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