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Abstract-A variant of a sparse distributed memory (SDM) is shown to have the capability of storing and recalling patterns containing rank-order information. These are patterns where information is encoded not only in the subset of neuron outputs that fire, but also in the order in which that subset fires. This is an interesting companion to several recent works in the neuroscience literature, showing that human memories may be stored in terms of neural spike timings. In our model, the ordering is stored in static synaptic weights using a Hebbian single-shot learning algorithm, and can be reliably recovered whenever the associated input is supplied. It is shown that the memory can operate using only unipolar binary connections throughout. The behavior of the memory under noisy input conditions is also investigated. It is shown that the memory is capable of improving the quality of the data that passes through it. That is, under appropriate conditions the output retrieved from the memory is less noisy than the input used to retrieve it. Thus, this memory architecture could be used as a component in a complex system with stable noise properties and, we argue, it can be implemented using spiking neurons.Index Terms-Associative memory, neural networks (NNs), rank-order codes, sparse distributed memory (SDM), spiking neurons.

The number of machine learning, artificial intelligence or data science related software engineering projects using Agile methodology is increasing. However, there are very few studies on how such projects work in practice. In this paper, we analyze project issues tracking data taken from Scrum (a popular tool for Agile) for several machine learning projects. We compare this data with corresponding data from non-machine learning projects, in an attempt to analyze how machine learning projects are executed differently from normal software engineering projects. On analysis, we find that machine learning project issues use different kinds of words to describe issues, have higher number of exploratory or research oriented tasks as compared to implementation tasks, and have a higher number of issues in the product backlog after each sprint, denoting that it is more difficult to estimate the duration of machine learning project related tasks in advance. After analyzing this data, we propose a few ways in which Agile machine learning projects can be better logged and executed, given their differences with normal software engineering projects.