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Event segmentation theory posits that people segment continuous experience into discrete events and that event boundaries occur when there are large transient increases in prediction error. Here, we set out to test this theory in the context of story listening, by using a deep learning language model (GPT‐2) to compute the predicted probability distribution of the next word, at each point in the story. For three stories, we used the probability distributions generated by GPT‐2 to compute the time series of prediction error. We also asked participants to listen to these stories while marking event boundaries. We used regression models to relate the GPT‐2 measures to the human segmentation data. We found that event boundaries are associated with transient increases in Bayesian surprise but not with a simpler measure of prediction error (surprisal) that tracks, for each word in the story, how strongly that word was predicted at the previous time point. These results support the hypothesis that prediction error serves as a control mechanism governing event segmentation and point to important differences between operational definitions of prediction error.

 

A far-red fluorescent protein-based indicator allowed the imaging of synaptic Zn 2+ in brain slices and awake mice. 

Abstract The Snf1-related protein kinase 1 (SnRK1) is the plant homolog of the heterotrimeric AMP-activated protein kinase/sucrose non-fermenting 1 (AMPK/Snf1), which works as a major regulator of growth under nutrient-limiting conditions in eukaryotes. Along with its conserved role as a master regulator of sugar starvation responses, SnRK1 is involved in controlling the developmental plasticity and resilience under diverse environmental conditions in plants. In this review, through mining and analyzing the interactome and phosphoproteome data of SnRK1, we are highlighting its role in fundamental cellular processes such as gene regulation, protein synthesis, primary metabolism, protein trafficking, nutrient homeostasis, and autophagy. Along with the well-characterized molecular interaction in SnRK1 signaling, our analysis highlights several unchartered regions of SnRK1 signaling in plants such as its possible communication with chromatin remodelers, histone modifiers, and inositol phosphate signaling. We also discuss potential reciprocal interactions of SnRK1 signaling with other signaling pathways and cellular processes, which could be involved in maintaining flexibility and homeostasis under different environmental conditions. Overall, this review provides a comprehensive overview of the SnRK1 signaling network in plants and suggests many novel directions for future research. 

Neurons in the auditory cortex are tuned to specific ranges of sound frequencies. Although the cellular and network mechanisms underlying neuronal sound frequency selectivity are well studied and reflect the interplay of thalamocortical and intracortical excitatory inputs and further refinement by cortical inhibition, the precise synaptic signaling mechanisms remain less understood. To gain further understanding on these mechanisms and their effects on sound-driven behavior, we used in vivo imaging as well as behavioral approaches in awake and behaving female and male mice. We discovered that synaptic zinc, a modulator of neurotransmission and responsiveness to sound, sharpened the sound frequency tuning of principal and parvalbumin-expressing neurons and widened the sound frequency tuning of somatostatin-expressing inhibitory neurons in layer 2/3 of the primary auditory cortex. In the absence of cortical synaptic zinc, mice exhibited reduced acuity for detecting changes in sound frequencies. Together, our results reveal that cell-type-specific effects of zinc contribute to cortical sound frequency tuning and enhance acuity for sound frequency discrimination. SIGNIFICANCE STATEMENT Neuronal tuning to specific features of sensory stimuli is a fundamental property of cortical sensory processing that advantageously supports behavior. Despite the established roles of synaptic thalamocortical and intracortical excitation and inhibition in cortical tuning, the precise synaptic signaling mechanisms remain unknown. Here, we investigated these mechanisms in the mouse auditory cortex. We discovered a previously unknown signaling mechanism linking synaptic zinc signaling with cell-specific cortical tuning and enhancement in sound frequency discrimination acuity. Given the abundance of synaptic zinc in all sensory cortices, this newly discovered interaction between synaptic zinc and cortical tuning can provide a general mechanism for modulating neuronal stimulus specificity and sensory-driven behavior. 

In 2013, we released a position paper to launch a community effort to define a common set of building blocks for constructing graph algorithms in the language of linear algebra. This led to the GraphBLAS. We released a specification for the C programming language binding to the GraphBLAS in 2017. Since that release, multiple libraries that conform to the GraphBLAS C specification have been produced. In this position paper, we launch the next phase of this ongoing community effort: a project to assemble a set of high level graph algorithms built on top of the GraphBLAS. While many of these algorithms are well-known with high quality implementations available, they have not been assembled in one place and integrated with the GraphBLAS. We call this project the LAGraph graph algorithms project and with this position paper, we put out a call for collaborators to join us. While the initial goal is to just assemble these algorithms into a single framework, the long term goal is a library of production-worthy code, with the LAGraph library serving as an open source repository of verified graph algorithms that use the GraphBLAS. 

In 2013, we released a position paper to launch a community effort to define a common set of building blocks for constructing graph algorithms in the language of linear algebra. This led to the GraphBLAS. We released a specification for the C programming language binding to the GraphBLAS in 2017. Since that release, multiple libraries that conform to the GraphBLAS C specification have been produced. In this position paper, we launch the next phase of this ongoing community effort: a project to assemble a set of high level graph algorithms built on top of the GraphBLAS. While many of these algorithms are well-known with high quality implementations available, they have not been assembled in one place and integrated with the GraphBLAS. We call this project the LAGraph graph algorithms project and with this position paper, we put out a call for collaborators to join us. While the initial goal is to just assemble these algorithms into a single framework, the long term goal is a library of production-worthy code, with the LAGraph library serving as an open source repository of verified graph algorithms that use the GraphBLAS. 

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research. 

The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based graph algorithms to the broadest possible audience. Mathematically, the GraphBLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, the two are easily connected by matrix multiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of a small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.