scholarly journals An emergentist perspective on the origin of number sense

2018 ◽  
Vol 373 (1740) ◽  
pp. 20170043 ◽  
Author(s):  
Marco Zorzi ◽  
Alberto Testolin
Keyword(s):  
Visual Cues ◽  
Number Sense ◽  
Network Models ◽  
Learning Mechanisms ◽  
Neural Network Models ◽  
Human Infants ◽  
Numerosity Discrimination ◽  
Domain Specific ◽  
Numerical Abilities ◽  
Dependent Learning

The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a ‘nativist’ stance on the origin of number sense. Here, we tackle this issue within the ‘emergentist’ perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietal neurons. We conclude that a form of innatism based on architectural and learning biases is a fruitful approach to understanding the origin and development of number sense. This article is part of a discussion meeting issue ‘The origins of numerical abilities'.

scholarly journals The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms.

1994 ◽  
Vol 1 (1) ◽  
pp. 1-33
Author(s):  
P R Montague ◽  
T J Sejnowski
Keyword(s):  
Synaptic Plasticity ◽  
Temporal Order ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Network Models ◽  
Learning Mechanisms ◽  
Neural Network Models ◽  
Spatial And Temporal Scales ◽  
Synaptic Inputs ◽  
Learning Rules

Some forms of synaptic plasticity depend on the temporal coincidence of presynaptic activity and postsynaptic response. This requirement is consistent with the Hebbian, or correlational, type of learning rule used in many neural network models. Recent evidence suggests that synaptic plasticity may depend in part on the production of a membrane permeant-diffusible signal so that spatial volume may also be involved in correlational learning rules. This latter form of synaptic change has been called volume learning. In both Hebbian and volume learning rules, interaction among synaptic inputs depends on the degree of coincidence of the inputs and is otherwise insensitive to their exact temporal order. Conditioning experiments and psychophysical studies have shown, however, that most animals are highly sensitive to the temporal order of the sensory inputs. Although these experiments assay the behavior of the entire animal or perceptual system, they raise the possibility that nervous systems may be sensitive to temporally ordered events at many spatial and temporal scales. We suggest here the existence of a new class of learning rule, called a predictive Hebbian learning rule, that is sensitive to the temporal ordering of synaptic inputs. We show how this predictive learning rule could act at single synaptic connections and through diffuse neuromodulatory systems.

scholarly journals A continuous-time neural model for sequential action

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130623 ◽  
Author(s):  
George Kachergis ◽  
Dean Wyatte ◽  
Randall C. O'Reilly ◽  
Roy de Kleijn ◽  
Bernhard Hommel
Keyword(s):  
Continuous Time ◽  
Network Models ◽  
Neural Model ◽  
Learning Mechanisms ◽  
Neural Network Models ◽  
Continuous Processes ◽  
Sequential Action ◽  
Directed Learning ◽  
Bidirectional Associations ◽  
Task Representations

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.

scholarly journals ROBIN: A Robust Optical Binary Neural Network Accelerator

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-24
Author(s):  
Febin P. Sunny ◽  
Asif Mirza ◽  
Mahdi Nikdast ◽  
Sudeep Pasricha
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Network Models ◽  
Microring Resonator ◽  
Memory Consumption ◽  
Neural Network Models ◽  
Domain Specific ◽  
Binary Neural Network ◽  
Optical Domain ◽  
Time Overhead

Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNNs), which utilize single-bit weights, represent an efficient way to implement and deploy neural network models on accelerators. In this paper, we present a novel optical-domain BNN accelerator, named ROBIN , which intelligently integrates heterogeneous microring resonator optical devices with complementary capabilities to efficiently implement the key functionalities in BNNs. We perform detailed fabrication-process variation analyses at the optical device level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, our proposed ROBIN architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN models. Our analysis shows that ROBIN can outperform the best-known optical BNN accelerators and many electronic accelerators. Specifically, our energy-efficient ROBIN design exhibits energy-per-bit values that are ∼4 × lower than electronic BNN accelerators and ∼933 × lower than a recently proposed photonic BNN accelerator, while a performance-efficient ROBIN design shows ∼3 × and ∼25 × better performance than electronic and photonic BNN accelerators, respectively.

Size Perception and the Foundation of Numerical Processing

2017 ◽  
Vol 26 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Avishai Henik ◽  
Yarden Gliksman ◽  
Arava Kallai ◽  
Tali Leibovich
Keyword(s):  
Evolutionary History ◽  
Numerical Cognition ◽  
Number Sense ◽  
Size Perception ◽  
Intraparietal Sulcus ◽  
Numerical Processing ◽  
Domain Specific ◽  
Numerical Abilities ◽  
The Brain ◽  
Numerical Knowledge

Research in numerical cognition has led to a widely accepted view of the existence of innate, domain-specific, core numerical knowledge that involves the intraparietal sulcus in the brain. Much of this research has revolved around the ability to perceive and manipulate discrete quantities (e.g., enumeration of dots). We question several aspects of this accepted view and suggest that continuous noncountable dimensions might play an important role in the development of numerical cognition. Accordingly, we propose that a relatively neglected aspect of performance—the ability to perceive and evaluate sizes or amounts—might be an important foundation of numerical processing. This ability might even constitute a more primitive system that has been used throughout evolutionary history as the basis for the development of the number sense and numerical abilities.

scholarly journals Asymmetrical interference between number and item size perception provide evidence for a domain specific impairment in dyscalculia

2018 ◽  
Author(s):  
Elisa Castaldi ◽  
Anne Mirassou ◽  
Stanislas Dehaene ◽  
Manuela Piazza ◽  
Evelyn Eger
Keyword(s):  
Number Sense ◽  
Two Dimensions ◽  
Size Perception ◽  
Alternative View ◽  
Numerosity Discrimination ◽  
Domain Specific ◽  
Control Subjects ◽  
Item Size ◽  
Orthogonal Dimension ◽  
Calculation Ability

AbstractDyscalculia, a specific learning disability that impacts arithmetical skills, has previously been associated to a deficit in the precision of the system that estimates the approximate number of objects in visual scenes (the so called ‘number sense’ system). However, because in tasks involving numerosity comparisons dyscalculics’ judgements appears disproportionally affected by continuous quantitative dimensions (such as the size of the items), an alternative view linked dyscalculia to a domain-general difficulty in inhibiting task-irrelevant responses.To arbitrate between these views, we evaluated the degree of reciprocal interference between numerical and non-numerical quantitative dimensions in adult dyscalculics and matched controls. We used a novel stimulus set orthogonally varying in mean item size and numerosity, putting particular attention into matching both features’ perceptual discriminability. Participants compared those stimuli based on each of the two dimensions. While control subjects showed no significant size interference when judging numerosity, dyscalculics’ numerosity judgments were strongly biased by the unattended size dimension. Importantly however, both groups showed the same degree of interference from number when judging mean size. Moreover, only the ability to discard the irrelevant size information when comparing numerosity (but not the reverse) significantly predicted calculation ability across subjects.Overall, our results show that numerosity discrimination is less prone to interference than discrimination of another quantitative feature (mean item size) when the perceptual discriminability of these features is matched, as here in control subjects. By quantifying, for the first time, dyscalculic subjects’ degree of interference on another orthogonal dimension of the same stimuli, we are able to exclude a domain-general inhibition deficit as explanation for their poor / biased numerical judgement. We suggest that enhanced reliance on non-numerical cues during numerosity discrimination can represent a strategy to cope with a less precise number sense.

scholarly journals Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Amirita Dewani ◽  
Mohsin Ali Memon ◽  
Sania Bhatti
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Network Models ◽  
Linguistic Features ◽  
Neural Network Models ◽  
Solution Strategies ◽  
Domain Specific ◽  
Research Gap ◽  
And Performance ◽  
Cyberbullying Detection

AbstractSocial media have become a very viable medium for communication, collaboration, exchange of information, knowledge, and ideas. However, due to anonymity preservation, the incidents of hate speech and cyberbullying have been diversified across the globe. This intimidating problem has recently sought the attention of researchers and scholars worldwide and studies have been undertaken to formulate solution strategies for automatic detection of cyberaggression and hate speech, varying from machine learning models with vast features to more complex deep neural network models and different SN platforms. However, the existing research is directed towards mature languages and highlights a huge gap in newly embraced resource poor languages. One such language that has been recently adopted worldwide and more specifically by south Asian countries for communication on social media is Roman Urdu i-e Urdu language written using Roman scripting. To address this research gap, we have performed extensive preprocessing on Roman Urdu microtext. This typically involves formation of Roman Urdu slang- phrase dictionary and mapping slangs after tokenization. We have also eliminated cyberbullying domain specific stop words for dimensionality reduction of corpus. The unstructured data were further processed to handle encoded text formats and metadata/non-linguistic features. Furthermore, we performed extensive experiments by implementing RNN-LSTM, RNN-BiLSTM and CNN models varying epochs executions, model layers and tuning hyperparameters to analyze and uncover cyberbullying textual patterns in Roman Urdu. The efficiency and performance of models were evaluated using different metrics to present the comparative analysis. Results highlight that RNN-LSTM and RNN-BiLSTM performed best and achieved validation accuracy of 85.5 and 85% whereas F1 score was 0.7 and 0.67 respectively over aggression class.

scholarly journals Object-scene conceptual regularities reveal fundamental differences between biological and artificial object vision

2021 ◽  
Author(s):  
Stefania Bracci ◽  
Jakob Mraz ◽  
Astrid Zeman ◽  
Gaelle Leys ◽  
Hans Op de Beeck
Keyword(s):  
Neural Networks ◽  
Network Models ◽  
Representational Content ◽  
Human Vision ◽  
Natural Images ◽  
Neural Network Models ◽  
Domain Specific ◽  
Rich Domain ◽  
Specific Object ◽  
Object Segregation

Human vision is still largely unexplained. Computer vision made impressive progress on this front, but it is unclear to what extent artificial neural networks approximate human brain strategies. Here, we confirm this gap by testing how biological and artificial systems encode object-scene contextual regularities in natural images. Both systems represent these regularities, but the underlying information processing is markedly different. In human vision, objects and backgrounds are represented separately, with rich domain-specific representations characterizing human visual cortex. Interaction between these components occurs downstream in frontoparietal areas. Conversely, neural networks represent image components in a single entangled representation revealing reduced object-segregation abilities and impoverished domain-specific object spaces. These results show the uniqueness of human vision that allows understanding that images are not just a collection of features and points to the need for developing neural network models with a similar richness of representational content.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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