Biology subjects

Pedrocchi, A.

Publications and source records attributed to Pedrocchi, A..

7 recordsLinked to original sources

bioRxiv2026

Dopamine Depletion Drives Whole-Brain Oscillatory Disruptions via Cortico-Subcortical Resonance: A Multiscale Model of Parkinson's Disease in Mice

Parkinson's disease is defined by dopaminergic neuron loss in the substantia nigra, yet its hallmark, exaggerated beta-band synchrony, pervades motor cortex, thalamus, and cerebellum, implicating network dynamics far beyond any single circuit. How focal subcortical dopamine depletion translates into brain-wide oscillatory pathology remains unresolved. We use a connectome-constrained multiscale model of the mouse brain, embedding biophysically detailed spiking networks of basal ganglia and cerebellum within whole-brain corticothalamic dynamics grounded in the Allen Mouse Brain Connectivity Atlas. We show that confining dopamine depletion exclusively to subcortical circuits is sufficient to produce widespread beta hypersynchrony (10-30 Hz), accompanied by heterogeneous theta and gamma dysregulation. Virtual loop ablations reveal that cortical and cerebellar beta amplification strictly requires intact cortico-basal ganglia-thalamic feedback; severing this loop confines beta to subcortical generators. These results support resonance within closed large-scale loops, rather than local rhythmogenesis, as the mechanism underlying distributed Parkinsonian beta pathology.

neuroscience↗

bioRxiv2025

A human spiking computational model to explore sound localization

Sound localization relies on precise processing of binaural cues in medial (MSO) and lateral superior olive (LSO). However, key questions remain on how these two nuclei perform their specific computations depending on sound frequency, the relative contributions of interaural time differences (ITDs) and interaural level differences (ILDs), as well as the role of inhibitory timings. Experimental studies have struggled to address these issues because of technical challenges and a lack of methodological consistency. Here, we present a comprehensive computational model of auditory peripheral and brainstem neural populations to investigate how ITDs and ILDs are encoded by LSO and MSO. We developed a spiking neural network with realistic tonotopic organization and biologically consistent synaptic connections. We tested responses to pure tones and white noise from different locations under three cue conditions: human-recorded head-related transfer functions, isolated ITDs, and isolated ILDs. LSO neurons showed realistic ipsilateral-preferring responses across different stimuli, with cue dependency varying by tone frequency, while white noise responses were driven mostly by ILDs. MSO responses showed heterogeneous tuning, with contralateral preference for low-frequency tones, which got lost for higher-frequency ones, and white noise response driven by ITDs. We further validated two experimental findings: (1) removing MSO inhibition abolished contralateral tuning, and (2) varying the timing between excitation and inhibition produced large shifts in tuning, highlighting the importance of synaptic timing for ITD coding. This model serves as an in silico testbed for auditory research, offering new insights into the functioning of human spatial hearing.

neuroscience↗

bioRxiv2024

Modeling Nitric Oxide Diffusion and Plasticity Modulation in Cerebellar Learning

AO_SCPLOWBSTRACTC_SCPLOWNitric Oxide (NO) is a versatile signalling molecule with significant roles in various physiological processes, including synaptic plasticity and memory formation. In the cerebellum, NO is produced by neural NO Synthase and diffuses to influence synaptic changes, particularly at parallel fiber - Purkinje cell synapses. This study aims to investigate NOs role in cerebellar learning mechanisms using a biologically realistic simulation-based approach. We developed the NO Diffusion Simulator (NODS), a Python module designed to model NO production and diffusion within a cerebellar spiking neural network framework. Our simulations focus on the Eye-Blink Classical Conditioning protocol to assess the impact of NO modulation on long-term potentiation and depression at parallel fiber - Purkinje cell synapses. The results demonstrate that NO diffusion significantly affects synaptic plasticity, dynamically adjusting learning rates based on synaptic activity patterns. This metaplasticity mechanism enhances the cerebellums capacity to prioritize relevant inputs and mitigate learning interference selectively modulating synaptic efficacy. Our findings align with theoretical models suggesting that NO serves as a contextual indicator, optimizing learning rates for effective motor control and adaptation to new tasks. The NODS implementation provides an efficient tool for large-scale simulations, facilitating future studies on NO dynamics in various brain regions and neurovascular coupling scenarios. By bridging the gap between molecular processes and network-level learning, this work underscores the critical role of NO in cerebellar function and offers a robust framework for exploring NO-dependent plasticity in computational neuroscience.

neuroscience↗

bioRxiv2023

Dopamine-dependent cerebellar dysfunction enhances beta oscillations and disrupts motor learning in a multiarea model

Parkinsons disease (PD) is a chronic degenerative disorder of the central nervous system that affects the motor system. The discovery that PD motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of PD research on the basal ganglia. However, recent findings point to an active involvement of the cerebellum in PD. Here, we have developed a multiscale computational model of the rodent brains basal ganglia-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of PD neural activity, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in PD. Overall, these results suggest a relevant involvement of cerebellum in PD motor symptoms. Significance StatementThis study highlights the role of cerebellum in Parkinsons disease (PD). While most studies on PD concentrate on dopaminergic mechanisms in the basal ganglia, here we show that dopamine depletion impacts also on the cerebellum, generating a complex dysfunctional interaction between the two subcortical circuits. To investigate this interaction, we developed de novo a multiarea multiscale network model that mechanistically addresses the effects of dopamine depletionon the cerebellum. Our study demonstrates that this aspect is crucial to reproduce experimental data, particularly the increased beta wave activity. Moreover, alterations in spike-time-dependent plasticity at the parallel fibre - Purkinje cell synapse of cerebellum can explain the link between dopamine depletion to motor learning impairment. These simulations indicate that the cerebellum warrants more attention in future PD research.

neuroscience↗

bioRxiv2023

Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning

According to Marrs motor learning theory, plasticity at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, the discovery of multiple forms of plasticity distributed over different cerebellar circuit synapses prompts to remap the cerebellar learning sites. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding upbound and downbound modules that are subject to multiple plasticity rules. Simulations show that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity in both the pf-PC synapses and in the feedforward inhibitory loop passing through the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both modules contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

neuroscience↗

bioRxiv2021

Brain-inspired spiking neural network controller for a neurorobotic whisker system

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.

bioengineering↗

bioRxiv2018

Complex dynamics in simplified neuronal models: reproducing Golgi cell electroresponsiveness

Brain neurons exhibit complex electroresponsive properties - including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset - which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in \"realistic\" models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. E-GLIF derives from the GLIF model family and is therefore mono-compartmental, keeps the limited computational load typical of a linear low-dimensional system, admits analytical solutions and can be tuned through gradient-descent algorithms. Importantly, E-GLIF is designed to maintain a correspondence between model parameters and neuronal membrane mechanisms through a minimum set of equations. In order to test its potential, E-GLIF was used to model a specific neuron showing rich and complex electroresponsiveness, the cerebellar Golgi cell, and was validated against experimental electrophysiological data recorded from Golgi cells in acute cerebellar slices. During simulations, E-GLIF was activated by stimulus patterns, including current steps and synaptic inputs, identical to those used for the experiments. The results demonstrate that E-GLIF can reproduce the whole set of complex neuronal dynamics typical of these neurons - including intensity-frequency curves, spike-frequency adaptation, depolarization-induced and post-inhibitory rebound bursting, spontaneous subthreshold oscillations, resonance and phase-reset, - providing a new effective tool to investigate brain dynamics in large-scale simulations.

neuroscience↗