This study introduces an approach to optimizing large-scale embodied spiking neural networks (SNNs) for simulating the brain in a closed-loop environment, crucial for validating theoretical neuroscience hypotheses about the brain-body relationship. Accurately modeling this relationship at scale allows for the simulation of neural plasticity, temporal dynamics, and spike timing. Traditional parameter tuning methods are impractical for complex SNNs due to their non-differentiable nature and computational challenges. To address these issues, we apply gradient descent optimization with forward propagation through time, enhanced by surrogate gradient techniques, enabling efficient and scalable SNN tuning. We demonstrate this approach with a proof-of-concept system comprising a three-layer leaky-integrate- and-fire neural network with recurrent connections, integrated with a 2D musculoskeletal model using Hill-type muscle representations. All components are fully differentiable, allowing for gradient calcula- tions through the system. The results demonstrate that the weights are updated and the performance of the embodied SNN increases as it learns to stabilize the arms angle to zero degrees. Together with the improved motor control behavior, these results indicate that the optimization approach can handle the non-linearities of the muscle model. Spike activity show representative spike firing frequencies during the training process. The system operates within zero memory constraints and has an easily adjustable and well structured software architecture enabling scalability of the system. These findings support gra- dient descent optimization with forward propagation through time as a viable and scalable approach for embodied SNNs in motor control simulations, paving the way for more extensive applications like closed-loop cerebellum modeling. ... Read more

This paper investigates the effect of intrinsic muscle stiffness on neural control parameters in biological musculoskeletal control of stabilisation or reaching tasks. Current model implementations of intrinsic muscle properties are highly simplified, limiting their accuracy in replicating experimental short-range stiffness (SRS) behaviour, which appears to be important for stabilisation tasks. The Hill model, often used in musculoskeletal simulations, cannot account for SRS, while the Huxley model, which can account for non-linear muscle phenomena such as SRS , has a higher computational burden. The study compares a simplified Huxley-type model to two Hill-type models and determines the effect of intrinsic SRS on the control parameters of stabilizing 1- and 2-Degree of Freedom musculoskeletal models over various positive and negative stiffness positions in the force-length curve. Furthermore, the effect of the intrinsic muscle stiffness on the robustness of the feedback parameters of simple individual muscle feedback systems is determined in reaching experiments similar to classic experiments.

The study finds that the Huxley model shows positive SRS in the negative flank of the force-length curve, achieves stabilisation through only co-contraction using a lower level of required muscle excitation than both Hill-type models and stabilises both musculoskeletal systems at a larger muscle range than the Hill-type models, including in the negative stiffness flank. The feedback parameters dominantly responsible for muscle activation patterns are also more robust to change in the Huxley model. These findings suggest that intrinsic muscle stiffness impacts neural control parameters in stabilisation and reaching tasks, and further musculoskeletal modelling should consider using more complex muscle stiffness calculations for improved accuracy. ... Read more

Biologically inspired neural networks are a promising approach to understand the causes and improve the treatments of brain damage. Parkinson's disease is a progressive nervous system disorder that affects mainly movements, speech and cognitive problems. It symptoms cannot be cured, though medications can significantly improve the condition. Among the symptoms, tremor is the only one which remains unaffected by medications and is only responsive to deep-brain stimulation. A simplified, cortico-thalamo-cerebellar model will be simulated with spiking neural networks to evaluate the disease effects under dopamine depletion and connectivity weight changes. Confirming previous findings, striatal dopamine depletion was not found to cause tremor, nor its injection to affect tremor severity. The model showed evidence that parkinsonian weight changes in the pallidal inner feedback loop (GPi-GPe) are responsible of creating a suitable environment for the PD tremor oscillations to rise in the thalamus. Furthermore, both the GPi and the GPe present enhanced maximal activity coherent with muscular co-contraction onsets showing evidence of abnormal basal ganglia firing during re-emergent tremor. These findings may connect abnormal basal ganglia activity to the main parkinsonian motor impairments and may help explaining the beneficial effects of deep-brain stimulation on tremor severity. ... Read more

The olivocerebellar system plays a crucial role in control of movements of the human body in terms of coordination, precision and timing. Long-term plasticity is directly linked to motor learning and control. In this research, we developed a phenomenological model of the olivocerebellar system with balancing of long-term potentiation (LTP) and long-term depression (LTD) at the parallel fiber-Purkinje cell (PF-PC) synapse. By ranging the PF input over frequencies, we found that PCs can select frequencies in a highly non-linear manner. There is a sharp contrast in synaptic weight change between neighbouring frequencies, which is caused by the temporal spiking property of the inferior olive (IO) cell. This research found a novel signal processing capability of the PC. ... Read more

The olivocerebellar system plays a central role in motor learning, crucially contributing to the coordination, precision and accurate timing of movements. The system is formed by Purkinje cells (PC), the Deep Cerebellar Nucleus (DCN) and Inferior Olive (IO). Its input activate the PC which produces simple (SS) and complex spikes (CS). The latter induced by the IO. The oscillatory nature of the production of CS seems to play a role in motor control and motor timing. In addition, CS modulate the parallel fiber-Purkinje cell (PF-PC) synaptic plasticity. IO synchrony can help the system learn timing with the PF input as the timing context. Furthermore, the level of synchronization of the coupled IO cells could determine the function of complex spikes, implying that the olivocerebellum is capable of switching between modes of learning by changing the level of synchronization. In this paper we introduced a novel computational model to analyze the role of coupling of the IO and long-term plasticity at the PF-PC synapse in the response of the olivocerebellar loop. It is a resonant system formed by a detailed IO model and Integrate-and-Fire PC and DCN models, with physiologically observed firing frequencies. The IO cells are modeled as coupled oscillators and plasticity is incorporated through a timing dependent specialization of Hebbian learning (Spike-Timing Dependent Plasticity or STDP). Two different simulations are performed both for the coupled and uncoupled scenarios. The STDP is used only in the second type of simulation. Both types of simulations use the same noisy input, which is applied twice during the second type of simulation. The second half of the latter is interpreted as the response of a trained loop. Results show that in the presence of coupling the correlation of the firing rates distribution decreases. This indicates that for the coupled scenario PCs are separating the patterns, while for the uncoupled scenarios the noise is encoded more robustly. Furthermore, a drop in the noise current (inhibiting the PC) leads to an IO spike at about 100 milliseconds later. After training, however, the loop recognizes a drop in the noise and depresses the synapses avoiding an increase in the firing rate of the PC. This effect is more noticeable in the coupled scenario. In conclusion, the model shows that plasticity can lead to learning, and that this process is more efficient for a coupled system. ... Read more