Methods for Interpreting and Understanding Deep Neural Networks

This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data. It also discusses a number of practical applications.Comment: 14 pages, 10 figure

A fast and accurate algorithm for facial feature segmentation

Abstrac

Interpreting EEG and MEG signal modulation in response to facial features: the influence of top-down task demands on visual processing strategies

The visual processing of faces is a fast and efficient feat that our visual system usually accomplishes many times a day. The N170 (an Event-Related Potential) and the M170 (an Event-Related Magnetic Field) are thought to be prominent markers of the face perception process in the ventral stream of visual processing that occur ~ 170 ms after stimulus onset. The question of whether face processing at the time window of the N170 and M170 is automatically driven by bottom-up visual processing only, or whether it is also modulated by top-down control, is still debated in the literature. However, it is known from research on general visual processing, that top-down control can be exerted much earlier along the visual processing stream than the N170 and M170 take place. I conducted two studies, each consisting of two face categorization tasks. In order to examine the influence of top-down control on the processing of faces, I changed the task demands from one task to the next, while presenting the same set of face stimuli. In the first study, I recorded participants’ EEG signal in response to faces while they performed both a Gender task and an Expression task on a set of expressive face stimuli. Analyses using Bubbles (Gosselin & Schyns, 2001) and Classification Image techniques revealed significant task modulations of the N170 ERPs (peaks and amplitudes) and the peak latency of maximum information sensitivity to key facial features. However, task demands did not change the information processing during the N170 with respect to behaviourally diagnostic information. Rather, the N170 seemed to integrate gender and expression diagnostic information equally in both tasks. In the second study, participants completed the same behavioural tasks as in the first study (Gender and Expression), but this time their MEG signal was recorded in order to allow for precise source localisation. After determining the active sources during the M170 time window, a Mutual Information analysis in connection with Bubbles was used to examine voxel sensitivity to both the task-relevant and the task-irrelevant face category. When a face category was relevant for the task, sensitivity to it was usually higher and peaked in different voxels than sensitivity to the task-irrelevant face category. In addition, voxels predictive of categorization accuracy were shown to be sensitive to task-relevant, behaviourally diagnostic facial features only. I conclude that facial feature integration during both N170 and M170 is subject to top-down control. The results are discussed against the background of known face processing models and current research findings on visual processing

Alterations in amygdala-prefrontal functional connectivity account for excessive worry and autonomic dysregulation in generalized anxiety disorder

Background: Generalized anxiety disorder (GAD) is characterized by the core symptom of uncontrollable worry. Functional magnetic resonance imaging studies link this symptom to aberrant functional connectivity between the amygdala and prefrontal cortex. Patients with GAD also display a characteristic pattern of autonomic dysregulation. Although frontolimbic circuitry is implicated in the regulation of autonomic arousal, no previous study to our knowledge combined functional magnetic resonance imaging with peripheral physiologic monitoring in these patients to test the hypothesis that core symptoms of worry and autonomic dysregulation in GAD arise from a shared underlying neural mechanism. Methods: We used resting-state functional magnetic resonance imaging and the measurement of parasympathetic autonomic function (heart rate variability) in 19 patients with GAD and 21 control subjects to define neural correlates of autonomic and cognitive responses before and after induction of perseverative cognition. Seed-based analyses were conducted to quantify brain changes in functional connectivity with the right and left amygdala. Results: Before induction, patients showed relatively lower connectivity between the right amygdala and right superior frontal gyrus, right paracingulate/anterior cingulate cortex, and right supramarginal gyrus than control subjects. After induction, such connectivity patterns increased in patients with GAD and decreased in control subjects, and these changes tracked increases in state perseverative cognition. Moreover, decreases in functional connectivity between the left amygdala and subgenual cingulate cortex and between the right amygdala and caudate nucleus predicted the magnitude of reduction in heart rate variability after induction. Conclusions: Our results link functional brain mechanisms underlying worry and rumination to autonomic dyscontrol, highlighting overlapping neural substrates associated with cognitive and autonomic responses to the induction of perseverative cognitions in patients with GAD