DYNAMIC FUZZY CLUSTERING SPIKING NEURAL NETWORK
Tofara Moyo
We describe a new type of spiking neural network where
nodes exist in some sufficiently high dimensional space that
any node can theoretically be within a certain radius r of any
other node. this radius forms a hyper sphere around each
node and a node may have any number of other nodes within
its radius. If a particular node Z is known to have activated the
algorithm selects only those nodes within the functional radius
as candidates to fire next. The closer a node is to the node
that activated the greater its probability to fire next. If a node
Y, within Z’s functional radius, has a probability of firing next greater than a threshold value
it will fire and if that node is within the radius of another node
X as well that had also previously fired along with Z, then both
probabilities it has in Z and X are summed to determine
whether it should fire next or not. To break the symmetry of
connections we create two instances for each node. A post
and pre mode, in the pre mode it exists as a centroid while in
the post mode it exists within the radius of a centroid . the
algorithm performs a variant of the Fuzzy c means clustering
algorithm in order to affect each nodes probability of firing next
from a particular centroid’s perspective and coordinates this
across the entire network. We will ultimately train the network
to represent a “way of thinking” that it determines by
connecting nodes added to the system that do not represent
input variables or output variables. But mediate between them.