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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 numbermore »Free, publiclyaccessible full text available January 1, 2023

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.more »Free, publiclyaccessible full text available January 1, 2023

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 environmentsmore »Free, publiclyaccessible full text available January 1, 2023

This article considers resilient cooperative state estimation in unreliable multiagent networks. A network of agents aim to collaboratively estimate the value of an unknown vector parameter, while an unknown subset of agents suffer Byzantine faults. We refer to the faulty agents as Byzantine agents. Byzantine agents malfunction arbitrarily and may send out highly unstructured messages to other agents in the network. As opposed to faultfree networks, reaching agreement in the presence of Byzantine agents is far from trivial. In this article, we propose a computationally efficient algorithm that is provably robust to Byzantine agents. At each iteration of the algorithm,more »

We consider the problem of multiagent optimization wherein an unknown subset of agents suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a local cost function fi , and the overarching goal of the good agents is to collaboratively minimize a global objective that properly aggregates these local cost functions. To the best of our knowledge, we are among the first to study Byzantineresilient optimization where no central coordinating agent exists, and we are the first to characterize the structures of the convex coefficients of the achievable global objectives. Dealing with Byzantine faults is verymore »

Abstract—READ transactions that read data distributed across servers dominate the workloads of realworld distributed storage systems. The SNOW Theorem [13] stated that ideal READ transactions that have optimal latency and the strongest guarantees—i.e., “SNOW” READ transactions—are impossible in one specific setting that requires three or more clients: at least two readers and one writer. However, it left many open questions.We close all of these open questions with new impossibility results and new algorithms. First, we prove rigorously the result from [13] saying that it is impossible to have a READ transactions system that satisfies SNOW properties with three or moremore »

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 simulatedmore »

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 accordingmore »

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 onlymore »

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 ismore »