A computational neuroscience project using high-performance computing to study spiking neural networks and neocortex evolution. Access to CINECA resources supports numerical research into biologically inspired network models and cortical organization.
Research connections
Publications
2026
Entropy (Basel, Switzerland)
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 systematica…Read the full abstractCollapse abstract
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.
2025
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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 bi…Read the full abstractCollapse abstract
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.