This document presents a seminar on artificial neural networks. It discusses how neural networks are faster than digital computers due to their parallel and adaptive nature, like the human brain. It covers characteristics of artificial neural networks like learning by example and generalization. The backpropagation algorithm for training neural networks is explained. Applications of neural networks discussed include voice recognition, target recognition, and medical diagnosis.
Neuromorphic computing is a new computing paradigm inspired by the workings of the human brain.
It involves the use of artificial neural networks that mimic the structure and function of biological neurons.
These networks are implemented in specialized hardware that is designed to optimize the performance of neural computations.
Neuromorphic computing is a new computing paradigm inspired by the workings of the human brain.
It involves the use of artificial neural networks that mimic the structure and function of biological neurons.
These networks are implemented in specialized hardware that is designed to optimize the performance of neural computations.
Holographic memory seminar ppt contains all aspects of holography and holographic storage. It provide history and technical background of holography. Contains reading and writing data into photopolymer. Lack of development of HDSS, its application and conclusion.
20 Latest Computer Science Seminar Topics on Emerging TechnologiesSeminar Links
A list of Top 20 technical seminar topics for computer science engineering (CSE) you should choose for seminars and presentations in 2019. The list also contains related seminar topics on the emerging technologies in computer science, IT, Networking, software branch. To download PDF, PPT Seminar Reports check the links.
Neuralink white-paper. Elon Musk & Neuralink
Abstract
Brain-machine interfaces (BMIs) hold promise for the restoration of sensory and motor function and
the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part
because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth BMI system. We have built arrays of small and
flexible electrode “threads”, with as many as 3,072 electrodes per array distributed across 96 threads.
We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute.
Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small
implantable device that contains custom chips for low-power on-board amplification and digitization: the package for 3,072 channels occupies less than (23 × 18.5 × 2) mm3
. A single USB-C cable
provides full-bandwidth data streaming from the device, recording from all channels simultaneously.
This system has achieved a spiking yield of up to 85.5 % in chronically implanted electrodes. Neuralink’s approach to BMI has unprecedented packaging density and scalability in a clinically relevant
package.
Holographic memory seminar ppt contains all aspects of holography and holographic storage. It provide history and technical background of holography. Contains reading and writing data into photopolymer. Lack of development of HDSS, its application and conclusion.
20 Latest Computer Science Seminar Topics on Emerging TechnologiesSeminar Links
A list of Top 20 technical seminar topics for computer science engineering (CSE) you should choose for seminars and presentations in 2019. The list also contains related seminar topics on the emerging technologies in computer science, IT, Networking, software branch. To download PDF, PPT Seminar Reports check the links.
Neuralink white-paper. Elon Musk & Neuralink
Abstract
Brain-machine interfaces (BMIs) hold promise for the restoration of sensory and motor function and
the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part
because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth BMI system. We have built arrays of small and
flexible electrode “threads”, with as many as 3,072 electrodes per array distributed across 96 threads.
We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute.
Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small
implantable device that contains custom chips for low-power on-board amplification and digitization: the package for 3,072 channels occupies less than (23 × 18.5 × 2) mm3
. A single USB-C cable
provides full-bandwidth data streaming from the device, recording from all channels simultaneously.
This system has achieved a spiking yield of up to 85.5 % in chronically implanted electrodes. Neuralink’s approach to BMI has unprecedented packaging density and scalability in a clinically relevant
package.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
The report covers the diverse field of neural networks compiled from various sources into a compact yet detailed form. It also has the formal report writing pattern incorporated in it.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
This paper demonstrates a framework that entails a bottom-up approach to
accelerate research, development, and verification of neuro-inspired sensing
devices for real-life applications. Previous work in neuromorphic
engineering mostly considered application-specific designs which is a strong
limitation for researchers to develop novel applications and emulate the true
behaviour of neuro-inspired systems. Hence to enable the fully parallel
brain-like computations, this paper proposes a methodology where a spiking
neuron model was emulated in software and electronic circuits were then
implemented and characterized. The proposed approach offers a unique
perspective whereby experimental measurements taken from a fabricated
device allowing empirical models to be developed. This technique acts as a
bridge between the theoretical and practical aspects of neuro-inspired
devices. It is shown through software simulations and empirical modelling
that the proposed technique is capable of replicating neural dynamics and
post-synaptic potentials. Retrospectively, the proposed framework offers a
first step towards open-source neuro-inspired hardware for a range of
applications such as healthcare, applied machine learning and the internet of
things (IoT).
Brain Computer Interface Next Generation of Human Computer InteractionSaurabh Giratkar
In the area of HCI research the main focus is on defining new ways of human interaction with computer system. With the passes of time a number of inventions have been made in this field. In initial days we used only keyboards to access our computer system (e.g. in Unix Terminal). In Second phase, after invention of mouse and other pointing devices, we started using graphical user interface using pointing devices like mouse which make the use of computer more easy and comfortable. Nowadays we are using pressure-driven mechanism, i.e. touch screen, which is common at ATMs, Mobile phones and PDAs etc. Although it is not as common in daily works but the release of tablet PCs and its popularity shows that the day is not much far when we wouldn’t be having keyboards and mouse at all.
All of these inventions have been made for balancing the requirements of society and user. E.g. Games, Multimedia Applications etc are not possible using only-Keyboard so we need mouse driven system for such applications, similarly we cannot have large keyboard on mobile so we need a touch screen system for mobiles. In addition to these traditional HCI models, there are some more advance HCI technology too for adding more flexibility and hence making the product more useful. E.g. swap card system at office doors for attendance and ATM-swap card for shopping. Speech processing systems are also there where we can access our computer system using our speech. Fig 1 shows most popular traditional HCI system.
Introduction to Neural networks (under graduate course) Lecture 1 of 9
Seminar Neuro-computing
1. Seminar on
UNDER THE GUIDANCE OF
Prof. K. E. Ch. Vidyasagar
PRESENTED BY
Aniket R. Jadhao
Dr. Bhausaheb Nandurkar College of Engineering&
Technology,
Yavatmal.
2012-2013
3. Introduction
Neurocomputing is concerned with information
processing
A neurocomputing approach to information
processing first involves a learning process within a
neural network architecture that adaptively
responds to inputs according to a learning rule
4. Cont...
After the neural network has learned what it needs
to know , the trained network can be used to
perform certain tasks depending on a particular
application
Neural networks have the capability to learn from
their environment and to adapt to it in an
interactive manner.
5. What do you think which is faster?
OR
A DIGITAL COMPUTER A HUMAN BEING?
6. Here come to answer ...
A human being is faster than Digital computer.
But why ?
How can we perform certain tasks better and faster
than a digital computer?
Do you know ?
Difference between brain and a digital computer?
7. Cont..
Neuron
Neurons are approximately six orders of
magnitude slower than silicon logic gates, However
the brain can compensate for the relatively slow
operational speed of the neuron by processing data in
a highly parallel architecture that is massively
interconnected. It is estimated that the human brain
must contain in the order of 10 raise to power 11
neurons and approximately three orders of magnitude
more connections or synapses
Therefore, the BRAIN is an
adaptive, nonlinear, parallel computer that is capable
of organizing neurons to perform certain tasks
9. Cont...
Characteristics of ARTIFICIAL NEURAL
NETWORKS
Ability to learn by example, An ARTIFICIAL NEURAL
NETWORK stores the knowledge that has been
learned during the training process in the synaptic
weights of neurons
Ability to generalise
11. Advantages of neurocomputing approach
to solving certain problems
Adaptive learning: An ability to learn how to do
tasks based on the data.
Self-Organization: An ANN can create its own
organization
Real Time Operation: ANN computations may be
carried out in parallel and special hardware devices
are being designed and manufactured which take
advantage of this capability
Fault Tolerance via Redundant Information Coding:
Partial destruction of a network leads to the
corresponding degradation of performance
12. Applications
Voice Recognition - Transcribing spoken words into
ASCII text
Target Recognition - Military application which uses
video and/or infrared image data to determine if an
enemy target is present
Medical Diagnosis - Assisting doctors with their
diagnosis by analyzing the reported symptoms and/or
image data such as MRIs or X-rays
Radar –signature classifier
13. Conclusion
Then the network is followed by the error generator
at the output, which compares the output of the
neuron with the target signal for which the network
has to be trained. Similarly, there is error generator
at the input, which updates the weights of the first
layer taking into account the error propagated back
from the output layer. Finally, a weight transfer unit is
present just to pass on the values of the updated
weights to the actual weights.
14. REFERNACES
[1] Simon Haykin, “Neural Networks”, Second edition
by, Prentice Hall of India, 2005.
[2] Christos Stergiou and Dimitrios Siganos, “Neural
Networks”, Computer Science Deptt. University of
U.K., Journal, Vol. 4, 1996.
[3] Robert J Schalkoff, “Artificial Neural Networks”,
McGraw-Hill International Editions, 1997.
[4] Uthayakumar Gevaran, “Back Propagation”,
Brandeis University, Department of Computer
Science.
[5] Jordan B.Pollack, “Connectionism: Past, Present
and Future”, Computer and Information Science
Department, The Ohio State University, 1998.
15. So, we can see that how neural networks and
neurocomputing is beneficial for us.
Thank you !
Instead of seeing society as a collection of clearly
defined “interest groups”, society must be
reconceptualise as a complex network of groups of
interacting individuals whose membership and
communication pattern are seldom confined to one
such group alone