for extensions to more elaborate and realistic cases.

Obstetric maternal outcomes at Bella Coola General Hospital: 1940 to 2001.

OBJECTIVE: To describe obstetric procedures (episiotomy, forceps, vacuum extraction, caesarean section) and maternal outcomes for patients who gave birth in an isolated, rural hospital. DESIGN: A retrospective cohort study. STUDY POPULATION: Women beyond 20 weeks' gestation who gave birth between Mar. 7, 1940, and June 9, 2001, inclusive, at the Bella Coola General Hospital (BCGH). MAIN OUTCOME MEASURES: Data collected included maternal age, date of delivery, mode of delivery (vaginal delivery v. cesarean section), whether an episiotomy was performed or not, if forceps or vacuum extraction were used, whether analgesia, sedation or anesthesia was used, and maternal mortality. RESULTS: There were 2373 deliveries, including 12 sets of twins. There were no maternal mortalities. Cesarean sections were not routinely performed until the 1970s. Since then, there has been an increase in cesarean section rates to 11% of all deliveries in the 1990s. In the 1940s 28% of deliveries involved an episiotomy. This increased to 47% in the 1970s and was followed by a sharp decline to 4% in the 1990s. There was an increase, followed by a more gradual decrease in the use of forceps, and there was a recent increase in the use of vacuum extraction. The changes in procedure rates appear to reflect best practice guidelines of the times. In the case of episiotomies, the data suggest rural physicians are capable of rapid incorporation of recent recommendations. Rates for all procedures tended to be lower than those reported elsewhere in Canada and the United States. Narcotics, sedatives, inhalation agents and regional anesthetics were used to relieve the pain of labour and delivery throughout the study period. CONCLUSIONS: Women giving birth in the low technology environment of the BCGH experienced relatively low obstetric procedural rates with excellent maternal outcomes.

Creating effective advisory boards for schools of nursing.

Increasingly, a significant priority for the dean and faculty in schools of nursing is fundraising. Raising financial resources is highly competitive and requires sophisticated approaches to building relationships with individual donors, government agencies, private foundations, and corporations. Fundraising efforts need to be designed to cultivate alumni, parents, and friends as key leaders educated in the work of the school, its vision for the future, and the nursing profession. Advisory boards, with an emphasis on development, can effectively nurture such leaders who are fully versed in the strategic vision of the school and who are willing to provide financial support and access to a broad community of interest. An integrated approach that capitalizes on the expertise and knowledge of the dean, the faculty, advancement officers, and a carefully selected board chair forms the foundation of a successful model for development-focused advisory boards. Advisory board implementation is discussed from the perspective of a clearly articulated board charge, selection and recruitment, board retreat, assessment of interest and inclination through an annual board-planning process, engagement in priority project planning with the faculty, and careful cultivation toward deepened relationships and funding.

Does having cesarean section capability make a difference to a small rural maternity service?

OBJECTIVE: To determine whether having cesarean section capability in an isolated rural community makes a difference in adverse maternal or perinatal outcomes. DESIGN: Retrospective study comparing population-based obstetric outcomes of two rural remote hospitals in northwestern British Columbia. One hospital had cesarean section capability; one did not. SETTING: Bella Coola General Hospital (with cesarean section capability) in Bella Coola Valley (BCV) and Queen Charlotte Islands General Hospital (without cesarean section capability) in Queen Charlotte City (QCC). PARTICIPANTS: Women who carried pregnancies beyond 20 weeks' gestation and who gave birth between January 1, 1986, and December 31, 2000. INTERVENTIONS: British Columbia Vital Statistics Agency data was used to compare obstetric outcomes in the two communities. A chart audit of local births at BCV and QCC was done to validate the vital statistics data. MAIN OUTCOME MEASURES: Perinatal death, newborn transfer to a tertiary care facility, birth weight, gestational age at delivery, mode of delivery, and Apgar score. RESULTS: The rate of preterm deliveries in QCC was higher (relative risk 1.41, 95% confidence interval 1.00 to 1.99; P = .047) than the rate in BCV. Otherwise, there were no differences in adverse maternal or perinatal outcomes in the two populations. In BCV, 69.8% of women delivered locally compared with 50.2% of women in the southern Queen Charlotte Islands (P < .001). CONCLUSION: Having local cesarean section capability is associated with a greater proportion of local deliveries and a lower rate of preterm deliveries.