TY - CONF T1 - Evolution of Bistable Dynamics in Spiking Neural Controllers for Agents Performing Olfactory Attraction and Aversion T2 - Proc. 19th Annual Computational Neuroscience Meeting (CNS*2010) Y1 - 2010 A1 - Oros, Nicolas A1 - Volker Steuber A1 - Davey, Neil A1 - Lola Cañamero A1 - Roderick G Adams JF - Proc. 19th Annual Computational Neuroscience Meeting (CNS*2010) PB - BioMed Central Ltd. CY - San Antonio, TX VL - 11(Suppl 1) UR - http://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-11-S1-P92 ER - TY - CONF T1 - Evolution of Bilateral Symmetry in Agents Controlled by Spiking Neural Networks T2 - Proc. 2009 IEEE Symposium on Artificial Life (ALIFE 2009) Y1 - 2009 A1 - Oros, Nicolas A1 - Volker Steuber A1 - Davey, Neil A1 - Lola Cañamero A1 - Roderick G Adams AB - We present in this paper three novel developmental models allowing information to be encoded in space and time, using spiking neurons placed on a 2D substrate. In two of these models, we introduce neural development that can use bilateral symmetry. We show that these models can create neural controllers for agents evolved to perform chemotaxis. Neural bilateral symmetry can be evolved and be beneficial for an agent. This work is the first, as far as we know, to present developmental models where spiking neurons are generated in space and where bilateral symmetry can be evolved and proved to be beneficial in this context. JF - Proc. 2009 IEEE Symposium on Artificial Life (ALIFE 2009) PB - IEEE Press CY - Nashville, TN SN - 978-1-4244-2763-5 UR - http://ieeexplore.ieee.org/document/4937702/ ER - TY - CONF T1 - Adaptive Olfactory Encoding in Agents Controlled by Spiking Neural Networks T2 - From Animals to Animats 10: Proc. 10th International Conference on Simulation of Adaptive Behavior (SAB 2008) Y1 - 2008 A1 - Oros, Nicolas A1 - Volker Steuber A1 - Davey, Neil A1 - Lola Cañamero A1 - Roderick G Adams ED - Asada, Minoru ED - Hallam, John C T ED - Jean-Arcady Meyer ED - Tani, Jun AB - We created a neural architecture that can use two different types of information encoding strategies depending on the environment. The goal of this research was to create a simulated agent that could react to two different overlapping chemicals having varying concentrations. The neural network controls the agent by encoding its sensory information as temporal coincidences in a low concentration environment, and as firing rates at high concentration. With such an architecture, we could study synchronization of firing in a simple manner and see its effect on the agent’s behaviour. JF - From Animals to Animats 10: Proc. 10th International Conference on Simulation of Adaptive Behavior (SAB 2008) T3 - Lecture Notes in Computer Science (LNCS) PB - Springer, Berlin, Heidelberg CY - Osaka, Japan VL - 5040 SN - 978-3-540-69134-1 UR - http://link.springer.com/chapter/10.1007/978-3-540-69134-1_15 ER - TY - CONF T1 - Optimal Noise in Spiking Neural Networks for the Detection of Chemicals by Simulated Agents T2 - Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems Y1 - 2008 A1 - Oros, Nicolas A1 - Volker Steuber A1 - Davey, Neil A1 - Lola Cañamero A1 - Roderick G Adams ED - Seth Bullock ED - Jason Noble ED - Richard A. Watson ED - Mark A Bedau AB - We created a spiking neural controller for an agent that could use two different types of information encoding strategies depending on the level of chemical concentration present in the environment. The first goal of this research was to create a simulated agent that could react and stay within a region where there were two different overlapping chemicals having uniform concentrations. The agent was controlled by a spiking neural network that encoded sensory information using temporal coincidence of incoming spikes when the level of chemical concentration was low, and as firing rates at high level of concentration. With this architecture, we could study synchronization of firing in a simple manner and see its effect on the agent’s behaviour. The next experiment we did was to use a more realistic model by having an environment composed of concentration gradients and by adding input current noise to all neurons. We used a realistic model of diffusive noise and showed that it could improve the agent’s behaviour if used within a certain range. Therefore, an agent with neuronal noise was better able to stay within the chemical concentration than an agent without. JF - Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems PB - MIT Press CY - Winchester, UK SN - 978-0-262-75017-2 UR - https://mitpress-request.mit.edu/sites/default/files/titles/alife/0262287196chap58.pdf ER - TY - CONF T1 - Optimal Receptor Response Functions for the Detection of Pheromones by Agents Driven by Spiking Neural Networks T2 - Proc. 9th European Meeting on Cybernetics and Systems Research, Vol. II Y1 - 2008 A1 - Oros, Nicolas A1 - Volker Steuber A1 - Davey, Neil A1 - Lola Cañamero A1 - Roderick G Adams ED - Trappl, R AB - The goal of the work presented here is to find a model of a spiking sensory neuron that could cope with small variations in the concentration of simulated chemicals and also the whole range of concentrations. By using a biologically plausible sigmoid function in our model to map chemical concentration to current, we could produce agents able to detect the whole range of concentration of chemicals (pheromones) present in the environment as well as small variations of them. The sensory neurons used in our model are able to encode the stimulus intensity into appropriate firing rates. JF - Proc. 9th European Meeting on Cybernetics and Systems Research, Vol. II PB - Austrian Society for Cybernetic Studies CY - Vienna, Austria SN - 978-3-85206-175-7 UR - http://www.cogsci.uci.edu/~noros/mypapers/OROS_2008_EMCSR.pdf ER -