88 research outputs found
Competition through selective inhibitory synchrony
Models of cortical neuronal circuits commonly depend on inhibitory feedback
to control gain, provide signal normalization, and to selectively amplify
signals using winner-take-all (WTA) dynamics. Such models generally assume that
excitatory and inhibitory neurons are able to interact easily, because their
axons and dendrites are co-localized in the same small volume. However,
quantitative neuroanatomical studies of the dimensions of axonal and dendritic
trees of neurons in the neocortex show that this co-localization assumption is
not valid. In this paper we describe a simple modification to the WTA circuit
design that permits the effects of distributed inhibitory neurons to be coupled
through synchronization, and so allows a single WTA to be distributed widely in
cortical space, well beyond the arborization of any single inhibitory neuron,
and even across different cortical areas. We prove by non-linear contraction
analysis, and demonstrate by simulation that distributed WTA sub-systems
combined by such inhibitory synchrony are inherently stable. We show
analytically that synchronization is substantially faster than winner
selection. This circuit mechanism allows networks of independent WTAs to fully
or partially compete with each other.Comment: in press at Neural computation; 4 figure
An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks
In this article, we propose a novel Winner-Take-All (WTA) architecture
employing neurons with nonlinear dendrites and an online unsupervised
structural plasticity rule for training it. Further, to aid hardware
implementations, our network employs only binary synapses. The proposed
learning rule is inspired by spike time dependent plasticity (STDP) but differs
for each dendrite based on its activation level. It trains the WTA network
through formation and elimination of connections between inputs and synapses.
To demonstrate the performance of the proposed network and learning rule, we
employ it to solve two, four and six class classification of random Poisson
spike time inputs. The results indicate that by proper tuning of the inhibitory
time constant of the WTA, a trade-off between specificity and sensitivity of
the network can be achieved. We use the inhibitory time constant to set the
number of subpatterns per pattern we want to detect. We show that while the
percentage of successful trials are 92%, 88% and 82% for two, four and six
class classification when no pattern subdivisions are made, it increases to
100% when each pattern is subdivided into 5 or 10 subpatterns. However, the
former scenario of no pattern subdivision is more jitter resilient than the
later ones.Comment: 11 pages, 10 figures, journa
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