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1. AIM: A network model of attention in auditory cortex

   https://doi.org/10.1371/journal.pcbi.1009356  

   Chou, Kenny F.; Sen, Kamal (August 2021, PLOS Computational Biology)

   Gutkin, Boris S. (Ed.)

   Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem. 

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2. A robust and compact population code for competing sounds in auditory cortex

   https://doi.org/10.1152/jn.00148.2023  

   Nocon, Jian Carlo; Witter, Jake; Gritton, Howard; Han, Xue; Houghton, Conor; Sen, Kamal (September 2023, Journal of Neurophysiology)

   Little is known about how populations of neurons within cortical circuits encode sensory stimuli in the presence of competing stimuli at other spatial locations. Here, we investigate this problem in auditory cortex using a recently proposed information-theoretic approach. We find a small subset of neurons nearly maximizes information about target sounds in the presence of competing maskers, approaching information levels for isolated stimuli, and provides a noise-robust code for sounds in a complex auditory scene. 

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3. Correlates of Attention in the Cingulate Cortex During Gambling in Humans

   https://doi.org/10.1109/EMBC44109.2020.9175499  

   Taylor, Christopher; Greene, Patrick; D'Aleo, Raina; Breault, Macauley Smith; Steinhardt, Cynthia; Gonzalez-Martinez, Jorge; Sarma, Sridevi V. (July 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   null (Ed.)
4. A Tale of Two Comprehensions? Analyzing Student Programmer Attention during Code Summarization

   https://doi.org/10.1145/3664808  

   Karas, Zachary; Bansal, Aakash; Zhang, Yifan; Li, Toby; McMillan, Collin; Huang, Yu (September 2024, ACM Transactions on Software Engineering and Methodology)

   Code summarization is the task of creating short, natural language descriptions of source code. It is an important part of code comprehension and a powerful method of documentation. Previous work has made progress in identifying where programmers focus in code as they write their own summaries (i.e., Writing). However, there is currently a gap in studying programmers’ attention as they read code with pre-written summaries (i.e., Reading). As a result, it is currently unknown how these two forms of code comprehension compare: Reading and Writing. Also, there is a limited understanding of programmer attention with respect to program semantics. We address these shortcomings with a human eye-tracking study (n= 27) comparing Reading and Writing. We examined programmers’ attention with respect to fine-grained program semantics, including their attention sequences (i.e., scan paths). We find distinctions in programmer attention across the comprehension tasks, similarities in reading patterns between them, and differences mediated by demographic factors. This can help guide code comprehension in both computer science education and automated code summarization. Furthermore, we mapped programmers’ gaze data onto the Abstract Syntax Tree to explore another representation of human attention. We find that visual behavior on this structure is not always consistent with that on source code. 

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5. SPLAT: A Framework for Optimised GPU Code-Generation for SParse reguLar ATtention

   https://doi.org/10.1145/3720503  

   Gupta, Ahan; Yuan, Yueming; Jain, Devansh; Ge, Yuhao; Aponte, David; Zhou, Yanqi; Mendis, Charith (April 2025, Proceedings of the ACM on Programming Languages)

   Multi-head-self-attention (MHSA) mechanisms achieve state-of-the-art (SOTA) performance across natural language processing and vision tasks. However, their quadratic dependence on sequence lengths has bottlenecked inference speeds. To circumvent this bottleneck, researchers have proposed various sparse-MHSA models, where a subset of full attention is computed. Despite their promise, current sparse libraries and compilers do not support high-performance implementations fordiversesparse-MHSA patterns due to the underlying sparse formats they operate on. On one end, sparse libraries operate ongeneral sparse formatswhich target extreme amounts of random sparsity (<10% non-zero values) and have high metadata inO(nnzs). On the other end, hand-written kernels operate oncustom sparse formatswhich target specific sparse-MHSA patterns. However, the sparsity patterns in sparse-MHSA are moderately sparse (10-50% non-zero values) and varied, resulting in general sparse formats incurring high metadata overhead and custom sparse formats covering few sparse-MSHA patterns, trading off generality for performance. We bridge this gap, achieving both generality and performance, by proposing a novel sparse format: affine-compressed-sparse-row (ACSR) and supporting code-generation scheme, SPLAT, that generates high-performance implementations for diverse sparse-MHSA patterns on GPUs. Core to our proposed format and code generation algorithm is the observation that common sparse-MHSA patterns have uniquely regular geometric properties. These properties, which can be analyzed just-in-time, expose novel optimizations and tiling strategies that SPLAT exploits to generate high-performance implementations for diverse patterns. To demonstrate SPLAT’s efficacy, we use it to generate code for various sparse-MHSA models, achieving speedups of up-to 2.05x and 4.05x over hand-written kernels written in triton and TVM respectively on A100 GPUs in single-precision. 

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