Training recurrent neural networks (RNNs) has become a go-to approach for generating and evaluating mechanistic neural hypotheses for cognition. The ease and efficiency of training RNNs with backpropagation through time and the availability of robustly supported deep learning libraries has made RNN modeling more approachable and accessible to neuroscience.

Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing different cognitive tasks -- e.g., watching movies or making decisions. MVPA works best with a well-designed feature set and an adequate sample size. However, most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. Further, training a robust, generalized predictive model that can analyze homogeneous cognitive tasks provided by multi-site fMRI datasets has additional challenges. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning (TL) approach that can functionally align homogeneous multi-site fMRI datasets, and so improve the prediction performance in every site.

Can multi-modal large language models (MLLMs) truly understand what they can see? Extending Searle's Chinese Room into the multi-modal domain, this paper proposes the Visual Room argument: MLLMs may describe every visual detail precisely yet fail to comprehend the underlying emotions and intentions, namely seeing is not understanding. Building on this, we introduce \textit{Visual Room} 2.0, a hierarchical benchmark for evaluating perception-cognition alignment of MLLMs. We model human perceptive and cognitive processes across three levels: low, middle, and high, covering 17 representative tasks. The perception component ranges from attribute recognition to scene understanding, while the cognition component extends from textual entailment to causal and social reasoning. The dataset contains 350 multi-modal samples, each with six progressive questions (2,100 in total) spanning perception to cognition. Evaluating 10 state-of-the-art (SoTA) MLLMs, we highlight three key findings: (1) MLLMs exhibit stronger perceptual competence than cognitive ability (8.0\%$\uparrow$); (2) cognition appears not causally dependent on perception-based reasoning; and (3) cognition scales with model size, but perception does not consistently improve with larger variants. This work operationalizes Seeing $\ne$ Understanding as a testable hypothesis, offering a new paradigm from perceptual processing to cognitive reasoning in MLLMs. Our dataset is available at https://huggingface.co/datasets/LHK2003/PCBench.

The ability to continually learn, retain and deploy skills to accomplish goals is a key feature of intelligent and efficient behavior. However, the neural mechanisms facilitating the continual learning and flexible (re-)composition of skills remain elusive. Here, we study continual learning and the compositional reuse of learned computations in recurrent neural network (RNN) models using a novel two-system approach: one system that infers what computation to perform, and one that implements how to perform it. We focus on a set of compositional cognitive tasks commonly studied in neuroscience. To construct the what system, we first show that a large family of tasks can be systematically described by a probabilistic generative model, where compositionality stems from a shared underlying vocabulary of discrete task epochs. The shared epoch structure makes these tasks inherently compositional. We first show that this compositionality can be systematically described by a probabilistic generative model. Furthermore, We develop an unsupervised online learning approach that can learn this model on a single-trial basis, building its vocabulary incrementally as it is exposed to new tasks, and inferring the latent epoch structure as a time-varying computational context within a trial. We implement the how system as an RNN whose low-rank components are composed according to the context inferred by the what system. Contextual inference facilitates the creation, learning, and reuse of low-rank RNN components as new tasks are introduced sequentially, enabling continual learning without catastrophic forgetting. Using an example task set, we demonstrate the efficacy and competitive performance of this two-system learning framework, its potential for forward and backward transfer, as well as fast compositional generalization to unseen tasks.

To address this, we introduce a hybrid architecture that integrates the continuous-time recurrent dynamics of RNNs with the spatial processing capabilities of CNNs.

Training recurrent neural networks (RNNs) has become a go-to approach for generating and evaluating mechanistic neural hypotheses for cognition.

Abstract--Current deep learning models for electroencephalog-raphy (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability. Although emerging foundation models aim for broader applicability, their rigid dependence on fixed, high-density multi-channel montages restricts their use across heterogeneous datasets and in missing-channel or practical low-channel settings. T o address these limitations, we introduce SingLEM, a self-supervised foundation model that learns robust, general-purpose representations from single-channel EEG, making it inherently hardware agnostic. The model employs a hybrid encoder architecture that combines convolutional layers to extract local features with a hierarchical transformer to model both short-and long-range temporal dependencies. When evaluated as a fixed feature extractor across six motor imagery and cognitive tasks, aggregated single-channel representations consistently outperformed leading multi-channel foundation models and handcrafted baselines. These results demonstrate that a single-channel approach can achieve state-of-the-art generalization while enabling fine-grained neurophysiological analysis and enhancing interpretability. LECTROENCEPHALOGRAPHY (EEG) is a noninvasive neurophysiological technique that measures brain activity through scalp electrodes. Because of to its high temporal resolution, portability, and affordability, EEG is widely applied in diverse domains, including brain-computer interfaces (BCIs) [1], sleep staging [2], seizure detection [3], [4], [5], clinical diagnosis [6], [7], and emotion recognition [8], [9], [10]. Despite its potential, EEG analysis is challenged by non-stationarity across subjects and sessions, susceptibility to noise (e.g., ocular or muscular artifacts), and low signal-to-noise ratios [11]. To address this, deep neural networks (DNNs) have emerged as the state-of-the-art paradigm, learning complex and task-relevant features automatically from raw data [12]. This work was supported in part by JSPS KAKENHI 23H00548. The work of Jamiyan Sukhbaatar was supported by the Mongolia-Japan Engineering for Education Development (MJEED) project.