Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum

From Frontiers via Jisc Publications RouterHistory: received 2020-10-29, collection 2021, accepted 2021-03-24, epub 2021-05-31Publication status: PublishedThis work presents the first simulation of a large-scale, bio-physically constrained cerebellum model performed on neuromorphic hardware. A model containing 97,000 neurons and 4.2 million synapses is simulated on the SpiNNaker neuromorphic system. Results are validated against a baseline simulation of the same model executed with NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic. Individual cell and network-level spiking activity is validated in terms of average spike rates, relative lead or lag of spike times, and membrane potential dynamics of individual neurons, and SpiNNaker is shown to produce results in agreement with NEST. Once validated, the model is used to investigate how to accelerate the simulation speed of the network on the SpiNNaker system, with the future goal of creating a real-time neuromorphic cerebellum. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Propagation of spiking activity through the network is measured, and will inform the future development of accelerated execution strategies for cerebellum models on neuromorphic hardware. The large ratio of granule cells to other cell types in the model results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. Organizing cells on SpiNNaker in accordance with their spatial position is shown to reduce the peak communication load by 41%. It is hoped that these insights, together with alternative parallelization strategies, will pave the way for real-time execution of large-scale, bio-physically constrained cerebellum models on SpiNNaker. This in turn will enable exploration of cerebellum-inspired controllers for neurorobotic applications, and execution of extended duration simulations over timescales that would currently be prohibitive using conventional computational platforms

Safety of extended interval dosing immune checkpoint inhibitors:a multicenter cohort study

BACKGROUND: Real-life spectrum and survival implications of immune-related adverse events (irAEs) in patients treated with extended interval dosing (ED) immune checkpoint inhibitors (ICIs) are unknown. METHODS: Characteristics of 812 consecutive solid cancer patients who received at least 1 cycle of ED monotherapy (pembrolizumab 400 mg Q6W or nivolumab 480 mg Q4W) after switching from canonical interval dosing (CD; pembrolizumab 200 mg Q3W or nivolumab 240 mg Q2W) or treated upfront with ED were retrieved. The primary objective was to compare irAEs patterns within the same population (before and after switch to ED). irAEs spectrum in patients treated upfront with ED and association between irAEs and overall survival were also described. RESULTS: A total of 550 (68%) patients started ICIs with CD and switched to ED. During CD, 225 (41%) patients developed any grade and 17 (3%) G3 or G4 irAEs; after switching to ED, any grade and G3 or G4 irAEs were experienced by 155 (36%) and 20 (5%) patients. Switching to ED was associated with a lower probability of any grade irAEs (adjusted odds ratio [aOR] = 0.83, 95% confidence interval [CI] = 0.64 to 0.99; P = .047), whereas no difference for G3 or G4 events was noted (aOR = 1.55, 95% CI = 0.81 to 2.94; P = .18). Among patients who started upfront with ED (n = 232, 32%), 107 (41%) developed any grade and 14 (5%) G3 or G4 irAEs during ED. Patients with irAEs during ED had improved overall survival (adjusted hazard ratio [aHR] = 0.53, 95% CI = 0.34 to 0.82; P = .004 after switching; aHR = 0.57, 95% CI = 0.35 to 0.93; P = .025 upfront). CONCLUSIONS: Switching ICI treatment from CD and ED did not increase the incidence of irAEs and represents a safe option also outside clinical trials.</p

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

A parallel neural processor for real-time applications

In this paper we try to identify the most promising way to execute the training and the testing of a Multi Layer Perceptron neural network, by considering the use of a Matlab implementation on a PC and an embedded architecture based either on a general purpose multiprocessor or on a dedicated device. We compared the performance obtained from these three different approaches by first implementing a classification problem of recognising the region of the space to which randomly extracted points belong to and then facing a biomedical signal fitting problem. In both applications the superiority of the dedicated chip in terms of performance was evident. Moreover the dedicated chip uses a training technique, known as Reactive Tabu Search algorithm, which is often more efficient than the Back Propagation approach used in the other solutions

OpenMP and CUDA Simulations of Sella Zerbino Dam Break on Unstructured Grids

This paper presents two 2D dam break parallelized models based on shallow water equations (SWE) written in conservative form. The models were implemented exploiting multicore PC systems and graphics processor unit (GPU) architectures under the OpenMP and the NVIDIA™’s compute unified device architecture (CUDA) frameworks. The mathematical model is solved using a finite-volume technique on an unstructured grid, with Roe’s approximate Riemann solver, a first-order upwind scheme. The upwind treatment of the source terms is implemented. A technique to cope with a wetting-drying advance front is adopted, together with the inclusion of the influence of source terms in the stability constraint in order to prevent negative water depths at the dry fronts. The proposed model is first applied to a laboratory test and then to a real dam break that occurred in Italy in 1935. Results on different grid sizes are compared to show the computing efficiency between the original sequential model and the parallelized models

Flow Rate Profiler: an instrument to measure blood velocity profiles

In this paper we present FRP (Flow Rate Profiler), an instrument for measuring the blood velocity by means of ultrasound-based techniques. The velocity is directly related to the shear-rate, which is in turn proportional to the shear-stress, a parameter expressing the pressure exerted by the blood on the vessel walls. The knowledge of this value is important in medicine to establish the state of the vessels, directly related to vascular diseases. FRP provides a non-invasive measure of the blood velocity by exploiting the red corpuscles property of diffusing ultrasound waves: in practice blood velocity is determined by a cross-correlation technique, which analyses the time shift between correlated subsequent echo waves, instead of frequency shift characteristic of the Doppler technique. The acquired data are then processed on a Personal Computer by means of mathematical techniques based on the evaluation of the correlation function, giving a reconstructed velocity profile and showing a good adherence with experimental data, since the average error is nearly the 10%. The reconstructed profile is displayed to the operator, who can follow the vessel status in real-time. A few comparisons between the reconstructed and the experimental profiles are also presented, together with a study on a small set of patients suffering from artery hypertension

FPGA based accelerators for computing intensive applications

This was a invited talk giving a brief overview about accelerator