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The brain continuously estimates the state of body and environment, with specific regions that are thought to act as Bayesian estimator, optimally integrating noisy and delayed sensory feedback with sensory predictions generated by the cerebellum. In control theory, Bayesian estimators are usually implemented using high-level representations. In this work, we designed a new spike-based computational model of a Bayesian estimator. The state estimator receives spiking activity from two neural populations encoding the sensory feedback and the cerebellar prediction, and it continuously computes the spike variability within each population as a reliability index of the signal these populations encode. The state estimator output encodes the current state estimate. We simulated a reaching task at different stages of cerebellar learning. The activity of the sensory feedback neurons encoded a noisy version of the trajectory after actual movement, with an almost constant intrapopulation spiking variability. Conversely, the activity of the cerebellar output neurons depended on the phase of the learning process. Before learning, they fired at their baseline not encoding any relevant information, and the variability was set to be higher than that of the sensory feedback (more reliable, albeit delayed). When learning was complete, their activity encoded the trajectory before the actual execution, providing an accurate sensory prediction; in this case, the variability was set to be lower than that of the sensory feedback. The state estimator model optimally integrated the neural activities of the afferent populations, so that the output state estimate was primarily driven by sensory feedback in prelearning and by the cerebellar prediction in postlearning. It was able to deal even with more complex scenarios, for example, by shifting the dominant source during the movement execution if information availability suddenly changed. The proposed tool will be a critical block within integrated spiking, brain-inspired control systems for simulations of sensorimotor tasks.

At present, several strategies for preventing neuromuscular pain in Type 2 Diabetes Mellitus (T2DM) have been investigated. Recently, findings on genetic variants associated with adverse events to statin-based therapy have been reported. The study aimed at measuring whether Pharmacogenomics (PGx) profile can affect neuromuscular pain in patients carrying T2DM and cardiovascular diseases. An extensive panel of 5 polymorphisms on 4 candidate genes, previously validated as significant markers related to Sulphonylureas and Glitinides (SU-G) plus Simvastatin neuromuscular toxicity, is herein analyzed and discussed.We genotyped 76 T2DM patients carrying cardiovascular dyscrasia undergone anti-diabetic and anti-cholesterolemic polypharmacy. 35 subjects out of the total received concurrent SU-G and Statin-based therapy. Candidate variants consisted of drug transporters, such as Solute Carrier Organic 1B1 (SLCO1B1) Val174Ala ATP-binding cassette subfamily B member (ABCB1), subfamily C member 8 (ABCC8), and drug biotransformers of Cytochrome P450 Family (CYP) including CYP2C9*2 CYP2C9*3 CYP2C8*3, and CYP3A4*22. Moreover, we also focused on an early outline evaluation of the genotyping costs and benefits.6 out of 35 patients treated with SU-G plus statins (17.1% experienced adverse neuropathy events). Pharmacogenomics analysis showed a lack of any correlation between candidate gene polymorphisms and toxicity, except for the SLCO1B1 T521C allele; 14.3% of patients had a high risk for grade2 neuromuscular pain (Odds Ratio [OR] 2.61.95% CI 0.90-7.61, p=0.03).The clinical polymorphism effectiveness outlined therein will be assured by diagnostic improvements suitable for driving treatment decisions. In light of our experimental results and literature data, the analysis of the SLCO1B1 T521C variant will allow clinicians to take advantage from a better treatment planned for their patients in order to minimize neuromuscular pain and maximize benefits.