Localist representation can improve efficiency for detection and counting

Almost all representations have both distributed and localist aspects, depending upon what properties of the data are being considered. With noisy data, features represented in a localist way can be detected very efficiently, and in binary representations they can be counted more efficiently than those represented in a distributed way. Brains operate in noisy environments, so the localist representation of behaviourally important events is advantageous, and fits what has been found experimentally. Distributed representations require more neurons to perform as efficiently, but they do have greater versatility

Network Dynamics of Visual Object Recognition

Visual object recognition is the principal mechanism by which humans and many animals interpret their surroundings. Despite the complexity of neural computation required, object recognition is achieved with such rapidity and accuracy that it appears to us almost effortless. Extensive human and non-human primate research has identified putative category-selective regions within higher-level visual cortex, which are thought to mediate object recognition. Despite decades of study, however, the functional organization and network dynamics within these regions remain poorly understood, due to a lack of appropriate animal models as well as the spatiotemporal limitations of current non-invasive human neuroimaging techniques (e.g. fMRI, scalp EEG). To better understand these issues, we leveraged the high spatiotemporal resolution of intracranial EEG (icEEG) recordings to study rapid, transient interactions between the disseminated cortical substrates within category-specific networks. Employing novel techniques for the topologically accurate and statistically robust analysis of grouped icEEG, we found that category-selective regions were spatially arranged with respect to cortical folding patterns, and relative to each other, to generate a hierarchical information structuring of visual information within higher-level visual cortex. This may facilitate rapid visual categorization by enabling the extraction of different levels of object detail across multiple spatial scales. To characterize network interactions between distributed regions sharing the same category-selectivity, we evaluated feed-forward, hierarchal and parallel, distributed models of information flow during face perception via measurements of cortical activation, functional and structural connectivity, and transient disruption through electrical stimulation. We found that input from early visual cortex (EVC) to two face-selective regions – the occipital and fusiform face areas (OFA and FFA, respectively) – occurred in a parallelized, distributed fashion: Functional connectivity between EVC and FFA began prior to the onset of subsequent re-entrant connectivity between the OFA and FFA. Furthermore, electrophysiological measures of structural connectivity revealed independent cortico- cortical connections between the EVC and both the OFA and FFA. Finally, direct disruption of the FFA, but not OFA, impaired face-perception. Given that the FFA is downstream of the OFA, these findings are incompatible with the feed-forward, hierarchical models of visual processing, and argue instead for the existence of parallel, distributed network interactions

Women in Science 2016

Women in Science 2016 summarizes research done by Smith College’s Summer Research Fellowship (SURF) Program participants. Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. In 2016, 150 students participated in SURF (144 hosted on campus and nearby eld sites), supervised by 56 faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants were asked to summarize their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1005/thumbnail.jp

Learning in the Real World: Constraints on Cost, Space, and Privacy

The sheer demand for machine learning in fields as varied as: healthcare, web-search ranking, factory automation, collision prediction, spam filtering, and many others, frequently outpaces the intended use-case of machine learning models. In fact, a growing number of companies hire machine learning researchers to rectify this very problem: to tailor and/or design new state-of-the-art models to the setting at hand. However, we can generalize a large set of the machine learning problems encountered in practical settings into three categories: cost, space, and privacy. The first category (cost) considers problems that need to balance the accuracy of a machine learning model with the cost required to evaluate it. These include problems in web-search, where results need to be delivered to a user in under a second and be as accurate as possible. The second category (space) collects problems that require running machine learning algorithms on low-memory computing devices. For instance, in search-and-rescue operations we may opt to use many small unmanned aerial vehicles (UAVs) equipped with machine learning algorithms for object detection to find a desired search target. These algorithms should be small to fit within the physical memory limits of the UAV (and be energy efficient) while reliably detecting objects. The third category (privacy) considers problems where one wishes to run machine learning algorithms on sensitive data. It has been shown that seemingly innocuous analyses on such data can be exploited to reveal data individuals would prefer to keep private. Thus, nearly any algorithm that runs on patient or economic data falls under this set of problems. We devise solutions for each of these problem categories including (i) a fast tree-based model for explicitly trading off accuracy and model evaluation time, (ii) a compression method for the k-nearest neighbor classifier, and (iii) a private causal inference algorithm that protects sensitive data

A Human-centric Approach to NLP in Healthcare Applications

The abundance of personal health information available to healthcare professionals can be a facilitator to better care. However, it can also be a barrier, as the relevant information is often buried in the sheer amount of personal data, and healthcare professionals already lack time to take care of both patients and their data. This dissertation focuses on the role of natural language processing (NLP) in healthcare and how it can surface information relevant to healthcare professionals by modeling the extensive collections of documents that describe those whom they serve. In this dissertation, the extensive natural language data about a person is modeled as a set of documents, where the model inference is at the level of the individual, but evidence supporting that inference is found in a subset of their documents. The effectiveness of this modeling approach is demonstrated in the context of three healthcare applications. In the first application, clinical coding, document-level attention is used to model the hierarchy between a clinical encounter and its documents, jointly learning the encounter labels and the assignment of credits to specific documents. The second application, suicidality assessment using social media, further investigates how document-level attention can surface "high-signal" posts from the document set representing a potentially at-risk individual. Finally, the third application aims to help healthcare professionals write discharge summaries using an extract-then-abstract multidocument summarization pipeline to surface relevant information. As in many healthcare applications, these three applications seek to assist, not replace, clinicians. Evaluation and model design thus centers around healthcare professionals' needs. In clinical coding, document-level attention is shown to align well with professional clinical coders' expectations of evidence. In suicidality assessment, document-level attention leads to better and more time-efficient assessment by surfacing document-level evidence, shown empirically using a theoretically grounded time-aware evaluation measure and a dataset annotated by suicidality experts. Finally, extract-then-abstract summarization pipelines that assist healthcare professionals in writing discharge summaries are evaluated by their ability to surface faithful and relevant evidence

11th Annual Research Week

A History of Mission Driven Scholarshi