  This paper proposes a novel Decentralized Spike-based Learning (DSL)
framework for the discrete Perimeter Defense Problem (d-PDP). A team of
defenders is operating on the perimeter to protect the circular territory from
radially incoming intruders. At first, the d-PDP is formulated as a
spatio-temporal multi-task assignment problem (STMTA). The problem of STMTA is
then converted into a multi-label learning problem to obtain labels of segments
that defenders have to visit in order to protect the perimeter. The DSL
framework uses a Multi-Label Classifier using Synaptic Efficacy Function
spiking neuRON (MLC-SEFRON) network for deterministic multi-label learning.
Each defender contains a single MLC-SEFRON network. Each MLC-SEFRON network is
trained independently using input from its own perspective for decentralized
operations. The input spikes to the MLC-SEFRON network can be directly obtained
from the spatio-temporal information of defenders and intruders without any
extra pre-processing step. The output of MLC-SEFRON contains the labels of
segments that a defender has to visit in order to protect the perimeter. Based
on the multi-label output from the MLC-SEFRON a trajectory is generated for a
defender using a Consensus-Based Bundle Algorithm (CBBA) in order to capture
the intruders. The target multi-label output for training MLC-SEFRON is
obtained from an expert policy. Also, the MLC-SEFRON trained for a defender can
be directly used for obtaining labels of segments assigned to another defender
without any retraining. The performance of MLC-SEFRON has been evaluated for
full observation and partial observation scenarios of the defender. The overall
performance of the DSL framework is then compared with expert policy along with
other existing learning algorithms. The scalability of the DSL has been
evaluated using an increasing number of defenders.
