Data Parallel pattern in Erlang / OpenCL

The goal of this thesis is to provide Erlang with an easy to use, high performance, data parallel skeleton library exploiting GPUs’ processing power. We choose OpenCL to be vendor neutral. The skeletons must be easy to use, possibly be a nearly perfect drop-in replacement to standard Erlang functions with similar semantic (minimal disruption principle). We developed a prototype Erlang library, using OpenCL, implementing the data parallel skeletons to accelerate computations on lists of Erlang floats. The user code for such computations is specified writing OpenCL kernels using the OpenCL C programming language. The library has a very user-friendly API since it is modeled after Erlang’s list module, with which every Erlang programmer is familiar. The outcome of the tests shows, on one hand, a significant performance improvement over a pure Erlang implementation and, on the other hand, an acceptable performance loss over a C++/OpenCL reference implementation

Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task:

Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their wellbeing and their chances of genetic continuation. However, the processes performed in these cycles come at a cost. Such costs force the agent to evaluate a tradeoff between the optimality of the decision making and the time and computational effort required to make it. Several cognitive mechanisms that play critical roles in managing this tradeoff have been identified. These mechanisms include adaptation, learning, memory, attention, and planning. One of the often overlooked outcomes of these cognitive mechanisms, in spite of the critical effect that they may have on the perception-action cycle of organisms, is "emotion." In this study, we hold that emotion can be considered as an emergent phenomenon of a plausible neurocomputational energy regulation mechanism, which generates an internal reward signal to minimize the neural energy consumption of a sequence of actions (decisions), where each action triggers a visual memory recall process. To realize an optimal action selection over a sequence of actions in a visual recalling task, we adopted a model-free reinforcement learning framework, in which the reward signal—that is, the cost—was based on the iteration steps of the convergence state of an associative memory network. The proposed mechanism has been implemented in simulation and on a robotic platform: the iCub humanoid robot. The results show that the computational energy regulation mechanism enables the agent to modulate its behavior to minimize the required neurocomputational energy in performing the visual recalling task

The iCub multisensor datasets for robot and computer vision applications

This document presents novel datasets, constructed by employing the iCub robot equipped with an additional depth sensor and color camera. We used the robot to acquire color and depth information for 210 objects in different acquisition scenarios. At this end, the results were large scale datasets for robot and computer vision applications: object representation, object recognition and classification, and action recognition.Comment: 6 pages, 6 figure

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.journal articl

Embodied bidirectional simulation of a spiking cortico-basal ganglia-cerebellar-thalamic brain model and a mouse musculoskeletal body model distributed across computers including the supercomputer Fugaku

Embodied simulation with a digital brain model and a realistic musculoskeletal body model provides a means to understand animal behavior and behavioral change. Such simulation can be too large and complex to conduct on a single computer, and so distributed simulation across multiple computers over the Internet is necessary. In this study, we report our joint effort on developing a spiking brain model and a mouse body model, connecting over the Internet, and conducting bidirectional simulation while synchronizing them. Specifically, the brain model consisted of multiple regions including secondary motor cortex, primary motor and somatosensory cortices, basal ganglia, cerebellum and thalamus, whereas the mouse body model, provided by the Neurorobotics Platform of the Human Brain Project, had a movable forelimb with three joints and six antagonistic muscles to act in a virtual environment. Those were simulated in a distributed manner across multiple computers including the supercomputer Fugaku, which is the flagship supercomputer in Japan, while communicating via Robot Operating System (ROS). To incorporate models written in C/C++ in the distributed simulation, we developed a C++ version of the rosbridge library from scratch, which has been released under an open source license. These results provide necessary tools for distributed embodied simulation, and demonstrate its possibility and usefulness toward understanding animal behavior and behavioral change

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project) and from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1)

SP8 transcriptional regulation of Cyclin D1 during mouse early corticogenesis

Multiple signals control the balance between proliferation and differentiation of neural progenitor cells during corticogenesis. A key point of this regulation is the control of G1 phase length, which is regulated by the Cyclin/Cdks complexes. Using genome-wide chromatin immunoprecipitation assay and mouse genetics, we have explored the transcriptional regulation of Cyclin D1 (Ccnd1) during the early developmental stages of the mouse cerebral cortex. We found evidence that SP8 binds to the Ccnd1 locus on exon regions. In vitro experiments show SP8 binding activity on Ccnd1 gene 3′-end, and point to a putative role for SP8 in modulating PAX6-mediated repression of Ccnd1 along the dorso-ventral axis of the developing pallium, creating a medialLow-lateralHigh gradient of neuronal differentiation. Activation of Ccnd1 through the promoter/5′-end of the gene does not depend on SP8, but on βcatenin (CTNNB1). Importantly, alteration of the Sp8 level of expression in vivo affects Ccnd1 expression during early corticogenesis. Our results indicate that Ccnd1 regulation is the result of multiple signals and that SP8 is a player in this regulation, revealing an unexpected and potentially novel mechanism of transcriptional activation

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Data parallel patterns in Erlang/OpenCL

We introduce a library supporting execution of data parallel kernels on GPUs from Erlang. The library provides calls with the same semantics of the map and fold functions of the lists Erlang library, where the functions to be computed on the input list(s) are actually provided as OpenCL C kernels. The map and reduce (fold) higher order functions are provided in such a way that subsequent calls may leave temporary data (partial results) on the GPU memory while computing complex, possibly composed data parallel patterns. In addition, data transfers to and from the GPU, from and to the Erlang subsystem, are overlapped with Erlang to C and C to Erlang marshaling, such that the cost of the overall type conversion is minimized. We assess the performances of the data parallel library via simple synthetic benchmarks and real application kernels showing substantial speedups with respect to pure Erlang implementation of the same synthetic benchmarks/application kernel