Douglas Kirkpatrick

Clifford Bohm, Douglas Kirkpatrick, Victoria Cao et al.

Assessing where and how information is stored in biological networks (such as neuronal and genetic networks) is a central task both in neuroscience and in molecular genetics, but most available tools focus on the network's structure as opposed to its function. Here we introduce a new information-theoretic tool: "information fragmentation analysis" that, given full phenotypic data, allows us to localize information in complex networks, determine how fragmented (across multiple nodes of the network) the information is, and assess the level of encryption of that information. Using information fragmentation matrices, we can also create information flow graphs that illustrate how information propagates through these networks. We illustrate the use of this tool by analyzing how artificial brains that evolved "in silico" solve particular tasks, and show how information fragmentation analysis provides deeper insights into how these brains process information and "think". The measures of information fragmentation and encryption that result from our methods also quantify complexity of information processing in these networks and how this processing complexity differs between primary exposure to sensory data (early in the lifetime) and later routine processing.

Samuel-Hunter Berndt, Douglas Kirkpatrick, Timothy Taviano et al.

The proper classification of major eye movements, saccades, fixations, and smooth pursuits, remains essential to utilizing eye-tracking data. There is difficulty in separating out smooth pursuits from the other behavior types, particularly from fixations. To this end, we propose a new offline algorithm, I-VDT-HMM, for tertiary classification of eye movements. The algorithm combines the simplicity of two foundational algorithms, I-VT and I-DT, as has been implemented in I-VDT, with the statistical predictive power of the Viterbi algorithm. We evaluate the fitness across a dataset of eight eye movement records at eight sampling rates gathered from previous research, with a comparison to the current state-of-the-art using the proposed quantitative and qualitative behavioral scores. The proposed algorithm achieves promising results in clean high sampling frequency data and with slight modifications could show similar results with lower quality data. Though, the statistical aspect of the algorithm comes at a cost of classification time.

Arend Hintze, Douglas Kirkpatrick, Christoph Adami

Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of generalizing concepts, ANNs seem to focus on surface statistical regularities in a given task. Here we compare how recurrent artificial neural networks, long short-term memory units, and Markov Brains sense and remember their environments. We show that information in Markov Brains is localized and sparsely distributed, while the other neural network substrates "smear" information about the environment across all nodes, which makes them vulnerable to noise.

Musad Haque, Connor McGowan, Yifan Guo et al.

Unraveling the nature of the communication model that governs which two individuals in a swarm interact with each other is an important line of inquiry in the collective behavior sciences. A number of models have been proposed in the biological swarm literature, with the leading models being the metric, topological, and visual models. The hypothesis evaluated in this manuscript is whether the choice of a communication model impacts the performance of a tasked artificial swarm. The biological models are used to design coordination algorithms for a simulated swarm, which are evaluated over a range of six swarm robotics tasks. Each task has an associated set of performance metrics that are used to evaluate how the communication models fare against each other. The general findings demonstrate that the communication model significantly affects the swarm's performance for individual tasks, and this result implies that the communication model-task pairing is an important consideration when designing artificial swarms. Further analysis of each tasks' performance metrics reveal instances in which pairwise considerations of model and one of the various experimental factors becomes relevant. The reported research demonstrates that the artificial swarm's task performance can be increased through the careful selection of a communications model.