Fascinating world of Neural Networks
Swedha B
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Abstract
AI
AI
The paper explores the evolution and applications of Artificial Neural Networks (ANNs), detailing historical developments from early theoretical foundations laid by Warren McCulloch and Walter Pitts to contemporary advancements in Deep Learning that have spurred innovations in robotics and machine perception. It highlights significant milestones, including the enhancement of neural network capabilities and their integration into various technologies, illustrating the profound impact of neural networks on computing and artificial intelligence.
Key takeaways
- So, through this article my aim is to educate about Artificial Neural Network and the present stand of this field.
- The model of artificial neural network got inspired from the biological neural network.
- In order to describe how neurons in the brain might work, they also modelled a simple neural network using electrical circuits.
- Now, neural networks are used in several applications.
- The fundamental idea behind the nature of neural networks is that if it works in nature, it must be able to work in computers.
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This chapter conceives the history of neural networks emerging from two millennia of attempts to rationalise and formalise the operation of mind. It begins with a brief review of early classical conceptions of the soul, seating the mind in the heart; then discusses the subsequent Cartesian split of mind and body, before moving to analyse in more depth the twentieth century hegemony identifying mind with brain; the identity that gave birth to the formal abstractions of brain and intelligence we know as 'neural networks'. The chapter concludes by analysing this identity-of intelligence and mind with mere abstractions of neural behaviour-by reviewing various philosophical critiques of formal connectionist explanations of 'human understanding', 'mathematical insight' and 'consciousness'; critiques which, if correct, in an echo of Aristotelian insight, suggest that cognition may be more profitably understood not just as a result of [mere abstractions of] neural firings, but as a consequence of real, embodied neural behaviour, emerging in a brain, seated in a body, embedded in a culture and rooted in our world; the so called 4Es approach to cognitive science: the Embodied, Embedded, Enactive, and Ecological conceptions of mind. Contents 1. Introduction: the body and the brain 2. First steps towards modelling the brain 3. Learning: the optimisation of network structure 4. The fall and rise of connectionism 5. Hopfield networks 6. The 'adaptive resonance theory' classifier 7. The Kohonen 'feature-map' 8. The multi-layer perceptron 9. Radial basis function networks 10. Recent developments in neural networks 11. "What artificial neural networks cannot do .." 12. Conclusions and perspectives Glossary Nomenclature References Biographical sketches
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