Temporal clustering with spiking neurons and dynamic synapses: towards technological applications.
We apply spiking neurons with dynamic synapses to detect temporal patterns in a multi-dimensional signal. We use a network of integrate-and-fire neurons, fully connected via dynamic synapses, each of which is given by a biologically plausible dynamical model based on the exact pre- and post-synaptic spike timing. Dependent on their adaptable configuration (learning) the synapses automatically implement specific delays. Hence, each output neuron with its set of incoming synapses works as a detector for a specific temporal pattern. The whole network functions as a temporal clustering mechanism with one output per input cluster. The classification capability is demonstrated by illustrative examples including patterns from Poisson processes and the analysis of speech data.
Storck
J
J
Siemens AG, Corporate Technology, Munich, Germany.
Jäkel
F
F
Deco
G
G
D016000:Cluster Analysis; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology*