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Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that
can capture precise sequences of spikes directly in the neural simulation. In
this scheme, each neuron is assigned a randomized code that is used to tag
the post-synaptic neurons whenever a spike is transmitted. This creates a
polychronous code that preserves the order of pre-synaptic activity and can
be registered in a hash table when the post-synaptic neuron spikes. A polychronous
code is a sub-component of a polychronous group that will occur,
along with others, when the group is active. We demonstrate the representational
and pattern recognition ability of polychronous codes on a direction
selective visual task involving moving bars that is typical of a computation
performed by simple cells in the cortex. By avoiding the structural and temporal
analyses of polychronous group detection methods, the computational
efficiency of the proposed algorithm is improved for pattern recognition by
almost four orders of magnitude and is well suited for online detection
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