The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States

Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures. Indeed, the human brain includes about 10^15 synapses and 10^11 neurons activated at a mean rate of several Hz. Full brain simulation poses Exascale challenges even if simulated at the highest abstraction level. The WaveScalES experiment in the Human Brain Project (HBP) has the goal of matching experimental measures and simulations of slow waves during deep-sleep and anesthesia and the transition to other brain states. The focus is the development of dedicated large-scale parallel/distributed simulation technologies. The ExaNeSt project designs an ARM-based, low-power HPC architecture scalable to million of cores, developing a dedicated scalable interconnect system, and SWA/AW simulations are included among the driving benchmarks. At the joint between both projects is the INFN proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine. DPSNN can be configured to stress either the networking or the computation features available on the execution platforms. The simulation stresses the networking component when the neural net - composed by a relatively low number of neurons, each one projecting thousands of synapses - is distributed over a large number of hardware cores. When growing the number of neurons per core, the computation starts to be the dominating component for short range connections. This paper reports about preliminary performance results obtained on an ARM-based HPC prototype developed in the framework of the ExaNeSt project. Furthermore, a comparison is given of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server platforms

Neurological involvement in Ile68Leu (p.Ile88Leu) ATTR amyloidosis: not only a cardiogenic mutation

Ile68Leu transthyretin-related amyloidosis (ATTR) is known as a mainly or exclusively cardiogenic variant. We hypothesized that an accurate specialized neurological evaluation could reveal a consistent frequency of mixed phenotypes.Forty-six consecutive subjects with transthyretin (TTR) Ile68Leu (p.Ile88Leu) mutation (29 patients and 17 unaffected carriers) underwent an in-depth cardiac and neurologic evaluation at a single center.All 29 patients showed cardiac involvement. In 20 (69%) cases, it was associated with neurological abnormalities (i.e. a mixed phenotype): 10 (35% of the total) had signs and symptoms of neuropathy, 5 (17%) had abnormalities at the neurologic specialist examination but without symptoms, and 5 (17%) had abnormal nerve conduction study only. None of the asymptomatic carriers showed neurological abnormalities or cardiac involvement. The Neuropathy Impairment Score was5 in seven patients at baseline, and became5 in six more patients during follow-up. The probability of experiencing a major adverse cardiac event (MACE) during follow-up was higher in the mixed than cardiologic phenotype (At least two-thirds of patients with Ile68Leu ATTR and amyloidotic cardiomyopathy show an associated - definite or probable - neurologic impairment of variable degree if accurately evaluated in a neurologic setting. This proportion can rise during follow-up. The mixed phenotype carries a worse prognosis compared to the exclusively cardiologic one. These observations show that more patients could be eligible for treatment with gene silencers than currently indicated and highlight the need for an in-depth and continuous multidisciplinary evaluation of Ile68Leu ATTR patients

Metabolic risk factor profile associated with use of second generation antipsychotics: a cross-sectional study in a community mental health centre

open9noBACKGROUND: Second generation antipsychotics (SGA) have demonstrated several advantages over first generation antipsychotics (FGA) in terms of positive, negative, cognitive, and affective symptoms and a lower propensity for extrapyramidal side effects. Despite these undeniable advantages, SGA have been associated with causing and exacerbating metabolic disorders, such as obesity, diabetes, and hyperlipidemia. This cross sectional study aimed to evaluate the metabolic risk factor profile associated with use of SGAs in comparison with non -treated control patients. METHODS: The study was carried out at a Community Mental Health Centre (CMHC) in Bologna. The study subjects were outpatients with serious mental disorders treated with SGA (clozapine, olanzapine, risperidone, quetiapine). A sample of adult men and women suffering from idiopathic hyperhydrosis, without psychiatric history or antipsychotic treatment, were randomly selected from outpatients of the Department of Neurology in Bologna as a reference group. We investigated differences among the treatment and reference groups for glycaemia, cholesterolaemia and triglyceridaemia levels. RESULTS: The study sample was composed of 76 patients, 38 males and 38 females. The reference group was composed of 36 subjects, 19 females and 17 males. All patients treated with SGAs had higher mean glycaemia and triglyceridaemia and a significantly higher risk of receiving a diagnosis of hyperglycaemia and hypertriglyceridaemia than the reference group. We did not find any differences in mean glycaemia or mean triglyceridaemia levels among treatment groups. Patients with clozapine had a significantly higher mean BMI value and rate of obesity than patients treated with other SGAs. CONCLUSION: The rate of obesity and metabolic disorders observed in this study were higher than the prevalence in the control group and similar to that previously reported in psychiatric samples; these findings imply per se that more attention should be paid to the metabolic condition of psychiatric patients. In line with the International Consensus Conferences we recommend that monitoring of weight, fasting plasma glucose, cholesterol and triglyceride levels be obtained in routine clinical practice with all antipsychoticsopenTarricone I.; Casoria M.; Ferrari Gozzi B.; Grieco D.; Menchetti M.; Serretti A.; Ujkaj M.; Pastorelli F.; Berardi D.Tarricone I.; Casoria M.; Ferrari Gozzi B.; Grieco D.; Menchetti M.; Serretti A.; Ujkaj M.; Pastorelli F.; Berardi D

Real-time cortical simulations: energy and interconnect scaling on distributed systems

We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the speed and energy consumption of processor architectures typical of standard HPC and embedded platforms are compared. We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption. The cost of cortical simulations is quantified using the Joule per synaptic event metric on both architectures. Reaching efficient real-time on large scale cortical simulations is of increasing relevance for both future bio-inspired artificial intelligence applications and for understanding the cognitive functions of the brain, a scientific quest that will require to embed large scale simulations into highly complex virtual or real worlds. This work stands at the crossroads between the WaveScalES experiment in the Human Brain Project (HBP), which includes the objective of large scale thalamo-cortical simulations of brain states and their transitions, and the ExaNeSt and EuroExa projects, that investigate the design of an ARM-based, low-power High Performance Computing (HPC) architecture with a dedicated interconnect scalable to million of cores; simulation of deep sleep Slow Wave Activity (SWA) and Asynchronous aWake (AW) regimes expressed by thalamo-cortical models are among their benchmarks.Comment: 8 pages, 8 figures, 4 tables, submitted after final publication on PDP2019 proceedings, corrected final DOI. arXiv admin note: text overlap with arXiv:1812.04974, arXiv:1804.0344