scholarly journals Analysis of Neuromorphic Computing Systems and its Applications in Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 5309-5312
Author(s):  
Pranava Bhat
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Neural Systems ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
The Past ◽  
Computational Capability ◽  
Biological World ◽  
Areas Of Interest ◽  
The Brain

The domain of engineering has always taken inspiration from the biological world. Understanding the functionalities of the human brain is one of the key areas of interest over time and has caused many advancements in the field of computing systems. The computational capability per unit power per unit volume of the human brain exceeds the current best supercomputers. Mimicking the physics of computations used by the nervous system and the brain can bring a paradigm shift to the computing systems. The concept of bridging computing and neural systems can be termed as neuromorphic computing and it is bringing revolutionary changes in the computing hardware. Neuromorphic computing systems have seen swift progress in the past decades. Many organizations have introduced a variety of designs, implementation methodologies and prototype chips. This paper discusses the parameters that are considered in the advanced neuromorphic computing systems and the tradeoffs between them. There have been attempts made to make computer models of neurons. Advancements in the hardware implementation are fuelling the applications in the field of machine learning. This paper presents the applications of these modern computing systems in Machine Learning.

On the Limits of Time in the Brain

KronoScope ◽  
2013 ◽  
Vol 13 (2) ◽  
pp. 228-239
Author(s):  
Rémy Lestienne
Keyword(s):  
Human Brain ◽  
Brain Functions ◽  
Inner Sense ◽  
The Past ◽  
Intellectual Abilities ◽  
The Future ◽  
Flow Of Time ◽  
Nested Hierarchy ◽  
Future Outcomes ◽  
The Brain

Abstract J.T. Fraser used to emphasize the uniqueness of the human brain in its capacity for apprehending the various dimensions of “nootemporality” (Fraser 1982 and 1987). Indeed, our brain allows us to sense the flow of time, to measure delays, to remember past events or to predict future outcomes. In these achievements, the human brain reveals itself far superior to its animal counterpart. Women and men are the only beings, I believe, who are able to think about what they will do the next day. This is because such a thought implies three intellectual abilities that are proper to mankind: the capacity to take their own thoughts as objects of their thinking, the ability of mental time travels—to the past thanks to their episodic memory or to the future—and the possibility to project very far into the future, as a consequence of their enlarged and complexified forebrain. But there are severe limits to our timing abilities of which we are often unaware. Our sensibility to the passing time, like other of our intellectual abilities, is often competing with other brain functions, because they use at least in part the same neural networks. This is particularly the case regarding attention. The deeper the level of attention required, the looser is our perception of the flow of time. When we pay attention to something, when we fix our attention, then our inner sense of the flux of time freezes. This limitation should not sound too unfamiliar to the reader of J.T. Fraser who wrote in his book Time, Conflict, and Human Values (1999) about “time as a nested hierarchy of unresolvable conflicts.”

scholarly journals Predictability changes what we remember in familiar temporal contexts

2018 ◽  
Author(s):  
Hyojeong Kim ◽  
Margaret L. Schlichting ◽  
Alison R. Preston ◽  
Jarrod A. Lewis-Peacock
Keyword(s):  
Human Brain ◽  
Pattern Classification ◽  
Prediction Error ◽  
Fmri Data ◽  
The Past ◽  
The Future ◽  
Abstract Level ◽  
Episodic Memories ◽  
The Brain

AbstractThe human brain constantly anticipates the future based on memories of the past. Encountering a familiar situation reactivates memory of previous encounters which can trigger a prediction of what comes next to facilitate responsiveness. However, a prediction error can lead to pruning of the offending memory, a process that weakens its representation in the brain and leads to forgetting. Our goal in this study was to evaluate whether memories are spared from pruning in situations that allow for more abstract yet reliable predictions. We hypothesized that when the category, but not the identity, of a new stimulus can be anticipated, this will reduce pruning of existing memories and also reduce encoding of the specifics of new memories. Participants viewed a sequence of objects, some of which reappeared multiple times (“cues”), followed always by novel items. Half of the cues were followed by new items from different (unpredictable) categories, while others were followed by new items from a single (predictable) category. Pattern classification of fMRI data was used to identify category-specific predictions after each cue. Pruning was observed only in unpredictable contexts, while encoding of new items suffered more in predictable contexts. These findings demonstrate that how episodic memories are updated is influenced by the reliability of abstract-level predictions in familiar contexts.

scholarly journals Watershed Brain Regions for Characterizing Brand Equity-Related Mental Processes

2021 ◽  
Vol 11 (12) ◽  
pp. 1619
Author(s):  
Shinya Watanuki
Keyword(s):  
Brand Equity ◽  
Meta Analysis ◽  
Brain Regions ◽  
Parahippocampal Gyrus ◽  
Neural Systems ◽  
Mental Processes ◽  
Chi Square ◽  
The Past ◽  
Lingual Gyrus ◽  
The Brain

Brand equity is an important intangible for enterprises. As one advantage, products with brand equity can increase revenue, compared with those without such equity. However, unlike tangibles, it is difficult for enterprises to manage brand equity because it exists within consumers’ minds. Although, over the past two decades, numerous consumer neuroscience studies have revealed the brain regions related to brand equity, the identification of unique brain regions related to such equity is still controversial. Therefore, this study identifies the unique brain regions related to brand equity and assesses the mental processes derived from these regions. For this purpose, three analysis methods (i.e., the quantitative meta-analysis, chi-square tests, and machine learning) were conducted. The data were collected in accordance with the general procedures of a qualitative meta-analysis. In total, 65 studies (1412 foci) investigating branded objects with brand equity and unbranded objects without brand equity were examined, whereas the neural systems involved for these two brain regions were contrasted. According to the results, the parahippocampal gyrus and the lingual gyrus were unique brand equity-related brain regions, whereas automatic mental processes based on emotional associative memories derived from these regions were characteristic mental processes that discriminate branded from unbranded objects.

scholarly journals Computational modelling of social cognition and behaviour—a reinforcement learning primer

2020 ◽  
Author(s):  
Patricia L Lockwood ◽  
Miriam C Klein-Flügge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Abstract Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

Expanding perspectives on cognition in humans, animals, and machines

2016 ◽  
Author(s):  
Alex Gomez-Marin ◽  
Zachary F Mainen
Keyword(s):  
Machine Learning ◽  
Dynamical Systems ◽  
Molecular Biology ◽  
Future Research ◽  
Learning Behavior ◽  
The Past ◽  
The Mind ◽  
The Brain

Over the past decade neuroscience has been attacking the problem of cognition with increasing vigor. Yet, what exactly is cognition, beyond a general signifier of anything seemingly complex the brain does? Here, we briefly review attempts to define, describe, explain, build, enhance and experience cognition. We highlight perspectives including psychology, molecular biology, computation, dynamical systems, machine learning, behavior and phenomenology. This survey of the landscape reveals not a clear target for explanation but a pluralistic and evolving scene with diverse opportunities for grounding future research. We argue that rather than getting to the bottom of it, over the next century, by deconstructing and redefining cognition, neuroscience will and should expand rather than merely reduce our concept of the mind.

Maintaining Identity

2030 ◽  
2010 ◽  
Author(s):  
Rutger van Santen ◽  
Djan Khoe ◽  
Bram Vermeer
Keyword(s):  
Human Brain ◽  
Early Adolescence ◽  
Early Age ◽  
Modern Life ◽  
Story Line ◽  
The Past ◽  
The World ◽  
The Moment ◽  
The Brain ◽  
Machine Operator

Baroness Susan Greenfield’s origins are humbler than her title might suggest. Her father was a machine operator in an industrial neighbour-hood of London. In Britain, unlike many other countries, it is possible to earn a peerage through your own merits rather than pure heredity. Lady Greenfield is a leading world authority on the human brain. She is concerned that technology has invaded our lives so profoundly that it has begun to affect the way our brains operate and hence our very personalities. “People are longing for experiences rather than searching for meaning,” she says. “They live more in the moment and have less of a sense of the narrative of their lives—of continuity. They lack a sense of having a beginning, a middle, and an end. They have less of a feeling that they are developing an identity throughout their life with a continuing story line from childhood, youth, parenthood, to grandparenthood. The emphasis is more on process than content. You now have people who are much more ‘sensitive’ rather than ‘cognitive.’ ” Susan Greenfield identifies one of the causes of this development as the impressions our brains receive from a very early age. Modern life, she argues, with its hectic rhythm of visual impressions is very different from the past, in which she includes her own childhood in the 1950s and 1960s. It’s in our youth that our brains are shaped: They grow like mad during the first 2 years of life, developing a maze of connections. And in the years that follow, they remain extremely nimble, forming new connections rapidly and changing in response to our surroundings. It is very much the world around us during infancy, childhood, and early adolescence that determines the outcome of this stage of brain formation. The brain displays an immense degree of what Greenfield likes to call “plasticity” during this stage; connections are formed as and when they are needed. The foundations of Baroness Greenfield’s own personality were laid in a similar way during her youth.

scholarly journals Predictability Changes What We Remember in Familiar Temporal Contexts

2020 ◽  
Vol 32 (1) ◽  
pp. 124-140 ◽  
Author(s):  
Hyojeong Kim ◽  
Margaret L. Schlichting ◽  
Alison R. Preston ◽  
Jarrod A. Lewis-Peacock
Keyword(s):  
Human Brain ◽  
Prediction Error ◽  
Fmri Data ◽  
Prediction Errors ◽  
Associative Memories ◽  
The Past ◽  
Item Level ◽  
Abstract Level ◽  
The Brain

The human brain constantly anticipates the future based on memories of the past. Encountering a familiar situation reactivates memory of previous encounters, which can trigger a prediction of what comes next to facilitate responsiveness. However, a prediction error can lead to pruning of the offending memory, a process that weakens its representation in the brain and leads to forgetting. Our goal in this study was to evaluate whether memories are spared from such pruning in situations that allow for accurate predictions at the categorical level, despite prediction errors at the item level. Participants viewed a sequence of objects, some of which reappeared multiple times (“cues”), followed always by novel items. Half of the cues were followed by new items from different (unpredictable) categories, while others were followed by new items from a single (predictable) category. Pattern classification of fMRI data was used to identify category-specific predictions after each cue. Pruning was observed only in unpredictable contexts, while encoding of new items was less robust in predictable contexts. These findings demonstrate that how associative memories are updated is influenced by the reliability of abstract-level predictions in familiar contexts.

scholarly journals Computers That Look Like the Brain

2020 ◽  
Vol 8 ◽  
Author(s):  
Shahar Kvatinsky
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
The Past ◽  
Computer Structure ◽  
The Brain ◽  
The Way

Artificial intelligence applications have been developing rapidly over the past few years, allowing computers to perform complex actions, such as driving without a driver, making decisions, and recognizing faces. These applications require that many calculations be performed in parallel and immense amounts of information are needed. This article demonstrates how inefficient today’s computer structure is for performing artificial intelligence applications. To deal with this challenge and improve artificial intelligence applications, we will see how inspiration from the way the human brain works will allow us to build completely new computers, which will rock the way computers have been built for many years.

scholarly journals Computational modelling of social cognition and behaviour – a reinforcement learning primer

2019 ◽  
Author(s):  
Patricia Lockwood ◽  
Miriam Klein-Flugge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalising and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

scholarly journals Analysis of Brain Tumor Disease Detection Using Convolutional Neural Network

2021 ◽  
Author(s):  
Yogesh Deshmukh ◽  
Samiksha Dahe ◽  
Tanmayeeta Belote ◽  
Aishwarya Gawali ◽  
Sunnykumar Choudhary
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumor ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Vital Role ◽  
Machine Learning Algorithm ◽  
Different Types ◽  
The Brain

Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.

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