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We study the problem of househunting 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 househunting 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 O( log n) lower bound on the running time of our proposed househunting algorithm, where n is the number of ants. Furthermore, we show a matching upper bound of expected O( log n) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the househunting process, giving a perspective on how environmental factors such as nest quality or a quorum rule can affect the emigration process.more » « less

The Power of Population Effect in Temnothorax Ants HouseHunting: 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 househunting 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 decisionmaking. Additionally, we provide a versatile and easytouse 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.more » « less

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 taskoptimized 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 taskoptimized CNNs as a means of explaining computational mechanisms responsible for motionselective retinal circuits. We designed a biologically constrained CNN and optimized its performance on a motionclassification 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 directionselective 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 directionselective 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.more » « less

We use a recently developed synchronous Spiking Neural Network (SNN) model to study the problem of learning hierarchicallystructured concepts. We introduce an abstract data model that describes simple hierarchical concepts. We define a feedforward layered SNN model, with learning modeled using Oja’s local learning rule, a well known biologicallyplausible 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 subconcepts, is approximately O ( 1ηk(`max log(k) + 1ε) + b log(k)), where k is the number of subconcepts per concept, `max is the maximum hierarchical depth, η is the learning rate, ε 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 subconcepts in a highly interleaved manner. This algorithm assumes that the concepts are presented in a noisefree 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 noisetolerance, 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.more » « less

We investigate the importance of quorum sensing in the success of househunting of emigrating Temnothorax ant colonies. Specifically, we show that the absence of the quorum sensing mechanism leads to failure of consensus during emigrations. We tackle this problem through the lens of distributed computing by viewing it as a natural distributed consensus algorithm. We develop an agentbased model of the househunting process, and use mathematical tools such as conditional probability, concentration bounds and Markov mixing time to rigorously prove the negative impact of not employing the quorum sensing mechanism on emigration outcomes. Our main result is a high probability bound for failure of consensus without quorum sensing in a twonewnest environment, which we further extend to the general multiplenewnest environments. We also show preliminary evidence that appropriate quorum sizes indeed help with consensus during emigrations. Our work provides theoretical foundations to analyze why Temnothorax ants evolved to utilize the quorum rule in their househunting process.more » « less

Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via taskoptimization. This functionality has not been observed in CNN models of retinal circuits via taskoptimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motiondetection retinal circuit and optimizing it to solve a motionclassification task. The learned weights and parameters indicated that the CNN converged to directionsensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that taskoptimization is a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models.more » « less

The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bioinspired design. The understanding of these systems and their application can benefit from modeling and analysis of the underlying algorithms. In this study, we define a modeling framework that can be used to formally represent all components of such algorithms. As an example application of the framework, we adapt to it the muchstudied househunting algorithm used by emigrating colonies of Temnothorax ants to reach consensus on a new nest. We provide a Python simulator that encodes accurate individual behavior rules and produces simulated behaviors consistent with empirical observations, on both the individual and group levels. Critically, through multiple simulated experiments, our results highlight the value of individual sensitivity to site population in ensuring consensus. With the help of this social information, our model successfully reproduces experimental results showing the high cognitive capacity of colonies and their rational time investment during decisionmaking, and also predicts the pros and cons of social information with regard to the colonies’ ability to avoid and repair splits. Additionally, we use the model to make new predictions about several unstudied aspects of emigration behavior. Our results indicate a more complex relationship between individual behavior and the speed/accuracy tradeoff than previously appreciated. The model proved relatively weak at resolving colony divisions among multiple sites, suggesting either limits to the ants’ ability to reach consensus, or an aspect of their behavior not captured in our model.more » « less

We study the problem of househunting 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 househunting algorithm that is biologically inspired by Temnothorax ants. Each ant is modelled as a probabilistic agent with limited power, and there is no central control governing the ants. We show a (log n) lower bound on the running time of our proposed househunting algorithm, where n is the number of ants. Further, we show a matching upper bound of expected O(log n) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the househunting process, giving a perspective on how environmental factors such as nest qualities or a quorum rule can affect the emigration process. In particular, we find that a quorum threshold that is high enough causes transports to the inferior nest to cease to happen after O(log n) rounds when there are two nests in the environment.more » « less

Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via taskoptimization. This functionality has not been observed in CNN models of retinal circuits via taskoptimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motiondetection retinal circuit and optimizing it to solve a motionclassification task. The learned weights and parameters indicated that the CNN converged to directionsensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that taskoptimization is a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models.more » « less

We study the problem of househunting 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 househunting algorithm that is biologically inspired by Temnothorax ants. Each ant is modelled as a probabilistic agent with limited power, and there is no central control governing the ants. We show a Ω(log n) lower bound on the running time of our proposed househunting algorithm, where n is the number of ants. Further, we show a matching upper bound of expected O(log n) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the househunting process, giving a perspective on how environmental factors such as nest qualities or a quorum rule can affect the emigration process. In particular, we find that a quorum threshold that is high enough causes transports to the inferior nest to cease to happen after O(log n) rounds when there are two nests in the environment.more » « less