id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_s672dej6nvf6xabpnrkr3ytcwu	Alexander M. Duda	Information-preserving Transforms: Two Graph Metrics for Simulated Spiking Neural Networks	2013	8	.pdf	application/pdf	3972	347	61	Information-preserving Transforms: Two Graph Metrics for Simulated Spiking Neural Networks systems have a variety of sensory inputs that provide access to the richness, complexity, and noise of real-world signals. Specifically, the systems we design and implement are ab initio simulated spiking neural networks (SSNNs) with cellular of SSNNs. We report the encouraging results of an experiment carried out in the Language Acquisition and Robotics Group. Nonlinear Dynamics; Real-World Coupling; Simulated Spiking Neural Networks; Spike-timing Dependent Plasticity; STDP Learning; Technology, we are designing computational models that enable a humanoid robot (see Fig.1) to learn natural With conductance-based synaptic dynamics, plasticity determined by a spiketiming dependent model developed by Clopath-Gerstner, and a reliability parameter (which in our experiments was Fig. 2 The left figure shows a single directed synapse going from the presynaptic neuron, A, with membrane voltage A.V to the postsynaptic The network consists of 100 excitatory HH neuron models, as detailed in Section 2.1, 1991 directed synapses using 	./cache/work_s672dej6nvf6xabpnrkr3ytcwu.pdf	./txt/work_s672dej6nvf6xabpnrkr3ytcwu.txt