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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

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 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 probabilitymore »

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 onlymore »

Oja’s rule [Oja, Journal of mathematical biology 1982] is a wellknown biologicallyplausible algorithm using a Hebbiantype synaptic update rule to solve streaming principal component analysis (PCA). Computational neuroscientists have known that this biological version of Oja’s rule converges to the top eigenvector of the covariance matrix of the input in the limit. However, prior to this work, it was open to prove any convergence rate guarantee. In this work, we give the first convergence rate analysis for the biological version of Oja’s rule in solving streaming PCA. Moreover, our convergence rate matches the information theoretical lower bound up to logarithmicmore »

We present a distributed Bayesian algorithm for robot swarms to classify a spatially distributed feature of an environment. This type of “go/nogo” decision appears in applications where a group of robots must collectively choose whether to take action, such as determining if a farm field should be treated for pests. Previous bioinspired approaches to decentralized decisionmaking in robotics lack a statistical foundation, while decentralized Bayesian algorithms typically require a strongly connected network of robots. In contrast,our algorithm allows simple, sparsely distributed robots to quickly reach accurate decisions about a binary feature of their environment. We investigate the speed vs. accuracymore »