Eidetic Wolf Search Algorithm with a global memory structure

Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences - a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.

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Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration

10.1101/2021.01.14.426746 ◽

2021 ◽

Author(s):

Matthew Rosenberg ◽

Tony Zhang ◽

Pietro Perona ◽

Markus Meister

Keyword(s):

Search Algorithm ◽

Forced Choice ◽

Learning Rate ◽

Global Memory ◽

Complex Tasks ◽

Rapid Learning ◽

Efficient Exploration

AbstractAnimals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal quickly discovers the location of a reward in the maze and executes correct 10-bit choices after only 10 reward experiences – a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.

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Forced-choice associative recognition: Implications for global-memory models.

Journal of Experimental Psychology Learning Memory and Cognition ◽

10.1037/0278-7393.19.4.871 ◽

1993 ◽

Vol 19 (4) ◽

pp. 871-881 ◽

Cited By ~ 23

Author(s):

Steven E. Clark ◽

Alden Hori ◽

Daniel E. Callan

Keyword(s):

Forced Choice ◽

Memory Models ◽

Associative Recognition ◽

Global Memory

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Fast search algorithm for VQ-based recognition of isolated words

IEE Proceedings I Communications Speech and Vision ◽

10.1049/ip-i-2.1989.0059 ◽

1989 ◽

Vol 136 (6) ◽

pp. 391 ◽

Cited By ~ 15

Author(s):

S.H. Chen ◽

J.S. Pan

Keyword(s):

Search Algorithm ◽

Fast Search ◽

Fast Search Algorithm

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Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189134 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8125-8137

Author(s):

Jackson J Christy ◽

D Rekha ◽

V Vijayakumar ◽

Glaucio H.S. Carvalho

Keyword(s):

Intelligent Transportation System ◽

Search Algorithm ◽

Mac Protocol ◽

High Mobility ◽

Optimal Solution ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Traffic Density ◽

Scheduling Problem ◽

Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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