A Multiple-Plasticity Spiking Neural Network Embedded in a Closed-Loop Control System to Model Cerebellar Pathologies

The cerebellum plays a crucial role in sensorimotor control and cerebellar disorders compromise adaptation and learning of motor responses. However, the link between alterations at network level and cerebellar dysfunction is still unclear. In principle, this understanding would benefit of the development of an artificial system embedding the salient neuronal and plastic properties of the cerebellum and operating in closed-loop. To this aim, we have exploited a realistic spiking computational model of the cerebellum to analyze the network correlates of cerebellar impairment. The model was modified to reproduce three different damages of the cerebellar cortex: (i) a loss of the main output neurons (Purkinje Cells), (ii) a lesion to the main cerebellar afferents (Mossy Fibers), and (iii) a damage to a major mechanism of synaptic plasticity (Long Term Depression). The modified network models were challenged with an Eye-Blink Classical Conditioning test, a standard learning paradigm used to evaluate cerebellar impairment, in which the outcome was compared to reference results obtained in human or animal experiments. In all cases, the model reproduced the partial and delayed conditioning typical of the pathologies, indicating that an intact cerebellar cortex functionality is required to accelerate learning by transferring acquired information to the cerebellar nuclei. Interestingly, depending on the type of lesion, the redistribution of synaptic plasticity and response timing varied greatly generating specific adaptation patterns. Thus, not only the present work extends the generalization capabilities of the cerebellar spiking model to pathological cases, but also predicts how changes at the neuronal level are distributed across the network, making it usable to infer cerebellar circuit alterations occurring in cerebellar pathologies

Rom, Italien. Auf dem Weg zur Rekonstruktion eines mittelrepublikanischen Tempels. Die Forschungen von 2020 am Largo Argentina in Rom

Despite the challenges of the pandemic, the collaborative project at Largo Argentina in Rome was able to make a significant step forward towards the reconstruction of the Mid Republican phase of Temple A. Its extant remains, which are hidden in an artificial underground area, were documented in a 3D model (SfM). It provides the basis for analytical architectural drawings. Moreover, it was possible to launch the study of the architectural terracotta from the site. A review of the excavator’s archival documents, in conjunction with an examination of the material, allowed preliminary but promising results in identifying parts of the roof decoration of Temple A

The response of high latitude ionosphere to the 2015 June 22 storm

This work investigates physical mechanisms triggering phase scintillations on L-band signals under strong stormy conditions. Thanks to selected ground-based Global Navigation Satellite Systems (GNSS) receivers, located both in Antarctica and in the Arctic, an interhemispheric comparison between high latitude ionospheric observations in response to the peculiar solar wind conditions occurred on June 22, 2015 is here shown. To trace back the observed phase scintillations to the physical mechanisms driving it, we combine measurements from GNSS receivers with in-situ and ground-based observations. Our study highlights the ionospheric scenario in which irregularities causing scintillation form and move, leveraging on a multi-observation approach. Such approach allows deducing that scintillations are caused by the presence of fast-moving electron density gradients originated by particle precipitation induced by solar wind variations. In addition, we show how the numerous and fast oscillations of the north-south component of the interplanetary magnetic field (Bz,IMF) result to be less effective in producing moderate/intense scintillation events than during period of long lasting negative values. Finally, we also demonstrate how the in-situ electron density data can be used to reconstruct the evolution of the ionospheric dynamics, both locally and globally

Emergence of associative learning in a neuromorphic inference network

OBJECTIVE: In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH: We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS: Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE: These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence

Model-driven analysis of eyeblink classical conditioning reveals the underlying structure of cerebellar plasticity and neuronal activity

The cerebellum plays a critical role in sensorimotor control. However, how the specific circuits and plastic mechanisms of the cerebellum are engaged in closed-loop processing is still unclear. We developed an artificial sensorimotor control system embedding a detailed spiking cerebellar microcircuit with three bidirectional plasticity sites. This proved able to reproduce a cerebellar-driven associative paradigm, the eyeblink classical conditioning (EBCC), in which a precise time relationship between an unconditioned stimulus (US) and a conditioned stimulus (CS) is established. We challenged the spiking model to fit an experimental data set from human subjects. Two subsequent sessions of EBCC acquisition and extinction were recorded and transcranial magnetic stimulation (TMS) was applied on the cerebellum to alter circuit function and plasticity. Evolutionary algorithms were used to find the near-optimal model parameters to reproduce the behaviors of subjects in the different sessions of the protocol. The main finding is that the optimized cerebellar model was able to learn to anticipate (predict) conditioned responses with accurate timing and success rate, demonstrating fast acquisition, memory stabilization, rapid extinction, and faster reacquisition as in EBCC in humans. The firing of Purkinje cells (PCs) and deep cerebellar nuclei (DCN) changed during learning under the control of synaptic plasticity, which evolved at different rates, with a faster acquisition in the cerebellar cortex than in DCN synapses. Eventually, a reduced PC activity released DCN discharge just after the CS, precisely anticipating the US and causing the eyeblink. Moreover, a specific alteration in cortical plasticity explained the EBCC changes induced by cerebellar TMS in humans. In this paper, for the first time, it is shown how closed-loop simulations, using detailed cerebellar microcircuit models, can be successfully used to fit real experimental data sets. Thus, the changes of the model parameters in the different sessions of the protocol unveil how implicit microcircuit mechanisms can generate normal and altered associative behaviors

Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms

In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. Methods: By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal plasticity mechanism parameters that better reproduced human-like behavior in eye blink classical conditioning, one of the most extensively studied paradigms related to the cerebellum. We used two models: one with only the cortical plasticity and another including two additional plasticity sites at nuclear level. Results: First, both spiking cerebellar models were able to well reproduce the real human behaviors, in terms of both "timing" and "amplitude", expressing rapid acquisition, stable late acquisition, rapid extinction, and faster reacquisition of an associative motor task. Even though the model with only the cortical plasticity site showed good learning capabilities, the model with distributed plasticity produced faster and more stable acquisition of conditioned responses in the reacquisition phase. This behavior is explained by the effect of the nuclear plasticities, which have slow dynamics and can express memory consolidation and saving. Conclusions: We showed how the spiking dynamics of multiple interactive neural mechanisms implicitly drive multiple essential components of complex learning processes. Significance: This study presents a very advanced computational model, developed together by biomedical engineers, computer scientists, and neuroscientists. Since its realistic features, the proposed model can provide confirmations and suggestions about neurophysiological and pathological hypotheses and can be used in challenging clinical application

Constant rate infusion of diazepam or propofol for the management of canine cluster seizures or status epilepticus

INTRODUCTION: Cluster seizures (CS) and status epilepticus (SE) in dogs are severe neurological emergencies that require immediate treatment. Practical guidelines call for constant rate infusion (CRI) of benzodiazepines or propofol (PPF) in patients with seizures not responding to first-line treatment, but to date only few studies have investigated the use of CRI in dogs with epilepsy. STUDY DESIGN: Retrospective clinical study. METHODS: Dogs that received CRI of diazepam (DZP) or PPF for antiepileptic treatment during hospitalization at the Veterinary Teaching Hospital of the University of Turin for CS or SE between September 2016 and December 2019 were eligible for inclusion. Favorable outcome was defined as cessation of clinically visible seizure activity within few minutes from the initiation of the CRI, no seizure recurrence within 24 h after discontinuation of CRI through to hospital discharge, and clinical recovery. Poor outcome was defined as recurrence of seizure activity despite treatment or death in hospital because of recurrent seizures, catastrophic consequences of prolonged seizures or no return to an acceptable neurological and clinical baseline, despite apparent control of seizure activity. Comparisons between the number of patients with favorable outcome and those with poor outcome in relation to type of CRI, seizure etiology, reason for presentation (CS or SE), sex, previous AED therapy and dose of PPF CRI were carried out. RESULTS: A total of 37 dogs, with 50 instances of hospitalization and CRI administered for CS or SE were included in the study. CRI of diazepam (DZP) or PPF was administered in 29/50 (58%) and in 21/50 (42%) instances of hospitalization, respectively. Idiopathic epilepsy was diagnosed in 21/37 (57%), (13/21 tier I and 8/21 tier II); structural epilepsy was diagnosed in 6/37 (16%) of which 4/6 confirmed and 2/6 suspected. A metabolic or toxic cause of seizure activity was recorded in 7/37 (19%). A total of 38/50 (76%) hospitalizations were noted for CS and 12/50 (24%) for SE. In 30/50 (60%) instances of hospitalization, the patient responded well to CRI with cessation of seizure activity, no recurrence in the 24 h after discontinuation of CRI through to hospital discharge, whereas a poor outcome was recorded for 20/50 (40%) cases (DZP CRI in 12/50 and PPF CRI in 8/50). Comparison between the number of patients with favorable outcome and those with poor outcome in relation to type of CRI, seizure etiology, reason for presentation (CS or SE), sex and previous AED therapy was carried out but no statistically significant differences were found. CONCLUSIONS: The present study is the first to document administration of CRI of DZP or PPF in a large sample of dogs with epilepsy. The medications appeared to be tolerated without major side effects and helped control seizure activity in most patients regardless of seizure etiology. Further studies are needed to evaluate the effects of CRI duration on outcome and complications