Recurrent neural networks (RNNs) are a widely used tool for sequential data analysis, however, they are still often seen as black boxes of computation. Understanding the functional principles of these networks is critical to developing ideal model architectures and optimization strategies. Previous studies typically only emphasize the network representation post-training, overlooking their evolution process throughout training. Here, we present Multiway Multislice PHATE (MM-PHATE), a novel method for visualizing the evolution of RNNs' hidden states. MM-PHATE is a graph-based embedding using structured kernels across the multiple dimensions spanned by RNNs: time, training epoch, and units. We demonstrate on various datasets that MM-PHATE uniquely preserves hidden representation community structure among units and identifies information processing and compression phases during training. The embedding allows users to look under the hood of RNNs across training and provides an intuitive and comprehensive strategy to understanding the network's internal dynamics and draw conclusions, e.g., on why and how one model outperforms another or how a specific architecture might impact an RNN's learning ability.

Modern recordings of neural activity provide diverse observations of neurons across brain areas, behavioral conditions, and subjects -- thus presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics underlying cognitive function. Current methods, however, often fail to fully harness the richness of such data as they either provide an uninterpretable representation (e.g., via "black box" deep networks) or over-simplify the model (e.g., assume stationary dynamics or analyze each session independently). Here, instead of regarding asynchronous recordings that lack alignment in neural identity or brain areas as a limitation, we exploit these diverse views of the same brain system to learn a unified model of brain dynamics. We assume that brain observations stem from the joint activity of a set of functional neural ensembles (groups of co-active neurons) that are similar in functionality across recordings, and propose to discover the ensemble and their non-stationary dynamical interactions in a new model we term CrEIMBO (Cross-Ensemble Interactions in Multi-view Brain Observations). CrEIMBO identifies the composition of the per-session neural ensembles through graph-driven dictionary learning and models the ensemble dynamics as a latent sparse time-varying decomposition of global sub-circuits, thereby capturing non-stationary dynamics. CrEIMBO identifies multiple co-active sub-circuits while maintaining representation interpretability due to sharing sub-circuits across sessions. CrEIMBO distinguishes session-specific from global (session-invariant) computations by exploring when distinct sub-circuits are active. We demonstrate CrEIMBO's ability to recover ground truth components in synthetic data and uncover meaningful brain dynamics, capturing cross-subject and inter- and intra-area variability, in high-density electrode recordings of humans performing a memory task.

Identifying latent interactions within complex systems is key to unlocking deeper insights into their operational dynamics, including how their elements affect each other and contribute to the overall system behavior. For instance, in neuroscience, discovering neuron-to-neuron interactions is essential for understanding brain function; in ecology, recognizing the interactions among populations is key for understanding complex ecosystems. Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification challenging. Existing dynamical system identification methods often yield operators that accurately capture short-term behavior but fail to predict long-term trends, suggesting an incomplete capture of the underlying process. Methods that consider extended forecasts (e.g., recurrent neural networks) lack explicit representations of element-wise interactions and require substantial training data, thereby failing to capture interpretable network operators. Here we introduce Lookahead-driven Inference of Networked Operators for Continuous Stability (LINOCS), a robust learning procedure for identifying hidden dynamical interactions in noisy time-series data. LINOCS integrates several multi-step predictions with adaptive weights during training to recover dynamical operators that can yield accurate long-term predictions. We demonstrate LINOCS' ability to recover the ground truth dynamical operators underlying synthetic time-series data for multiple dynamical systems models (including linear, piece-wise linear, time-changing linear systems' decomposition, and regularized linear time-varying systems) as well as its capability to produce meaningful operators with robust reconstructions through various real-world examples.

Data in many scientific domains are often collected under multiple distinct states (e.g., different clinical interventions), wherein latent processes (e.g., internal biological factors) can create complex variability between individual trials both within single states and between states. A promising approach for addressing this complexity is uncovering fundamental representational units within the data, i.e., functional Building Blocks (BBs), that can adjust their temporal activity and component structure across trials to capture the diverse spectrum of cross-trial variability. However, existing methods for understanding such multi-dimensional data often rely on tensor factorization under assumptions that may not align with the characteristics of real-world data, and struggle to accommodate trials of different durations, missing samples, and varied sampling rates. Here, we present a framework for Similarity-driven Building Block Inference using Graphs across States (SiBBlInGS). SiBBlInGS employs a robust graph-based dictionary learning approach for BB discovery that considers shared temporal activity, inter- and intra-state relationships, non-orthogonal components, and variations in session counts and duration across states, while remaining resilient to noise, random initializations, and missing samples. Additionally, it enables the identification of state-specific vs. state-invariant BBs and allows for cross-state controlled variations in BB structure and per-trial temporal variability. We demonstrate SiBBlInGS on synthetic and several real-world examples to highlight its ability to provide insights into the underlying mechanisms of complex phenomena across fields.

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity, or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold. Building on this concept, we propose a new decomposed dynamical system model that represents complex non-stationary and nonlinear dynamics of time series data as a sparse combination of simpler, more interpretable components. Our model is trained through a dictionary learning procedure, where we leverage recent results in tracking sparse vectors over time. The decomposed nature of the dynamics is more expressive than previous switched approaches for a given number of parameters and enables modeling of overlapping and non-stationary dynamics. In both continuous-time and discrete-time instructional examples we demonstrate that our model can well approximate the original system, learn efficient representations, and capture smooth transitions between dynamical modes, focusing on intuitive low-dimensional non-stationary linear and nonlinear systems. Furthermore, we highlight our model's ability to efficiently capture and demix population dynamics generated from multiple independent subnetworks, a task that is computationally impractical for switched models. Finally, we apply our model to neural "full brain" recordings of C. elegans data, illustrating a diversity of dynamics that is obscured when classified into discrete states.

While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in other languages or do not reach the same performance level. This undesired effect of AI advancements increases the gap between access to new technology from different populations across the world. This unsought bias mainly discriminates against individuals whose English skills are less developed, e.g., non-English speakers children. Following significant advancements in AI research in recent years, OpenAI has recently presented DALL-E: a powerful tool for creating images based on English text prompts. While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations. An additional limitation of the current DALL-E model is that it only allows for the creation of a few images in response to a given input prompt, rather than a series of consecutive coherent frames that tell a story or describe a process that changes over time. Here, we present an easy-to-use automatic DALL-E storytelling framework that leverages the existing DALL-E model to enable fast and coherent visualizations of non-English songs and stories, pushing the limit of the one-step-at-a-time option DALL-E currently offers. We show that our framework is able to effectively visualize stories from non-English texts and portray the changes in the plot over time. It is also able to create a narrative and maintain interpretable changes in the description across frames. Additionally, our framework offers users the ability to specify constraints on the story elements, such as a specific location or context, and to maintain a consistent style throughout the visualization.

As AI algorithms increasingly participate in daily activities that used to be the sole province of humans, we are inevitably called upon to consider how much machines are really like us. To address this question, we turn to the Turing test and systematically benchmark current AIs in their abilities to imitate humans. We establish a methodology to evaluate humans versus machines in Turing-like tests and systematically evaluate a representative set of selected domains, parameters, and variables. The experiments involved testing 769 human agents, 24 state-of-the-art AI agents, 896 human judges, and 8 AI judges, in 21,570 Turing tests across 6 tasks encompassing vision and language modalities. Surprisingly, the results reveal that current AIs are not far from being able to impersonate human judges across different ages, genders, and educational levels in complex visual and language challenges. In contrast, simple AI judges outperform human judges in distinguishing human answers versus machine answers. The curated large-scale Turing test datasets introduced here and their evaluation metrics provide valuable insights to assess whether an agent is human or not. The proposed formulation to benchmark human imitation ability in current AIs paves a way for the research community to expand Turing tests to other research areas and conditions. All of source code and data are publicly available at https://tinyurl.com/8x8nha7p