The Brain Works by Logic

2018 ◽  
Author(s):  
James A. Anderson
Keyword(s):  
Digital Computer ◽  
Brain Function ◽  
Action Potentials ◽  
Large Scale ◽  
Logic Function ◽  
John Von Neumann ◽  
Von Neumann ◽  
Scale Theory ◽  
Logic Functions ◽  
The Brain

Brains and computers were twins separated at birth. In 1943, it was known that action potentials were all or none, approximating TRUE or FALSE. In that year, Walter Pitts and Warren McCulloch wrote a paper suggesting that neurons were computing logic functions and that networks of such neurons could compute any finite logic function. This was a bold and exciting large-scale theory of brain function. Around the same time, the first digital computer, the ENIAC, was being built. The McCulloch-Pitts work was well known to the scientists building ENIAC. The connection between them appeared explicitly in a report by John von Neumann on the successor to the ENIAC, the EDVAC. It soon became clear that biological brain computation was not based on logic functions. However, this idea was believed by many scientists for decades. A brilliant wrong theory can sometimes cause trouble.

Implantable neural interfaces

2018 ◽  
Author(s):  
Stefano Vassanelli
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Ionic Channels ◽  
Patch Clamp Technique ◽  
Advantages And Disadvantages ◽  
Optogenetic Stimulation ◽  
Physical Interfaces ◽  
The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

Reconfigurable nonvolatile boolean logic with one-transistor-two-memristor for in-memory computing

2021 ◽  
Author(s):  
Ziling Wang ◽  
Li Luo ◽  
Jie Li ◽  
Lidan Wang ◽  
shukai duan
Keyword(s):  
Large Scale ◽  
Full Adder ◽  
Boolean Logic ◽  
Device Structure ◽  
Von Neumann ◽  
Computing Paradigm ◽  
Operation Process ◽  
Strong Candidate ◽  
High Flexibility ◽  
Logic Functions

Abstract In-memory computing is highly expected to break the von Neumann bottleneck and memory wall. Memristor with inherent nonvolatile property is considered to be a strong candidate to execute this new computing paradigm. In this work, we have presented a reconfigurable nonvolatile logic method based on one-transistor-two-memristor (1T2M) device structure, inhibiting the sneak path in the large-scale crossbar array. By merely adjusting the applied voltage signals, all 16 binary Boolean logic functions can be achieved in a single cell. More complex computing tasks including one-bit parallel full adder and Set-Reset latch have also been realized with optimization, showing simple operation process, high flexibility, and low computational complexity. The circuit verification based on cadence PSpice simulation is also provided, proving the feasibility of the proposed design. The work in this paper is intended to make progress in constructing architectures for in-memory computing paradigm.

scholarly journals Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

2020 ◽  
Author(s):  
Paul Triebkorn ◽  
Joelle Zimmermann ◽  
Leon Stefanovski ◽  
Dipanjan Roy ◽  
Ana Solodkin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Model Parameters ◽  
Network Modelling ◽  
Neural Populations ◽  
Global Coupling ◽  
The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

The Computer and the Brain. John von Neumann

Isis ◽  
1960 ◽  
Vol 51 (1) ◽  
pp. 94-96 ◽  
Author(s):  
A. H. Taub
Keyword(s):  
John Von Neumann ◽  
Von Neumann ◽  
The Brain

scholarly journals A macaque connectome for large-scale network simulations in TheVirtualBrain

2018 ◽  
Author(s):  
Kelly Shen ◽  
Gleb Bezgin ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Stefan Everling ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Function ◽  
Large Scale ◽  
Spatial And Temporal Scales ◽  
Multi Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Simulations ◽  
Dynamical Function ◽  
The Brain

AbstractModels of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

scholarly journals Memristive Artificial Synapses for Neuromorphic Computing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wen Huang ◽  
Xuwen Xia ◽  
Chen Zhu ◽  
Parker Steichen ◽  
Weidong Quan ◽  
...  
Keyword(s):  
Brain Function ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Optical Signals ◽  
Synaptic Functions ◽  
Neuromorphic Systems ◽  
Von Neumann Architecture ◽  
The Brain ◽  
Stimulation Of ◽  
Working Mechanisms

AbstractNeuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

scholarly journals Action Video Game Experience Related to Altered Large-Scale White Matter Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Diankun Gong ◽  
Weiyi Ma ◽  
Jinnan Gong ◽  
Hui He ◽  
Li Dong ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Deficits ◽  
Video Game ◽  
Neural Plasticity ◽  
Action Potentials ◽  
Large Scale ◽  
Brain Regions ◽  
Research Attention ◽  
Cognitive Benefits ◽  
The Brain

With action video games (AVGs) becoming increasingly popular worldwide, the cognitive benefits of AVG experience have attracted continuous research attention over the past two decades. Research has repeatedly shown that AVG experience can causally enhance cognitive ability and is related to neural plasticity in gray matter and functional networks in the brain. However, the relation between AVG experience and the plasticity of white matter (WM) network still remains unclear. WM network modulates the distribution of action potentials, coordinating the communication between brain regions and acting as the framework of neural networks. And various types of cognitive deficits are usually accompanied by impairments of WM networks. Thus, understanding this relation is essential in assessing the influence of AVG experience on neural plasticity and using AVG experience as an interventional tool for impairments of WM networks. Using graph theory, this study analyzed WM networks in AVG experts and amateurs. Results showed that AVG experience is related to altered WM networks in prefrontal networks, limbic system, and sensorimotor networks, which are related to cognitive control and sensorimotor functions. These results shed new light on the influence of AVG experience on the plasticity of WM networks and suggested the clinical applicability of AVG experience.

scholarly journals Pan-neuro: interactive computing at scale with BRAIN datasets

2021 ◽  
Author(s):  
Ariel Rokem ◽  
Ben Dichter ◽  
Christopher Holdgraf ◽  
Satrajit S Ghosh
Keyword(s):  
Open Source Software ◽  
Brain Function ◽  
Large Scale ◽  
Universal Access ◽  
The Public ◽  
Software Libraries ◽  
Interactive Computing ◽  
The Earth ◽  
Scientific Institutions ◽  
The Brain

New technical and scientific breakthroughs are enabling neuroscientific measurements that are both wider in scope and denser in their sampling, providing views of the brain that have not been possible before. At the same time, funding initiatives, as well as scientific institutions and communities are promoting sharing of neuroscientific data. These factors are creating a deluge of neuroscience data that promises to provide new and meaningful insights into brain function. However, the size, complexity, and identifiability of the data also present challenges that arise from the difficulties in storing, accessing, processing, analyzing, visualizing and understanding data at large scale. Based on their successful adoption in the earth sciences, we have started adopting and adapting a set of tools for interactive scalable computing in neuroscience. We are building an approach that is based on a combination of a vibrant ecosystem of open-source software libraries and standards, coupled with the massive computational power of the public cloud, and served through interactive browser-based Jupyter interfaces. Together, these could provide uniform universal access to datasets for flexible and scalable exploration and analysis. We present a few prototype use-cases of this approach. We identify barriers and technical challenges that still need to be addressed to facilitate wider deployment of this approach and full exploitation of its advantages.

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