  In this paper we analyze and extend the neural network based associative
memory proposed by Gripon and Berrou. This associative memory resembles the
celebrated Willshaw model with an added partite cluster structure. In the
literature, two retrieving schemes have been proposed for the network dynamics,
namely sum-of-sum and sum-of-max. They both offer considerably better
performance than Willshaw and Hopfield networks, when comparable retrieval
scenarios are considered. Former discussions and experiments concentrate on the
erasure scenario, where a partial message is used as a probe to the network, in
the hope of retrieving the full message. In this regard, sum-of-max outperforms
sum-of-sum in terms of retrieval rate by a large margin. However, we observe
that when noise and errors are present and the network is queried by a corrupt
probe, sum-of-max faces a severe limitation as its stringent activation rule
prevents a neuron from reviving back into play once deactivated. In this
manuscript, we categorize and analyze different error scenarios so that both
the erasure and the corrupt scenarios can be treated consistently. We make an
amendment to the network structure to improve the retrieval rate, at the cost
of an extra scalar per neuron. Afterwards, five different approaches are
proposed to deal with corrupt probes. As a result, we extend the network
capability, and also increase the robustness of the retrieving procedure. We
then experimentally compare all these proposals and discuss pros and cons of
each approach under different types of errors. Simulation results show that if
carefully designed, the network is able to preserve both a high retrieval rate
and a low running time simultaneously, even when queried by a corrupt probe.
