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Neuromorphic Engineering

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Presentation on theme: "Neuromorphic Engineering"— Presentation transcript:

1 Neuromorphic Engineering
University of Oxford Department of Engineering Science Natasha Chia

2 Outline Definition of neuromorphic systems
Principles of neuromorphic technology Typical applications of neuromorphic technology The basic computational element Neural codes and information representation Future of Neuromorphic systems

3 Emulates the functional structure of neurobiological systems.
Definitions Carver Mead introducec the term Neuromorphic Engineering to describe A new field of engineering whose design principles and architecture are biologically Inspired. Neuro “ to do with neurons i.e. neurally inspired” Morphic “ structure or form” Emulates the functional structure of neurobiological systems. Neuromorphic systems are systems implemented on silicon,whose architecture and design are based on neurobiology

4 Principles of neuromorphic technology
Build machines that have similar perception capabilities as human perception Adaptable and self organising Robust to changing environments Realisation of future “THINKING” machines (intelligent and interactive systems) These understanding are creating new opportunities for intelligent systems design Provides promising methods to tackle a wide variety of design related problems

5 What does neuromorphic Engineering involve?

6 Applications of Neuromorphic Systems

7 Neuromorphic systems Silicon Retina Learning and adaptation
silicon systems Koala-obstacle/tracking robot Silicon Cochlea Existing app are modest and the challenge of designing fully autonomous systems at the levels achieved by biological systems lies ahead. These devices are mostly analog and used as front end sensors TouchPad

8 Example of Modelling approaches
Functional approach adopted (The goal is to construct a biologically constrained framework for speech recognition at the network level)so functional components are used in the models not actual biophysical components. Build a hierarchical speech system based on a model of the mammalian auditory system The undertaking is to construct a biologically constrained framework for speech recognition at the network level. Parallel distributed analog correlation based processing is the basis of VLSI systems that emulate the function of neural information processing in biological systems

9 The Basic computational element : The Neuron
A typical neuron accepts input from its large dendritic tree and combines the information to produce a single output at its axon.

10 The Neuron Model

11 History of neuron modelling
1943 McCulloch & Pits 1963 Hodgkin & Huxley modelled axon of giant squid. 1970 Kiang & Gerstein numerical analysis of interactions in nerve cells 1983 Cohan & Mpitson Deterministic chaos used to describe behaviour of single neurons 1985 Meyer et al Discovery of electrical interaction between neurons 1992 Bower Quantitative evaluation of functional data 1993 Knopf and Gupta Fundamental neural processing element 1995 Bressler Parallel processing of information

12 Neural code and information representation
How does spikes represent sensory information? Models of neuron spiking mechanism Stimulus Response features Group behaviour

13 Stimulus Response features
Average firing rate Position of each neuronal discharge Instantaneous firing probability.(A ganglion’s firing rate depends on stimulation) Spike trains can only be encoded under two ideal conditions 1.Spike times must be precise 2.Distance between two events must be more than the spike time-jitter.

14 Spiking Neuron models Integrate and fire Stimulus Response Model (SRM)
Neurons spike regularly in response to an external current. Rate of spiking increases with the magnitude of the stimulus current Stimulus Response Model (SRM) The stimulus is generalized into certain patterns in order to quantify the stimulus response

15 Neural coding Information is conveyed to the brain in parallel by spike trains of the nerve fibers.

16 Group behaviour of spikes
One of the challenges has been to understand the relative contribution of various groups of spikes Oscillatory coupling effects and amplitude dynamics in two or more populations of neurons will be important topics for future research Some other examples: STDP(Spike Time-dependent plasticity) Group neurons with correlated inputs Brief talk on why the gp behaviour of spikes is not well understood

17 Future of neuromorphic systems
Implantable medical electronics Increased human computer interaction Intelligent transportantion systems Learning, pattern recognition Robot control(self motion estimation) Learning higher order perceptual computation Neu en is still an evolving field and has not matured into an established discipline. Human computer interaction is shifted away from the keyboard monitor to other natural means of interaction s a speech. Neural based computation(learning,recognition and attention

18 Conclusion

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