Andrea Duggento

RESEARCH OUTPUT

Selected publications

1. Entropy, Inhibition and Memory in Balanced Spiking Reservoirs.Entropy (Basel, Switzerland) · 2026
   Recurrent neural networks are studied along two largely parallel tracks: as machine-learning models evaluated by task performance and as computational-neuroscience models of cortical circuits evaluated by dynamical realism. Reservoir computing offers a meeting point, yet the link between dynamical regime and computational performance has not been systematically mapped in biologically constrained spiking architectures. We treat the Brunel balanced excitatory-inhibitory network as a reservoir and characterize separation capacity (kernel quality) and transient memory (corrected linear memory capacity, validated by non-parametric mutual information) across the full phase diagram. The analysis uses a four-state Markov source whose Shannon entropy rate is set in closed form by a single parameter at fixed marginal entropy. Both capabilities increase monotonically with the inhibitory ratio g, remaining jointly highest in the asynchronous irregular regime, with diminishing increments consistent with eventual saturation; the synchronous irregular regime, despite a network timescale three orders of magnitude longer, supports neither. Memory further requires sparse input coupling: dense coupling collapses the driven timescale and erases memory in every regime. Inhibitory balance thus emerges as a unified architectural control parameter, providing a quantitative design criterion for cortical-circuit modeling and reservoir computing applications.

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2. A new in silico model to precisely design focused ultrasound brain therapies.Medical physics · 2026
   BACKGROUND: Focused ultrasound (FUS) combined with microbubbles enables transient and noninvasive blood-brain barrier (BBB) opening, facilitating targeted drug delivery. However, accurate treatment planning remains difficult due to inter-patient anatomical variability and the common assumption in simulations that brain tissues behave like water. PURPOSE: To develop and evaluate MODFUS ( M O D e l t o p r e c i s e l y d e s i g n F o c u s e d U l t r a S o u n d e x p e r i m e n t s ), an in silico acoustic simulation framework that integrates a high-resolution anatomical head model to quantify the impact of intracranial tissue heterogeneity and probe alignment on transmitted acoustic pressure, supporting treatment planning for BBB opening. METHODS: MODFUS integrates an anatomical head model comprising 115 tissue types and Computed Tomography (CT)-derived skull properties. FUS simulations were conducted using a 250 kHz single-element transducer at 13 distinct skull entry locations. Two modeling approaches were evaluated: a classical model, in which the skull was embedded in water and brain tissues were homogenized as water, and an heterogeneous anatomical model. To further assess robustness, an additional set of 50 simulations introduced controlled perturbations in probe positioning, consisting of angular deviations ( ± 18 ∘ ) and translational offsets ( ± 7 mm) relative to the reference configuration. Model- and configuration-dependent differences were quantified using peak positive pressure (PPP), peak negative pressure (PNP), and potential therapeutic volume. Statistical significance was assessed using Wilcoxon rank-sum or Wilcoxon signed-rank tests ( α = 0.05). For Classical vs Realistic models, Wilcoxon rank-sum tests were applied to PPP, PNP, and BBB exposure volume, and Levene's test assessed variance differences across 13 positions. Multiple testing was controlled using the Holm-Bonferroni procedure ( α = 0.05) across all tests. Effect sizes were quantified using Cohen's d with 95% confidence intervals. RESULTS: Compared to the classical "skull+water" benchmark model, the heterogeneous model predicted up to 11% lower PPP, slightly lower PNP ( ∼ -5%), and 35% smaller potential BBB exposure volumes across the 13 paired sonication positions. After Bonferroni correction, Paired statistical testing (Wilcoxon signed-rank, two-sided) showed significant differences for PPP (p = 0.0270) and potential BBB exposure volume (p = 0.0015), while differences in PNP were not statistically significant (p = 0.8286). Levene's test for variance confirmed significant heteroscedasticity for PPP (p = 0.027) and PNP (p = 3.2 × 10), but not for BBB exposure volume (p = 0.2680). Cohen's d effect sizes indicated a large positive effect for PPP, a small negative effect for PNP, and a very large positive effect for BBB exposure volume. CONCLUSIONS: MODFUS demonstrates the influence of incorporating detailed tissue heterogeneity on simulation outcomes, including pressure distribution and potential BBB exposure volume. These results highlight the importance of realistic soft tissue modeling and stereotaxic probe alignment for safe and effective FUS treatment planning. The study serves as a preliminary proof-of-concept. Future studies incorporating in vivo experiments will be required to quantify the accuracy of this approach.

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3. Spiking Reservoir Computing Architectures for Model-based Epileptic Brain State Recognition.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
   Bio-inspired networks offer rich dynamic capabilities with minimal energy demands, especially when implemented on neuromorphic hardware. In particular, the recurrence in brain circuits enables Recurrent Spiking Neural Networks (RSNNs) to generate complex spatio-temporal spike patterns, forming internal representations of time-varying signals. Despite this biological sophistication, such architectures are often applied to machine learning tasks with limited biological relevance. In this work, we present a spiking reservoir computing architecture, implemented as a fully spiking Liquid State Machine (LSM) with Leaky Integrate-and-Fire (LIF) neurons, designed to recognize and decode internal brain states. We simulate epileptic activity using a spiking model and develop a complete pipeline where a source generates Local Field Potentials (LFPs). These signals are encoded through population coding and processed by the LSM, which performs regression on the biophysical parameters controlling epileptic dynamics, thereby inferring the source state. To enhance the LSM's performance, we apply a biologically inspired synaptic plasticity mechanism to the RSNN. Our results demonstrate that a simple, unsupervised plasticity mechanism can optimize the internal parameters of the reservoir, particularly in smaller networks. This approach offers a hardware-efficient strategy for task-specific adaptation of general-purpose circuits, highlighting its suitability for edge-device implementations. Our findings emphasize the potential of spiking reservoir computing for real-time decoding of complex brain dynamics, such as epileptic activity, and underscore the advantages of biologically inspired, energy-efficient methods for neuromorphic systems in real-world applications.Clinical relevance- The ability to decode internal brain states from Local Field Potentials (LFPs) using a biologically inspired spiking reservoir computing architecture has significant implications for clinical neuroscience. By providing a real-time, energy-efficient method for tracking epileptic dynamics, this approach could aid in the development of advanced braincomputer interfaces (BCIs) and personalized neurostimulation therapies. Such a system may help clinicians monitor seizure progression, optimize treatment strategies, and improve patient outcomes in clinical epilepsy management.

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4. Beyond multilayer perceptrons: Investigating complex topologies in neural networks.Neural networks : the official journal of the International Neural Network Society · 2024
   In this study, we explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs), with a particular focus on complex topologies. We propose a novel methodology for constructing complex ANNs based on various topologies, including Barabási-Albert, Erdős-Rényi, Watts-Strogatz, and multilayer perceptrons (MLPs). The constructed networks are evaluated on synthetic datasets generated from manifold learning generators, with varying levels of task difficulty and noise, and on real-world datasets from the UCI suite. Our findings reveal that complex topologies lead to superior performance in high-difficulty regimes compared to traditional MLPs. This performance advantage is attributed to the ability of complex networks to exploit the compositionality of the underlying target function. However, this benefit comes at the cost of increased forward-pass computation time and reduced robustness to graph damage. Additionally, we investigate the relationship between various topological attributes and model performance. Our analysis shows that no single attribute can account for the observed performance differences, suggesting that the influence of network topology on approximation capabilities may be more intricate than a simple correlation with individual topological attributes. Our study sheds light on the potential of complex topologies for enhancing the performance of ANNs and provides a foundation for future research exploring the interplay between multiple topological attributes and their impact on model performance.

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   PublisherPubMedarXivarXiv
5. Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow.Brain stimulation · 2023
   BACKGROUND: The autonomic response to transcutaneous auricular vagus nerve stimulation (taVNS) has been linked to the engagement of brainstem circuitry modulating autonomic outflow. However, the physiological mechanisms supporting such efferent vagal responses are not well understood, particularly in humans. HYPOTHESIS: We present a paradigm for estimating directional brain-heart interactions in response to taVNS. We propose that our approach is able to identify causal links between the activity of brainstem nuclei involved in autonomic control and cardiovagal outflow. METHODS: We adopt an approach based on a recent reformulation of Granger causality that includes permutation-based, nonparametric statistics. The method is applied to ultrahigh field (7T) functional magnetic resonance imaging (fMRI) data collected on healthy subjects during taVNS. RESULTS: Our framework identified taVNS-evoked functional brainstem responses with superior sensitivity compared to prior conventional approaches, confirming causal links between taVNS stimulation and fMRI response in the nucleus tractus solitarii (NTS). Furthermore, our causal approach elucidated potential mechanisms by which information is relayed between brainstem nuclei and cardiovagal, i.e., high-frequency heart rate variability, in response to taVNS. Our findings revealed that key brainstem nuclei, known from animal models to be involved in cardiovascular control, exert a causal influence on taVNS-induced cardiovagal outflow in humans. CONCLUSION: Our causal approach allowed us to noninvasively evaluate directional interactions between fMRI BOLD signals from brainstem nuclei and cardiovagal outflow.

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