Alessandro Selvitella

Vikas N. O'Reilly-Shah, Alessandro Maria Selvitella

Recurrent neural networks trained for time-series prediction often develop latent trajectories that preserve qualitative structure of the dynamical systems generating their inputs. Recent empirical work has documented topology-preserving latent organization in trained recurrent models, and recent theoretical results in reservoir computing establish conditions under which the synchronization map is an embedding. Here we synthesize these threads into a unified account of when contracting recurrent networks yield smooth, topology-preserving internal representations for a broad and biologically relevant class of inputs: regular dynamics on invariant circles and tori. Our contribution is an integrated framework that assembles (i) generalized synchronization and embedding guarantees for contracting reservoirs, (ii) regularity mechanisms ensuring differentiability of the synchronization map under mild constraints, and (iii) a base-system viewpoint in which the invariant manifold generating the input stream is treated as the driving system. In this regular setting, the conditions commonly viewed as restrictive in chaotic-attractor analyses become mild and readily satisfied by standard contractive architectures. The framework clarifies how representational content in recurrent circuits is inherently historical: the network state encodes finite windows of input history rather than instantaneous stimuli. By consolidating disparate empirical and theoretical results under common assumptions, the synthesis yields concrete, testable expectations about when prediction-trained recurrent circuits should (or should not) form smooth latent embeddings and how required state dimension scales with the intrinsic dimension of the driving dynamics.

Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang et al.

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. SSTL first extracts a set of common features for all subjects in each site. It then uses TL to map these site-specific features to a site-independent shared space in order to improve the performance of the MVPA. SSTL uses a scalable optimization procedure that works effectively for high-dimensional fMRI datasets. The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space. We evaluate the effectiveness of the proposed method for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques.

Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han et al.

Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Resonance Imaging (fMRI) datasets. Most of the existing HA methods utilized unsupervised approaches, where they only maximized the correlation between the voxels with the same position in the time series. However, these unsupervised solutions may not be optimum for handling the functional alignment in the supervised MVP problems. This paper proposes a Supervised Hyperalignment (SHA) method to ensure better functional alignment for MVP analysis, where the proposed method provides a supervised shared space that can maximize the correlation among the stimuli belonging to the same category and minimize the correlation between distinct categories of stimuli. Further, SHA employs a generalized optimization solution, which generates the shared space and calculates the mapped features in a single iteration, hence with optimum time and space complexities for large datasets. Experiments on multi-subject datasets demonstrate that SHA method achieves up to 19% better performance for multi-class problems over the state-of-the-art HA algorithms.