Nitric Oxide (NO) is an important molecule in processes such as synaptic plasticity and memory formation[1]. In the cerebellum, NO is produced by neural NO Synthase expressed in Granule Cells and Molecular Layer Interneurons[2]. NO diffuses freely in tissues beyond synaptic connections, functioning as a volume neurotransmitter. At parallel fiber-Purkinje Cell (pf-PC) synapses[4][5], NO is necessary but not sufficient for both Long Term Potentiation and Depression[6][7]. This study investigates NO role in cerebellar learning mechanisms using a biologically realistic Spiking Neural Network, implementing a NO-dependent plasticity model and testing it with an Eye-Blink Classical Conditioning (EBCC) protocol[8][9].
Methods We developed the NO Diffusion Simulator (NODS), a Python module modeling NO production and diffusion within a Spiking Neural Network. The model represents the chemical cascade triggered by calcium influx during spikes, leading to NO production[10]. NO diffusion is modeled using the heat diffusion equation and an inactivation term, solved with Green's function[11]. We implemented a NO-dependent supervised Spike-Timing Dependent Plasticity[12]where a termweights synaptic updates based on NO concentration. The model was tested using the EBCC protocol, where the cerebellum learns to associate a Conditioned Stimulus (CS) with an Unconditioned Stimulus (US), generating anticipatory Conditioned Responses (CR) (Fig. 1). Results We first validated the equation in NODS with the single source production of NO performed with NEURON simulator[13].Then we investigated the effect of NO in cerebellar learning trough the addition of different background noises. In principle, the incoming CS and US stimuli should exert a depression only at the pf-PC synapses active right before the US stimuli. By adding an increasing noise these learning processes result directly impaired.When including NO-dependent plasticity, we can highlight a different behavior of during a CS and 4 Hz simulation. Here, only the pf-PC synapses receiving the CS stimuli have sufficient NO for plasticity, while the ones randomly activated by noise remain under threshold. Discussion The results demonstrate that NO interaction significantly affects synaptic plasticity, dynamically adjusting learning rates based on synaptic activity patterns. This mechanism enhances the cerebellum's capacity to prioritize relevant inputs and mitigate learning interference selectively modulating synaptic efficacy. Our results prove that NO could act as a noise filter, thus focusing learning in the cerebellum only on the relevant inputs for the ongoing task. The NODS implementation connects the molecular processes and large spiking neural 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.
Figure 1. Spiking neural network with NODS mechanism. (A) SNN of the cerebellum microcircuit, with the different populations and detail of CS, US and Background Noise stimuli. (B) One trial of the EBCC protocol with timing of the stimuli. (C) The NO production mechanism at a single synapse. (D) NO as volume transmitter at different pf-PC synapses. Acknowledgements This research is supported by Horizon Europe Program for Research and Innovation under Grant Agreement No. 101147319 (EBRAINS 2.0). The simulations in NEURON were implemented by Stefano Masoli, Department of Brain and Behavioral Sciences, Università di Pavia, Pavia, Italy. References 1. https://doi.org/10.1126/science.1470903 2. https://doi.org/10.1016/s0896-6273(00)80340-2 3. https://doi.org/10.1523/JNEUROSCI.4064-13.2014 4. https://doi.org/10.1074/jbc.M111.289777 5. https://doi.org/10.1016/0006-2952(89)90403-6 6. https://doi.org/10.1073/pnas.122206399 7. https://doi.org/10.1016/j.celrep.2016.03.004 8. https://doi.org/10.3389/fnsys.2022.919761 9. https://doi.org/10.3389/fninf.2018.00088 10. https://doi.org/10.1016/j.niox.2009.07.002 11. https://doi.org/10.3389/fninf.2019.00063 12. https://doi.org/10.1109/TBME.2015.2485301 13. https://doi.org/10.1007/978-3-319-65130-9_9
