Neural Processing of Counting in Evolved Spiking and McCulloch-Pitts Agents Export

@article{citeulike:448687, abstract = {This article investigates the evolution of autonomous agents that perform a memory-dependent counting task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiking integrate-and-fire networks. The results demonstrate the superiority of the spiky model in evolutionary success and network simplicity. The combination of spiking dynamics with incremental evolution leads to the successful evolution of agents counting over very long periods. Analysis of the evolved networks unravels the counting mechanism and demonstrates how the spiking dynamics are utilized. Using new measures of spikiness we find that even in agents with spiking dynamics, these are usually truly utilized only when they are really needed, that is, in the evolved subnetwork responsible for counting.}, author = {Saggie-Wexler, Keren and Keinan, Alon and Ruppin, Eytan}, citeulike-article-id = {448687}, citeulike-linkout-0 = {http://dx.doi.org/10.1162/106454606775186428}, citeulike-linkout-1 = {http://www.ingentaconnect.com/content/mitpress/ali/2006/00000012/00000001/art00001}, doi = {10.1162/106454606775186428}, issn = {1064-5462}, journal = {Artificial Life}, keywords = {memory, neural\_control, spiking\_neurons}, number = {1}, pages = {1--16}, posted-at = {2006-01-05 20:39:05}, priority = {4}, publisher = {MIT Press}, title = {Neural Processing of Counting in Evolved Spiking and McCulloch-Pitts Agents}, url = {http://dx.doi.org/10.1162/106454606775186428}, volume = {12}, year = {2006} }

TY - JOUR ID - citeulike:448687 L3 - citeulike-article-id:448687 N2 - This article investigates the evolution of autonomous agents that perform a memory-dependent counting task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiking integrate-and-fire networks. The results demonstrate the superiority of the spiky model in evolutionary success and network simplicity. The combination of spiking dynamics with incremental evolution leads to the successful evolution of agents counting over very long periods. Analysis of the evolved networks unravels the counting mechanism and demonstrates how the spiking dynamics are utilized. Using new measures of spikiness we find that even in agents with spiking dynamics, these are usually truly utilized only when they are really needed, that is, in the evolved subnetwork responsible for counting. IS - 1 JF - Artificial Life SN - 1064-5462 EP - 16 TI - Neural Processing of Counting in Evolved Spiking and McCulloch-Pitts Agents PB - MIT Press VL - 12 SP - 1 KW - memory KW - neural_control KW - spiking_neurons AU - Saggie-Wexler, Keren AU - Keinan, Alon AU - Ruppin, Eytan PY - 2006/// UR - http://dx.doi.org/10.1162/106454606775186428 ER -