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Distributed algorithms from arboreal ants for the shortest path problem. Proc Natl Acad Sci U S A 2023; 120:e2207959120. [PMID: 36716366 PMCID: PMC9963535 DOI: 10.1073/pnas.2207959120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Colonies of the arboreal turtle ant create networks of trails that link nests and food sources on the graph formed by branches and vines in the canopy of the tropical forest. Ants put down a volatile pheromone on the edges as they traverse them. At each vertex, the next edge to traverse is chosen using a decision rule based on the current pheromone level. There is a bidirectional flow of ants around the network. In a previous field study, it was observed that the trail networks approximately minimize the number of vertices, thus solving a variant of the popular shortest path problem without any central control and with minimal computational resources. We propose a biologically plausible model, based on a variant of the reinforced random walk on a graph, which explains this observation and suggests surprising algorithms for the shortest path problem and its variants. Through simulations and analysis, we show that when the rate of flow of ants does not change, the dynamics converges to the path with the minimum number of vertices, as observed in the field. The dynamics converges to the shortest path when the rate of flow increases with time, so the colony can solve the shortest path problem merely by increasing the flow rate. We also show that to guarantee convergence to the shortest path, bidirectional flow and a decision rule dividing the flow in proportion to the pheromone level are necessary, but convergence to approximately short paths is possible with other decision rules.
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Lai H, Zhang L, Zhang S. Improving Network Training on Resource-Constrained Devices via Habituation Normalization. SENSORS (BASEL, SWITZERLAND) 2022; 22:9940. [PMID: 36560310 PMCID: PMC9783687 DOI: 10.3390/s22249940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/25/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
As a technique for accelerating and stabilizing training, the batch normalization (BN) is widely used in deep learning. However, BN cannot effectively estimate the mean and the variance of samples when training/fine-tuning with small batches of data on resource-constrained devices. It will lead to a decrease in the accuracy of the deep learning model. In the fruit fly olfactory system, the algorithm based on the "negative image" habituation model can filter redundant information and improve numerical stability. Inspired by the circuit mechanism, we propose a novel normalization method, the habituation normalization (HN). HN first eliminates the "negative image" obtained by habituation and then calculates the statistics for normalizing. It solves the problem of accuracy degradation of BN when the batch size is small. The experiment results show that HN can speed up neural network training and improve the model accuracy on vanilla LeNet-5, VGG16, and ResNet-50 in the Fashion MNIST and CIFAR10 datasets. Compared with four standard normalization methods, HN keeps stable and high accuracy in different batch sizes, which shows that HN has strong robustness. Finally, the applying HN to the deep learning-based EEG signal application system indicates that HN is suitable for the network fine-tuning and neural network applications under limited computing power and memory.
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Affiliation(s)
- Huixia Lai
- The College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350007, China
| | - Lulu Zhang
- The College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350007, China
| | - Shi Zhang
- The College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350007, China
- The Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou 350007, China
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Blok LER, Boon M, van Reijmersdal B, Höffler KD, Fenckova M, Schenck A. Genetics, molecular control and clinical relevance of habituation learning. Neurosci Biobehav Rev 2022; 143:104883. [PMID: 36152842 DOI: 10.1016/j.neubiorev.2022.104883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/08/2022] [Accepted: 08/30/2022] [Indexed: 11/29/2022]
Abstract
Habituation is the most fundamental form of learning. As a firewall that protects our brain from sensory overload, it is indispensable for cognitive processes. Studies in humans and animal models provide increasing evidence that habituation is affected in autism and related monogenic neurodevelopmental disorders (NDDs). An integrated application of habituation assessment in NDDs and their animal models has unexploited potential for neuroscience and medical care. With the aim to gain mechanistic insights, we systematically retrieved genes that have been demonstrated in the literature to underlie habituation. We identified 258 evolutionarily conserved genes across species, describe the biological processes they converge on, and highlight regulatory pathways and drugs that may alleviate habituation deficits. We also summarize current habituation paradigms and extract the most decisive arguments that support the crucial role of habituation for cognition in health and disease. We conclude that habituation is a conserved, quantitative, cognition- and disease-relevant process that can connect preclinical and clinical work, and hence is a powerful tool to advance research, diagnostics, and treatment of NDDs.
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Affiliation(s)
- Laura Elisabeth Rosalie Blok
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA, Nijmegen, the Netherlands.
| | - Marina Boon
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA, Nijmegen, the Netherlands.
| | - Boyd van Reijmersdal
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA, Nijmegen, the Netherlands.
| | - Kira Daniela Höffler
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA, Nijmegen, the Netherlands.
| | - Michaela Fenckova
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA, Nijmegen, the Netherlands; Department of Molecular Biology and Genetics, Faculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 31, 37005, Ceske Budejovice, Czech Republic.
| | - Annette Schenck
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA, Nijmegen, the Netherlands.
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Reply to Semelidou and Skoulakis: "Short-term" habituation has multiple distinct mechanisms. Proc Natl Acad Sci U S A 2020; 117:20373-20374. [PMID: 32843562 DOI: 10.1073/pnas.2013768117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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