Quantifying abstraction in neural networks to increase understanding of human brain processing

Scilight ◽  
2022 ◽  
Vol 2022 (1) ◽  
pp. 011105
Author(s):  
Anne Cockshott
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Brain Processing

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks

scholarly journals The ventral visual pathway represents animal appearance over animacy, unlike human behavior and deep neural networks

2017 ◽  
Author(s):  
Stefania Bracci ◽  
Ioannis Kalfas ◽  
Hans Op de Beeck
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks ◽  
Activity Patterns ◽  
Object Identification ◽  
Visual Appearance ◽  
Learning History ◽  
Fmri Study ◽  
Object Appearance ◽  
Biased Processing

AbstractRecent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here we explore one such bias, namely the bias to perceive animacy, and used the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (how an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow-mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were strongly biased towards object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, this bias interfered with proper object identification, such as failing to signal that a cow-mug is a mug. The bias in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to veridically represent objects when visual appearance is dissociated from animacy, probably due to a biased processing of visual features typical of animate objects.

Psychology of attention

2012 ◽  
pp. 245-249
Author(s):  
Elizabeth Coulthard ◽  
Masud Husain
Keyword(s):  
Human Brain ◽  
Parietal Lobe ◽  
Inferior Frontal Gyrus ◽  
Good Deal ◽  
Anterior Cingulate ◽  
Frontal Eye Field ◽  
Temporoparietal Junction ◽  
Flexible Control ◽  
Eye Field ◽  
Brain Processing

Attention is generally taken to be the process by which people are able to concentrate on certain information or processes, while ignoring other events. It appears to be a fundamental attribute of human brain processing, although difficult to pin down in terms of mechanism. Psychologists have attempted to fractionate attention in many different ways, using ingenious behavioural paradigms. In this section we, too, will consider different aspects of attention: selective, phasic and sustained, divided and executive control of attention. However, it would be fair to say that all these aspects of attention do not normally operate in isolation. Instead they interact, and deficiencies in one aspect of attention, for example, in a patient population, often to do not occur in isolation. Functional imaging and lesion studies of attention have proliferated in recent years, attempting to place a neurobiological framework to these varied processes. In general, these studies also tend to confirm the view that attention is likely an emergent property of widespread brain networks, with a special emphasis on frontal and parietal regions of the human brain (Fig. 2.5.2.1). In this discussion we illustrate several aspects of attention with examples particularly from literature on visual attention, which is the most widely studied area, but it should be appreciated that many of the concepts discussed here extend to other domains. In fact, there is a good deal of evidence to suggest that several aspects of attention operate at a supra- or cross-modal level allowing integration of information from different sources. Recent studies suggest there are two fronto-parietal networks: (Fig. 2.5.2.1) a dorsal parieto-frontal network involving the superior parietal lobe (SPL) and dorsal frontal regions such as the frontal eye field (FEF); and a ventral network involving the inferior parietal lobe (IPL), temporoparietal junction (TPJ) and inferior frontal gyrus (IFG). In addition, dorsomedial frontal areas, including the anterior cingulate cortex (ACC) and pre-supplementary area (pre-SMA) may play a key role in flexible control of attention for strategic behaviour.

scholarly journals Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions

2020 ◽  
Vol 416 ◽  
pp. 38-44
Author(s):  
Emmanouil Giannakakis ◽  
Cheol E. Han ◽  
Bernd Weber ◽  
Frances Hutchings ◽  
Marcus Kaiser
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Human Brain ◽  
Brain Regions ◽  
Long Term Behavior

Use of Neural Networks for Modelling of Passenger Dynamics in Airport Terminal Environment

2014 ◽  
Vol 708 ◽  
pp. 107-112
Author(s):  
Pavlína Hlavsová ◽  
Jaromír Široký
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Flow Control ◽  
Modelling And Simulation ◽  
Mathematics Methods ◽  
Flow Modelling ◽  
Passenger Flow ◽  
Airport Terminal ◽  
Central Nervous Systems ◽  
Modern Mathematics

Neural networks are methods inspired by animals´ central nervous systems, particularly by the human brain. As one of the modern mathematics methods, neural networks have been used to solve a wide variety of both practical and theoretical tasks. The aim of this paper is to illustrate the use of neural networks for modelling of passenger dynamics in the airport terminal environment. This model could be used for passenger flow control, since for the management to be appropriate it should involve passenger dynamics prediction for effective and accurate passenger flow modelling and simulation.

Efficient Luminosity Enhancement in Human Brain Images using Pulse-Coupled Neural Networks

2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Kevin S. Aguilar Domínguez ◽  
Manuel Mejía Lavalle ◽  
Juan Humberto Sossa Azuela
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Brain Images ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks

Efficient Luminosity Enhancement in Human Brain Images using Pulse-Coupled Neural Networks

2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Kevin S. Aguilar Domínguez ◽  
Manuel Mejía Lavalle ◽  
Juan Humberto Sossa Azuela
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Brain Images ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks
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