Decoding Function from Activity in Neural Systems

From science fiction to venture capital, decoding the brain presents a tantalizing application of technology to neuroscience. In neuroscience, and more recently for the emergent field of neural network interpretability, decoding neural function from its activity remains key to realizing this objective. Therefore, this thesis aims to identify activation characteristics that underlie function in neural systems. To this end, we employ a variety of decoding tools from the fields of neuroscience and neural network interpretability. In each chapter, we use these tools to connect either the activity of individual or groups of neural sources with two different types of function. The first type of function, which we call &ldquo;predictive function,&rdquo; arises when activity patterns predict behaviors of the system. Predictive function provides evidence that the corresponding activity is involved in the behavior but does not imply that it causes the behavior. For the latter type of evidence, we invoke the concept of &ldquo;causal function.&rdquo; Neural activity causes function when removing it from the network changes the behavior of the system. We start by decoding function from activity in the human brain. Using standard predictive decoding tools in neuroscience, we seek to refine our understanding of the role of the temporal parietal junction (TPJ). The TPJ is a higher-order area of the brain, located between motor and sensory regions where various processing streams converge to generate novel functions. These novel functions can be difficult to decipher. To decipher to function of the TPJ, we conduct experiments to generate predictive functional evidence for its role in the brain. From selective activity in the TPJ, we are able to predict whether participants are engaged in competition. This predictive ability indicates that the selective activity of the TPJ correlates with competitive behaviors but does not prove that it is necessary for competitive processing. To prove that predictive decoding tools isolate activity patterns that cause function we would have to remove the neural sources underlying behavior. To causally test predictive decoding tools, in chapter four, we turn to neural network models of human vision. In neural networks, we ask what is the relationship between selectivity and causal deficits produced by ablation. We conduct experiments that show that the most causally important neurons are those that are highly selective and highly active. This shows that selectivity alone does not modulate causal function in neural networks but interacts with activation magnitude to produce function. This is in contrast to the story told by predictive decoding which shows that the most selective neural sources, regardless of magnitude, code functionally important information. Previously, we tested for the relationship between activity of single neurons and causal function. But there is considerable debate in neuroscience and neural network interpretability over whether individual neural sources or groups of neural sources together enable function. In chapter five, we engage with this debate by testing for a relationship between patterns of activity across multiple neurons and causal function. We find that metrics that compare activity patterns across multiple neurons are more sensitive to changes to predictive function than causal function. A significant interaction effect between metric and function type also suggests that the benefits of using one metric over another are overestimated using predictive function tests alone. This work highlights the need for metrics that track causal function. In chapter six, we apply what we have learned to the domain of neural network pruning. We hypothesize that if highly selective and active neurons are important at the end of training for causal function, then promoting those characteristics at initialization should improve network training. We conduct experiments to rank and prune neurons according to their activation characteristics. We find that pruning the lowest magnitude neurons improves training performance over randomly pruned sparse networks. However, pruning based on selectivity seems to have no effect. This suggests that activation characteristics can be useful tools for pruning early in training. Through these studies I address several questions concerning the relationship between activation patterns in single and multiple neuron sources and predictive and causal function in neural systems.</p

Much Easier Said Than Done: Falsifying the Causal Relevance of Linear Decoding Methods

Linear classifier probes are frequently utilized to better understand how neural networks function. Researchers have approached the problem of determining unit importance in neural networks by probing their learned, internal representations. Linear classifier probes identify highly selective units as the most important for network function. Whether or not a network actually relies on high selectivity units can be tested by removing them from the network using ablation. Surprisingly, when highly selective units are ablated they only produce small performance deficits, and even then only in some cases. In spite of the absence of ablation effects for selective neurons, linear decoding methods can be effectively used to interpret network function, leaving their effectiveness a mystery. To falsify the exclusive role of selectivity in network function and resolve this contradiction, we systematically ablate groups of units in subregions of activation space. Here, we find a weak relationship between neurons identified by probes and those identified by ablation. More specifically, we find that an interaction between selectivity and the average activity of the unit better predicts ablation performance deficits for groups of units in AlexNet, VGG16, MobileNetV2, and ResNet101. Linear decoders are likely somewhat effective because they overlap with those units that are causally important for network function. Interpretability methods could be improved by focusing on causally important units.Comment: 6 pages, 3 figures, to be published in I Can't Believe It's Note Better Workshop at NeurIPS 202

Seasonal water level manipulation for flood risk management influences home-range size of common bream Abramis Brama L. in a lowland river

The increased threat of flooding from climate change requires ever greater management of rivers to alleviate flood risk. Although the impacts of river modification on fish communities are well documented, the effects of river management practices on fish behaviour have received relatively little attention. Here, a long-term (4 years) acoustic telemetry study was used to analyse the spatial–temporal behaviour of common bream in a lowland river system (River Witham, Lincolnshire, UK) in which water levels are artificially manipulated biannually as part of a flood storage strategy. Levels are lowered in the autumn and increased again in the spring, to increase in-river winter flood storage capacity. Home-range size varied according to season, with home ranges being larger in the spring and summer months in comparison with those recorded during the autumn and winter months. When water levels within the river system were artificially manipulated, the bream responded by altering their home-range size, increasing it after the levels had been raised and reducing it following the lowering of the river levels. This is in contrast to the cumulative overall distances bream were recorded to travel, which were unaffected by water level manipulation, suggesting water level manipulation did not affect activity levels. Although such changes in behaviour do not necessarily equate to a negative impact on fitness, reduced home-range size brought about by water level manipulation does have implications for habitat availability and the number of competitive, predatory and parasitic interactions encountered. Copyright © 2013 John Wiley & Sons, Ltd

Cryo-EM structures of the SARS-CoV-2 endoribonuclease Nsp15 reveal insight into nuclease specificity and dynamics.

Nsp15, a uridine specific endoribonuclease conserved across coronaviruses, processes viral RNA to evade detection by host defense systems. Crystal structures of Nsp15 from different coronaviruses have shown a common hexameric assembly, yet how the enzyme recognizes and processes RNA remains poorly understood. Here we report a series of cryo-EM reconstructions of SARS-CoV-2 Nsp15, in both apo and UTP-bound states. The cryo-EM reconstructions, combined with biochemistry, mass spectrometry, and molecular dynamics, expose molecular details of how critical active site residues recognize uridine and facilitate catalysis of the phosphodiester bond. Mass spectrometry revealed the accumulation of cyclic phosphate cleavage products, while analysis of the apo and UTP-bound datasets revealed conformational dynamics not observed by crystal structures that are likely important to facilitate substrate recognition and regulate nuclease activity. Collectively, these findings advance understanding of how Nsp15 processes viral RNA and provide a structural framework for the development of new therapeutics

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Neuroanatomical Correlates of Anxiety and Depression Tripartite Dimensions in Adolescents

This study aims to incorporate the Tripartite Model of Anxiety and Depression dimensions, proposed by Watson and Clark (1991) and administered with the Mood and Anxiety Symptom Questionnaire (MASQ), into continuous measures which can then be correlated with brain structure patterns in adolescents. During adolescence, the brain undergoes massive change, change which results in radical new ways of processing emotion and decision-making, and implicated in the onset of anxiety and depression. Through this methodology, we investigate the Dual Systems Model of adolescent development which suggests that two separate systems, the socioemotional system and the cognitive control system, develop either at different rates or at different times and that these differing rates are implicated in anxiety and depression development as well. Anatomical measures of mostly sensory processing regions correlated with anxious arousal and negative affect, supporting the overlap of those two dimensions and implicating sensory processing in anxiety. Anatomical measures of mostly cognitive processing regions in the prefrontal cortex (PFC) linked to learning, memory, and self-awareness correlated with positive affect. This finding supports the importance of PFC maturity in the modulation of and experience of emotional information. We speculate that positive affect is high during early adolescence, decreases in middle adolescence, then stabilizes or increases again as cognitive processing regions come online