VirtualEnaction - A Platform for Systemic Neuroscience Simulation

Neurocomputing, 2010

Driven by rapid ongoing advances in computer hardware, neuroscience and computer science, Artificial Brain research and development are blossoming. This article constitutes the first half of a two-part world survey of artificial brain projects: this part dealing with large-scale brain simulations, and the second part with biologically inspired cognitive architectures (BICAs). The large-scale brain simulations we consider in depth here include those by Markram, Modha, Boahen, Horwitz, Edelman, Izhikevich, and Just. As well as reviewing the particulars of these simulation projects, we position them in a broader perspective, comparing at the different underlying definitions of the concept of ''simulation,'' noting that in many ways the projects are modeling neurosystems at different levels as well as using different methodologies.

2006

Abstract- This paper describes two simulators that have been developed as part of Holland’s and Troscianko’s project to build a conscious robot. The first simulates a reconfigurable humanoid robot within a dynamic environment. This robot is more biologically realistic than traditional humanoid robots because its movements are transmitted across its whole structure, which poses the same control problems that brains have to solve with biological systems. The second simulator in this paper simulates large numbers of spiking neurons with a flexibility and speed that makes it ideal for developing models of biologically structured neural networks. In combination these simulators will enable researchers to develop models of how the brain controls the human body and they will also be used to create and test controllers for the real CRONOS robot, on which the virtual robot is based. These tools are currently in their final stage of development and will soon be made freely available for non-c...

IEEE Pulse, 2012

The researchers at Boston University (BU)'s Neuromorphics Laboratory, part of the National Science Foundation (NSF)-sponsored Center of Excellence for Learning in Education, Science, and Technology (CELEST), are working in collaboration with the engineers and scientists at Hewlett-Packard (HP) to implement neural models of intelligent processes for the next generation of dense, low-power, computer hardware that will use memristive technology to bring data closer to the processor where computation occurs. The HP and BU teams are jointly designing an optimal infrastructure, simulation, and software platform to build an artificial brain. The resulting Cog Ex Machina (Cog) software platform has been successfully used to implement a large-scale, multicomponent brain system that is able to simulate some key rat behavioral results in a virtual environment and has been applied to control robotic platforms as they learn to interact with their environment.

worldscinet.com

Whole brain emulation (WBE) has been, for a long time, the subject of interest for many science fiction productions and a subject for debate for many philosophers. This is one of the reasons why many mainstream scientists have remained skeptical about a scientifically sound approach to it. Whole brain emulation might be considered more complex than it actually is because of all the philosophical and methodological issues that people have been pointing out in the last decade. However, not everything that every brain and computer scientist had ever said needs to be taken into account when designing a realistic brain emulation system. In this paper I would like to give a brief overview of the relevant research for WBE coming from the neurosciences and how it can be combined with other disciplines in order to achieve the goals of WBE. Whole brain emulation (WBE) has been, for a long time, the subject of interest for many science fiction productions and a subject for debate for many philosophers. This is one of the reasons why many mainstream scientists have remained skeptical about a scientifically sound approach to it. Whole brain emulation might be considered more complex than it actually is because of all the philosophical and methodological issues that people have been pointing out in the last decade. However, not everything that every brain and computer scientist had ever said needs to be taken into account when designing a realistic brain emulation system. In this paper I would like to give a brief overview of the relevant research for WBE coming from the neurosciences and how it can be combined with other disciplines in order to achieve the goals of WBE.

Frontiers in Neuroinformatics, 2014

Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world's largest functional brain model . Simulating Spaun highlighted limitations in Nengo 1.4's ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.

Science, 2012

A central challenge for cognitive and systems neuroscience is to relate the incredibly complex behavior of animals to the equally complex activity of their brains. Recently described, large-scale neural models have not bridged this gap between neural activity and biological function. In this work, we present a 2.5-million-neuron model of the brain (called "Spaun") that bridges this gap by exhibiting many different behaviors. The model is presented only with visual image sequences, and it draws all of its responses with a physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy, neurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.

1994

Computational Neuroscience is a branch of neuroscience that investigates how the nervous system works, by developing comprehensive simulation models of single neurons or networks that are based on experimental findings. In this paper we describe our efforts in designing a scalable virtual reality system and heuristic-based interface library for use by computational neuroscientists to visualize simulated data from their research 1 . 1 Introduction The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function[Bow92]. As experimental data continues to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists are increasingly recognizing the need for the quantitative approach to exploring the functional consequences of particular neuronal featur...

2016

Neuroscience has become a very broad field indeed: each year around 30,000 researchers and students from around the ... We trace a path from neuron to cognition via computational neuroscience, but what is computational neuroscience?

Can intelligence be produced simply by reverse engineering the brain of an intelligent animal? In this paper, we argue that such reverse engineering will be ineffective if the focus is on reverse-engineering the brain mapping. We believe that the brain “hardware” implements a “system” and our work focuses on emulating this basic brain system and to provide it the necessary interfaces to support a collective intelligence.

Foresight Institute, 2023

Foresight Institute Whole Brain Emulation Workshop Report -- To the best of our understanding, there has not been a significant or comprehensive evaluation of Whole Brain Emulation (WBE) since the 2008 publication of The Future of Humanity Institute’s Whole Brain Emulation Roadmap. Given the recent acceleration in AI development, we believe it’s timely to revisit WBE and its implications for AI Safety. WBE is a potential technology to generate software intelligence that is human-aligned simply by being based directly on human brains. Past discussions generally assumed a lengthy timeline for WBE, whereas AGI timelines had broad uncertainty. There have also been concerns that the neuroscience of WBE might boost AGI capability development without helping safety, although there was no consensus on this issue. Recently many people have updated their AGI timelines towards earlier development, raising safety concerns. This has led some people to consider whether WBE development could be significantly speedup, producing a differential technology development that might lessen the risk of unaligned AGI by the presence of aligned software intelligence