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Publication Date: 2025
Advanced Intelligent Systems (26404567)
Multisensory integration (MSI) is a core brain function underlying perception, learning, and behavior. Understanding the computational mechanisms of MSI is key to advancing AI and brain-inspired systems. While earlier models relied on probabilistic frameworks, recurrent neural networks (RNNs) offer advantages in capturing temporal dynamics and neural computations. This review presents a critical examination of computational models of MSI, focusing on the evolution from probabilistic integration to modern RNN-based methods. Biological evidence for temporal coordination in multisensory areas is analyzed and explored how different RNN architectures (e.g., vanilla, long short-term memory, and gated recurrent unit) simulate these dynamics. Comparative analyses show RNNs’ superiority in robustness and learning efficiency, with up to 46.9% improvement in classification tasks involving sensory fusion. We introduce a taxonomy of MSI tasks and a novel evaluation framework for model benchmarking. Real-world case studies—from speech recognition to prosthetic control—highlight practical applications. Challenges in interpretability, data efficiency, and generalization are also discussed. The review provides actionable insights for future research in both computational neuroscience and artificial intelligence. By bridging neurobiological principles and machine learning, RNN-based models pave the way for intelligent systems capable of flexible, context-aware multisensory processing. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.
Publication Date: 2025
Chaos, Solitons and Fractals (09600779) 198
We investigate how modular and hierarchical delay structures shape synchronization dynamics in networks of uniformly connected phase oscillators. Using a Kuramoto model adapted to incorporate modular interaction delays, we isolate the effects of temporal heterogeneity from structural modularity. Our analysis identifies distinct synchronization regimes dependent on oscillator frequency: global coherence at low frequencies, modular synchronization at intermediate frequencies, and incoherence dominates at high frequencies, though localized coherence can reemerge under specific delay configurations. Under hierarchical delay arrangements, we further observe a reemergence of localized coherence within sub-modules at very high frequencies, indicating multi-scale synchronization facilitated purely by temporal delays. These findings highlight the fundamental role modular and hierarchical delays play in shaping functional network dynamics, offering insights relevant to the adaptability and multi-scale processing capabilities observed in neural and technological systems. © 2025 Elsevier Ltd
Publication Date: 2023
Nonlinear Dynamics (0924090X) 111(3)pp. 2681-2691
Brain networks are characterized by flexible patterns of pairwise correlations and information exchange between different brain regions. Such dynamic patterns are crucial for an efficient response of the brain to environmental and cognitive demands. We here propose that the collective oscillations in the brain can provide a mechanism to control dynamical interactions and the exchange of information across brain networks. In particular, we show that the phase difference between oscillatory activities in different brain regions determines the transmission of neural signals. To further corroborate this, we study a network of coupled oscillators with repulsive couplings and show that the amount of information transfer between the nodes is determined by the phase differences. The emergence of multiple (locally) stable states due to the frustration makes it possible to change the patterns of information transfer between the nodes by means of the switching between different stable states. Our results indicate that frustration can be the mechanism through which large-scale brain networks control the effective connectivity and the routes for the information transfer between different brain regions. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Publication Date: 2023
Scientific Reports (20452322) 13(1)
Medical machine learning frameworks have received much attention in recent years. The recent COVID-19 pandemic was also accompanied by a surge in proposed machine learning algorithms for tasks such as diagnosis and mortality prognosis. Machine learning frameworks can be helpful medical assistants by extracting data patterns that are otherwise hard to detect by humans. Efficient feature engineering and dimensionality reduction are major challenges in most medical machine learning frameworks. Autoencoders are novel unsupervised tools that can perform data-driven dimensionality reduction with minimum prior assumptions. This study, in a novel approach, investigated the predictive power of latent representations obtained from a hybrid autoencoder (HAE) framework combining variational autoencoder (VAE) characteristics with mean squared error (MSE) and triplet loss for forecasting COVID-19 patients with high mortality risk in a retrospective framework. Electronic laboratory and clinical data of 1474 patients were used in the study. Logistic regression with elastic net regularization (EN) and random forest (RF) models were used as final classifiers. Moreover, we also investigated the contribution of utilized features towards latent representations via mutual information analysis. HAE Latent representations model achieved decent performance with an area under ROC curve of 0.921 (±0.027) and 0.910 (±0.036) with EN and RF predictors, respectively, over the hold-out data in comparison with the raw (AUC EN: 0.913 (±0.022); RF: 0.903 (±0.020)) models. The study aims to provide an interpretable feature engineering framework for the medical environment with the potential to integrate imaging data for efficient feature engineering in rapid triage and other clinical predictive models. © 2023, Crown.
Publication Date: 2020
Chaos (10541500) 30(1)
We investigate the impact of a stochastic forcing, comprised of a sum of time-lagged copies of a single source of noise, on the system dynamics. This type of stochastic forcing could be made artificially, or it could be the result of shared upstream inputs to a system through different channel lengths. By means of a rigorous mathematical framework, we show that such a system is, in fact, equivalent to the classical case of a stochastically-driven dynamical system with time-delayed intrinsic dynamics but without a time lag in the input noise. We also observe a resonancelike effect between the intrinsic period of the oscillation and the time lag of the stochastic forcing, which may be used to determine the intrinsic period of oscillations or the inherent time delay in dynamical systems with oscillatory behavior or delays. As another useful application of imposing time-lagged stochastic forcing, we show that the dynamics of a system can be controlled by changing the time lag of this stochastic forcing, in a fashion similar to the classical case of Pyragas control via delayed feedback. To confirm these results experimentally, we set up a laser diode system with such stochastic inputs, which effectively behaves as a Langevin system. As in the theory, a peak emerged in the autocorrelation function of the output signal that could be tuned by the lag of the stochastic input. Our findings, thus, indicate a new approach for controlling useful instabilities in dynamical systems. © 2020 Author(s).
Publication Date: 2017
Physical Review E (24700045) 95(3)
Shared upstream dynamical processes are frequently the source of common inputs in various physical and biological systems. However, due to finite signal transmission speeds and differences in the distance to the source, time shifts between otherwise common inputs are unavoidable. Since common inputs can be a source of correlation between the elements of multi-unit dynamical systems, regardless of whether these elements are directly connected with one another or not, it is of importance to understand their impact on synchronization. As a canonical model that is representative for a variety of different dynamical systems, we study limit-cycle oscillators that are driven by stochastic time-shifted common inputs. We show that if the oscillators are coupled, time shifts in stochastic common inputs do not simply shift the distribution of the phase differences, but rather the distribution actually changes as a result. The best synchronization is therefore achieved at a precise intermediate value of the time shift, which is due to a resonance-like effect with the most probable phase difference that is determined by the deterministic dynamics. © 2017 American Physical Society.
Publication Date: 2015
Scientific Reports (20452322) 5
We show that for two weakly coupled identical neuronal oscillators with strictly positive phase resetting curve, isochronous synchrony can only be seen in the absence of noise and an arbitrarily weak noise can destroy entrainment and generate intermittent phase slips. Small inhomogeneity-mismatch in the intrinsic firing rate of the neurons-can stabilize the phase locking and lead to more precise relative spike timing of the two neurons. The results can explain how for a class of neuronal models, including leaky integrate-fire model, inhomogeneity can increase correlation of spike trains when the neurons are synaptically connected.
Publication Date: 2013
Frontiers in Computational Neuroscience (16625188) (JUL)
We address a question on the effect of common stochastic inputs on the correlation of the spike trains of two neurons when they are coupled through direct connections. We show that the change in the correlation of small amplitude stochastic inputs can be better detected when the neurons are connected by direct excitatory couplings. Depending on whether intrinsic firing rate of the neurons is identical or slightly different, symmetric or asymmetric connections can increase the sensitivity of the system to the input correlation by changing the mean slope of the correlation transfer function over a given range of input correlation. In either case, there is also an optimum value for synaptic strength which maximizes the sensitivity of the system to the changes in input correlation. © 2013 Bolhasani, Azizi and Valizadeh.
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