Alice Geminiani

Autism Spectrum Disorder is a multi-symptomatic disorder that affects different skills. An embodied mirroring setup involving the NAO robot was implemented as a potential therapy that focus on the social, motor and cognitive skills development of the children with this disorder.

-Since the Marr-Albus model, computational neuroscientists have been developing a variety of models of the cerebellum, with different approaches and features. In this work, we developed and tested realistic artificial Spiking Neural Networks inspired to this brain region. We tested in computational simulations of the Vestibulo-Ocular Reflex protocol three different models: a network equipped with a single plasticity site, at the cortical level; a network equipped with a distributed plasticity, at both cortical and nuclear levels; a network with a pathological plasticity mechanism at the cortical level. We analyzed the learning performance of the three different models, highlighting the behavioral differences among them. We proved that the model with a distributed plasticity produces a faster and more accurate cerebellar response, especially during a second session of acquisition, compared with the single plasticity model. Furthermore, the pathological model shows an impaired learnin...

Multi Electrode Array (MEA) technology allows to non-invasively record neural culture activity, overcoming several limits of traditional neuropharmacological and neurophysiological studies. To obtain reliable results and to ensure experiments reproducibility, it is necessary to maintain the cells in a controlled environment, which allows the proper growth of the culture. For this reason, MEAs recording setups need to be coupled with an environmental parameters control system, keeping CO2 and O2 concentration, temperature, humidity and pH within physiological ranges. In this work, we have validated a bench-top chamber prototype for long-term continuous and parallel recordings from multiple MEAs. We first validated the MEA acquisition chain through the comparison with a commercial gold-standard MEA recording system. Then, we tested a control unit based on environmental parameters sensors. To test the maintenance of optimal environmental conditions for cell cultures, long-term measurem...

The family of disorders commonly known as autism is characterized by a deficit in social interaction and restricted repetitive and stereotyped patterns of behaviours, activities and interests. Motor disturbances are not part of the diagnosis of the children with autism but some studies have estimated that between 80 and 90% of children with Autism Spectrum Disorder (ASD) demonstrate some degree of motor impairments. Several therapies have been used for the improvement of motor skills, always leading to behavioural improvements as side-effects, demonstrating the importance of motor interaction and stimulation for the case of autism. Recent studies have shown that motor, imitation and social abilities are all related in people with autism. In this work, a humanoid robot is used to create a therapy that unites all these areas. The system involves a robot (NAO), a Kinect camera and Personal Computer, with the goal of facilitating the interaction between therapist and a child with ASD du...

Modelling brain networks with complex configuration and cellular properties requires a set of neuroinformatic tools and an organized staged workflow. We have therefore developed the Brain Scaffold Builder (BSB), a new modeling framework embedding multiple strategies for cell placement and connectivity and a flexible management of cellular and network mechanisms. With BSB, for the first time, the mouse cerebellar cortex was reconstructed and simulated at cellular resolution, using morphologically realistic multi-compartmental single-neuron models. Embedded connection rules allowed BSB to generate the cerebellar connectome, unifying a collection of scattered experimental data into a coherent construct. Naturalistic background and sensory-burst stimulation were used for functional validation against recordings in vivo, monitoring the impact of subcellular mechanisms on signal propagation and spatio-temporal processing and providing a new ground-truth about circuit organization for the ...

Socially assistive robots may help the treatment of autism spectrum disorder(ASD), through games using dyadic interactions to train social skills. Existing systems are mainly based on simplified protocols which qualitatively evaluate subject performance. We propose a robotic coaching platform for training social, motor and cognitive capabilities, with two main contributions: (i) using triadic interactions(adult, robot and child), with robotic mirroring, and (ii) providing quantitative performance indicators. The key system features were accurately designed, including type of protocols, feedback systems and evaluation metrics, contemplating the requirements for applications with ASD children. We implemented two protocols, Robot-Master and Adult-Master, where children performed different gestures guided by the robot or the adult respectively, eventually receiving feedback about movement execution. In both, the robot mirrors the subject during the movement. To assess system functionali...

The cerebellar network is renowned for its regular architecture that has inspired foundational computational theories. However, the relationship between circuit structure, function and dynamics remained elusive. To tackle the issue, we have developed an advanced computational modeling framework that allowed us to reconstruct and simulate the structure and function of the mouse cerebellar cortex using morphologically realistic multi-compartmental neuron models. The cerebellar connectome was generated through appropriate connection rules, unifying a collection of scattered experimental data into a coherent construct and providing a new model-based ground-truth about circuit organization. Naturalistic background and sensory-burst stimulation were then used for functional validation against recordings in vivo, monitoring the impact of cellular mechanisms on signal propagation and spatio-temporal processing. Our simulations show, for the first time, how mossy fibers entrain the local neu...

The fundamental role of the cerebellum in motor learning explains the deficits of cerebellar patients in adaptation to a changing environment. For example, lesions to the cerebellar cortex compromise performance during tasks like reaching a target under a force field perturbation. However, the exact relationship between neural damages and misbehaviors still needs to be clarified. To this aim, it could become a turning point to exploit a bio-inspired cerebellar model able to simulate multiple tasks in closed-loop, under physiological and different pathological conditions. In the present study, we used a well-established Spiking Neural Network representing a cerebellar microcomplex to reproduce alterations in a perturbed reaching task, after lesions to the neural population in the cerebellar cortex. Following a multiscale approach, we explored different amounts of damage and analyzed the modified behavior, matching the results of a literature reference study. Then, we could make predictions about the underlying altered neural activity, showing the neural causes of high-level impairments. The results demonstrate the generalization capabilities of the model, extending previous studies on different lesions and tasks. We showed the strong potentialities of computational neuroscience in investigating cerebellar diseases through a non-invasive approach, allowing to isolate damages, test multiple configurations, and suggest treatments thanks to a deeper understanding of pathologies.

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 behav...

The cerebellum is involved in a large number of neural processes, especially in motor control and motor learning. From a functional point of view, the Deep Cerebellar Nuclei (DCN) constitute the core of the cerebellar processing, as they exploit the spatiotemporal filtering action provided by the cerebellar cortex to generate motor corrections. To investigate the contribution of the cerebellar nuclei in motor coordination, in-silico models of the DCN are often exploited in virtual simulations of complex sensorimotor tasks. Since the outcomes of this analysis may depend on the accuracy of the DCN model reconstruction, we here advanced existing spiking neural network models of the cerebellar nuclei. Specifically, the nucle-ocortical pathways, which provide feedback signals back to the cerebellar cortex, have been implemented. This reconstruction required the development of a new neural population in the DCN (Glycinergic-Inactive neurons). Using the neural simulator NEST, Glycinergic-Inactive neurons have been modeled as Extended-Generalized Leaky Integrate and Fire models, while nucleocortical pathways have been implemented considering their anatomical organization and functional behaviour. By exploiting these improvements, the DCN models may be used for more accurate simulations of complex cerebellum-driven tasks, investigating the signal integration process between sensorimotor inputs and cerebellar output internal feedback.

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...

Android robots that are close, but imperfect, likenesses of humans can provoke negative feelings of dislike and eeriness in humans (“Uncanny Valley” effect). We investigated whether category confusion between the perceptual categories of “robot” and “human” contributes to Uncanny Valley aversion. Using a novel, validated corpus of 182 images of real robot and human faces, we precisely estimated the shape of the Uncanny Valley and the location of the perceived robot/human boundary. To implicitly measure confusion, we tracked 358 subjects’ mouse trajectories as they categorized the faces. We observed a clear Uncanny Valley and a pattern of categorization supporting a perceived categorical boundary. Yet, in contrast to predictions of the category confusion mechanism hypothesis, the Uncanny Valley and category boundary locations did not coincide, and mediation analyses further failed to support a causal role of category confusion. These results suggest category confusion does not explai...