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1. Does predictive processing imply predictive coding in models of spoken word recognition?

   Magnuson, J. S. ; Li, M. ; Luthra, S. ; You, H. ; Steiner, R. ( July 2020 , Proceedings of the Cognitive Science Society)

   Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. In some cases, this may reflect a conflation of terms, but predictive coding formally is a computational mechanism where only deviations from top- down expectations are passed between levels of representation. We evaluate three models’ ability to simulate predictive processing and ask whether they exhibit the putative hallmark of formal predictive coding (reduced signal when input matches expectations). Of crucial interest, TRACE, an interactive activation model that does not explicitly implement prediction, exhibits both predictive processing and model- internal signal reduction. This may indicate that interactive activation is functionally equivalent or approximant to predictive coding, or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding.
2. From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

   Tanaka, H ; Nayebi, A ; Maheswaranathan, N ; McIntosh, L ; Baccus, S ; Ganguli, S. ( December 2019 , Advances in neural information processing systems)

   Recently, deep feedforward neural networks have achieved considerable success in modeling biological sensory processing, in terms of reproducing the input-output map of sensory neurons. However, such models raise profound questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (a biological circuit) with another (a deep network), without understanding either? Moreover, beyond neural representations, are the deep network's computational mechanisms for generating neural responses the same as those in the brain? Without a systematic approach to extracting and understanding computational mechanisms from deep neural network models, it can be difficult both to assess the degree of utility of deep learning approaches in neuroscience, and to extract experimentally testable hypotheses from deep networks. We develop such a systematic approach by combining dimensionality reduction and modern attribution methods for determining the relative importance of interneurons for specific visual computations. We apply this approach to deep network models of the retina, revealing a conceptual understanding of how the retina acts as a predictive feature extractor that signals deviations from expectations for diverse spatiotemporal stimuli. For each stimulus, our extracted computational mechanisms are consistent with prior scientific literature, and in one case yields a new mechanistic hypothesis. Thusmore »overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms.« less
3. Cortical encoding of melodic expectations in human temporal cortex

   https://doi.org/10.7554/eLife.51784  

   Di Liberto, Giovanni M ; Pelofi, Claire ; Bianco, Roberta ; Patel, Prachi ; Mehta, Ashesh D ; Herrero, Jose L ; de Cheveigné, Alain ; Shamma, Shihab ; Mesgarani, Nima ( March 2020 , eLife)

   Humans engagement in music rests on underlying elements such as the listeners’ cultural background and interest in music. These factors modulate how listeners anticipate musical events, a process inducing instantaneous neural responses as the music confronts these expectations. Measuring such neural correlates would represent a direct window into high-level brain processing. Here we recorded cortical signals as participants listened to Bach melodies. We assessed the relative contributions of acoustic versus melodic components of the music to the neural signal. Melodic features included information on pitch progressions and their tempo, which were extracted from a predictive model of musical structure based on Markov chains. We related the music to brain activity with temporal response functions demonstrating, for the first time, distinct cortical encoding of pitch and note-onset expectations during naturalistic music listening. This encoding was most pronounced at response latencies up to 350 ms, and in both planum temporale and Heschl’s gyrus.
4. A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram

   https://doi.org/10.3390/s21082779  

   Dhillon, Navjodh ; Sutandi, Agustinus ; Vishwanath, Manoj ; Lim, Miranda ; Cao, Hung ; Si, Dong ( April 2021 , Sensors)

   Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16–64 s epochs for TBI vs. control conditions. This work can enable the development ofmore »systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.« less
5. Quantifying Iron Overload using MRI, Active Contours, and Convolutional Neural Networks

   Sajewski, Andrea and ( May 2019 , 11th Annual Undergraduate Research & Scholarship Symposium, Duquesne University)

   Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, providing insight about the structure of each tissue. Liver iron content can be quantified based on this T2* value because signal decay accelerates with increasing iron concentration. We developed a method to automatically segment the liver from the MRI image to accurately calculate iron content. Our current algorithm utilizes the active contour model for image segmentation, which iteratively evolves a curve until it reaches an edge or a boundary. We applied this algorithm to each MRI image in addition to a map of pixelwise T2* values, combining basic image processing with imaging physics. One of the limitations of this segmentationmore »model is how it handles noise in the MRI data. Recent advancements in deep learning have enabled researchers to utilize convolutional neural networks to denoise and reconstruct images. We used the Trainable Nonlinear Reaction Diffusion network architecture to denoise the MRI images, allowing for smoother segmentation while preserving fine details.« less