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Evolution of Spiking Neural Networks for Temporal Pattern Recognition and Animat Control

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Figure 2.2: A schematic diagram for a spike generated by a neuron. When the membrane potential of a neuron reaches the threshold (1), its ion channels open leading for membrane depolarization (2)
Figure 2.3: An example of a synapse between two neurons. The transfer of the spike from the axon of the presynaptic neuron (upper part) to the dendrite of the postsynaptic neuron (lower part) includes transmission of neurotransmitters to the posysynaptic
Figure 2.10: The structure of Natschl¨ ager and Ruf network. Each input neuron u i
Figure 2.11: The agent model used by Oros and colleagues. Two long antennae (black) were connected to sensors (yellow) in order to detect the chemicals and two wheels controlled by 4 neurons 2 of them were responsible for forward movement (green) and the o
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Temporal Pattern Recognition with Spiking Neural Networks Mapping the GRN to the SNN Double-shifting task Animat Simulation The strength of food smell at sensors to synaptic conductance Using the sliding window thrust Encoding the sensory information at sensors as current injection Using two sound source types Initial communication protocol with the small SpiNNaker board

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