A novel simulation framework for modelling extracellular recordings in cortical tissue : implementation, validation and application to gamma oscillations in mammals

PhD ThesisThis thesis concerns the simulation of local field potentials (LFPs) from cortical network activity; network gamma oscillations in particular. Alterations in gamma oscillation measurements are observed in many brain disorders. Understanding these measurements in terms of the underlying neuronal activity is crucial for developing effective therapies. Modelling can help to unravel the details of this relationship. We first investigated a reduced compartmental neuron model for use in network simulations. We showed that reduced models containing <10 compartments could reproduce the LFP characteristics of the equivalent full-scale compartmental models to a reasonable degree of accuracy. Next, we created the Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX): a Matlab tool for simulating LFPs in large, spatially organised neuronal networks. We used VERTEX to implement a large-scale neocortical slice model exhibiting gamma frequency oscillations under bath kainate application, an experimental preparation frequently used to investigate properties of gamma oscillations. We built the model based on currently available data on neocortical anatomy. By positioning a virtual electrode grid to match Utah array placement in experiments in vitro, we could make a meaningful direct comparison between simulated and experimentally recorded LFPs. We next investigated the spatial properties of the LFP in more detail, using a smaller model of neocortical layer 2/3. We made several observations about the spatial features of the LFP that shed light on past experimental recordings: how gamma power and coherence decays away from an oscillating region, how layer thickness affects the LFP, which neurons contribute most to the LFP signal, and how the LFP power scales with frequency at different model locations. Finally, we discuss the relevance of our simulation results to experimental neuroscience. Our observations on the dominance of parvalbumin-expressing basket interneuron synapses on the LFP are of particular relevance to epilepsy and schizophrenia: changes in parvalbumin expression have been observed in both disorders. We suggest how our results could inform future experiments and aid in the interpretation of their results

Sanity Checks for Saliency Metrics

Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. These methods produce estimates of the relevance of each pixel to the classification output score, which can be displayed as a saliency map that highlights important pixels. Despite a proliferation of such methods, little effort has been made to quantify how good these saliency maps are at capturing the true relevance of the pixels to the classifier output (i.e. their "fidelity"). We therefore investigate existing metrics for evaluating the fidelity of saliency methods (i.e. saliency metrics). We find that there is little consistency in the literature in how such metrics are calculated, and show that such inconsistencies can have a significant effect on the measured fidelity. Further, we apply measures of reliability developed in the psychometric testing literature to assess the consistency of saliency metrics when applied to individual saliency maps. Our results show that saliency metrics can be statistically unreliable and inconsistent, indicating that comparative rankings between saliency methods generated using such metrics can be untrustworthy.Comment: Accepted for publication at the Thirty Fourth AAAI conference on Artificial Intelligence (AAAI-20

Reasoning and learning services for coalition situational understanding

Situational understanding requires an ability to assess the current situation and anticipate future situations, requiring both pattern recognition and inference. A coalition involves multiple agencies sharing information and analytics. This paper considers how to harness distributed information sources, including multimodal sensors, together with machine learning and reasoning services, to perform situational understanding in a coalition context. To exemplify the approach we focus on a technology integration experiment in which multimodal data — including video and still imagery, geospatial and weather data — is processed and fused in a service-oriented architecture by heterogeneous pattern recognition and inference components. We show how the architecture: (i) provides awareness of the current situation and prediction of future states, (ii) is robust to individual service failure, (iii) supports the generation of ‘why’ explanations for human analysts (including from components based on ‘black box’ deep neural networks which pose particular challenges to explanation generation), and (iv) allows for the imposition of information sharing constraints in a coalition context where there is varying levels of trust between partner agencies

Stakeholders in explainable AI

There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general consensus over what is meant by ‘explainable’ and ‘interpretable’. In this paper, we argue that this lack of consensus is due to there being several distinct stakeholder communities. We note that, while the concerns of the individual communities are broadly compatible, they are not identical, which gives rise to different intents and requirements for explainability/ interpretability. We use the software engineering distinction between validation and verification, and the epistemological distinctions between knowns/unknowns, to tease apart the concerns of the stakeholder communities and highlight the areas where their foci overlap or diverge. It is not the purpose of the authors of this paper to ‘take sides’ — we count ourselves as members, to varying degrees, of multiple communities — but rather to help disambiguate what stakeholders mean when they ask ‘Why?’ of an AI

Integrating learning and reasoning services for explainable information fusion

—We present a distributed information fusion system able to integrate heterogeneous information processing services based on machine learning and reasoning approaches. We focus on higher (semantic) levels of information fusion, and highlight the requirement for the component services, and the system as a whole, to generate explanations of its outputs. Using a case study approach in the domain of traffic monitoring, we introduce component services based on (i) deep neural network approaches and (ii) heuristic-based reasoning. We examine methods for explanation generation in each case, including both transparency (e.g, saliency maps, reasoning traces) and post-hoc methods (e.g, explanation in terms of similar examples, identification of relevant semantic objects). We consider trade-offs in terms of the classification performance of the services and the kinds of available explanations, and show how service integration offers more robust performance and explainability