Simulation of Rat Behavior in a Light-Dark Box via Neuroevolution
ResumoThe light-dark box is a widely used test for the investigation of animal behavior commonly used to identify and study anxious-like behavioral patterns in rodents. We propose a neuroevolution model for virtual rats in a simulated light-dark box. The virtual rat is controlled by an artificial neural network (ANN) optimized by a genetic algorithm (GA). The fitness function is given by a weighed sum of two terms (punishment and reward). By changing the weight of the punishment term, we are able to simulate the effects of anxiolytic/anxiogenic drugs on rats. We also propose using GAs to optimize the number of the ANN hidden neurons and sensors for the virtual rat. According to the experiments, the best results are obtained by ANNs combining both luminosity and wall sensors.
Barré-Sinoussi, F. and Montagutelli, X. (2015). Animal models are essential to biological research: issues and perspectives. Future science OA, 1(4).
Campos-Cardoso, R., Silva, C. P., Carolino, R. O., Anselmo-Franci, J. A., Tirapelli, C. R., and Padovan, C. M. (2021). Imipramine attenuates anxiety-and depressive-like effects of acute and prolonged ethanol-abstinence in male rats by modulating sert and gr expression in the dorsal hippocampus. Behavioural Brain Research, 408:113295.
Costa, A. A., Roque, A. C., Morato, S., and Tinós, R. (2012). A model based on genetic algorithm for investigation of the behavior of rats in the elevated plus-maze. In International Conference on Intelligent Data Engineering and Automated Learning, pages 151-158. Springer.
Costa, A. A. and Tinos, R. (2014). An evolving artificial neural network for the investigation of rat exploratory behavior. In 2014 Brazilian Conference on Intelligent Systems, pages 103-108. IEEE.
Costa, A. A. and Tinos, R. (2016). Investigation of rat exploratory behavior via evolving artificial neural networks. Journal of neuroscience methods, 270:102-110.
Costa, A. A., Vargas, P., and Tinós, R. (2013). Using explicit averaging fitness for studying the behaviour of rats in a maze. In ECAL 2013: The Twelfth European Conference on Artificial Life, pages 940-946. MIT Press.
Crawley, J. and Goodwin, F. K. (1980). Preliminary report of a simple animal behavior model for the anxiolytic effects of benzodiazepines. Pharmacology Biochemistry and Behavior, 13(2):167-170.
Drai, D. and Golani, I. (2001). See: a tool for the visualization and analysis of rodent exploratory behavior. Neuroscience & Biobehavioral Reviews, 25(5):409-426.
Floreano, D., Dürr, P., and Mattiussi, C. (2008). Neuroevolution: from architectures to learning. Evolutionary intelligence, 1(1):47-62.
Flossmann, T. and Rochefort, N. L. (2021). Spatial navigation signals in rodent visual cortex. Current opinion in neurobiology, 67:163-173.
Giddings, J. M. (2002). Modeling the behavior of rats in an elevated plus-maze. PhD thesis, Acadia University.
Gillespie, D., Yap, M. H., Hewitt, B. M., Driscoll, H., Simanaviciute, U., Hodson-Tole, E. F., and Grant, R. A. (2019). Description and validation of the locowhisk system: Quantifying rodent exploratory, sensory and motor behaviours. Journal of Neuroscience Methods, 328:108440.
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, USA, 2nd edition.
Krynitsky, J., Legaria, A. A., Pai, J. J., Garmendia-Cedillos, M., Salem, G., Pohida, T., and Kravitz, A. V. (2020). Rodent arena tracker (rat): A machine vision rodent tracking camera and closed loop control system. Eneuro, 7(3).
Miranda, D., Conde, C., Celis, C., and Corzo, S. (2009). Modelado del comportamiento de ratas en laberinto en cruz elevado basado en redes neuronales artificiales. Revista Colombiana de Física, 41(2):406.
Nandi, A., Virmani, G., Barve, A., and Marathe, S. (2021). Dbscorer: An open-source software for automated accurate analysis of rodent behavior in forced swim test and tail suspension test. Eneuro, 8(6).
Organization, W. H. (2017). Depression and other common mental disorders: global health estimates. Technical report, World Health Organization.
Raineri, L. T., Del Lama, R. S., Candido, R. M., Costa, A. A., and Tinós, R. (2019). Robôs evolutivos para a investigação do comportamento de ratos no teste de campo aberto. In Anais do XXXVIII Concurso de Trabalhos de Iniciação Científica da SBC. SBC.
Russell, W. M. S. and Burch, R. L. (1959). The principles of humane experimental technique. Methuen.
Salum, C., Morato, S., and Roque-da Silva, A. C. (2000). Anxiety-like behavior in rats: a computational model. Neural Networks, 13(1):21-29.
Shimo, H. K., Roque, A. C., Tinós, R., Tejada, J., and Morato, S. (2010). Use of evolutionary robots as an auxiliary tool for developing behavioral models of rats in an elevated plus-maze. In 2010 Eleventh Brazilian Symposium on Neural Networks, pages 217-222. IEEE.
Tarrega, J. P. M. (2019). Simulação do comportamento de ratos virtuais controlados por uma rede neural artificial no teste da caixa claro-escuro.
Tejada, J., Bosco, G. G., Morato, S., and Roque, A. C. (2010). Characterization of the rat exploratory behavior in the elevated plus-maze with markov chains. Journal of neuroscience methods, 193(2):288-295.
van der Staay, F. J., Arndt, S. S., and Nordquist, R. E. (2009). Evaluation of animal models of neurobehavioral disorders. Behavioral and Brain Functions, 5(1):1-23.