1. Neuromorphic Engineering

2. Definitions Carver Mead introduce 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.

3. 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)

4. Interest in exploring neuroscience Interest in building neurally inspired systems Key Advantages  emulating biological systems in real time  attempt to replicate the computational power of brain effectively  What if our primitive gates were a neuron computation? a synapse computation? a piece of dendritic cable?  Efficient implementations compute in their memory elements more efficient than directly reading all the coefficients  Precise systems out of imprecise parts Why Neuromorphic Engineering?

5. What does neuromorphic Engineering involve? Analyze neurophysiological functions in order to reproduce neuronal structures and architectures Build neuronal networks and rely on modelers for explanation and modeling of natural phenomena

6. Biological brains and digital computers are both complex information processing systems. But here the similarities end Brain vs. Computer

7. Progress of electronic information processing over past 60 years:  dramatic improvements:  from 5 Joules / instruction (vacuum tube computer, 1940s)  to 0.0000000001 Joules / instruction (ARM968)  50,000,000,000 times better  Raw performance increase about 1 million Energy efficiency  Chip: 10 -11 J/operation  Computer system level: 10 -9 J/operation  Brain: 10 -15 J/operation  Brain is 1 million times more energy efficient!!! Energy Efficiency

8.  Massive parallelism (10 11 neurons)  Massive connectivity (10 15 synapses)  Low-speed components (~1 – 100 Hz)  >10 16 complex operations / second (10 Petaflops!!!)  10-15 watts!!!  1.5 kg Computing Power: Human Brain vs. Computer

9. Applications of Neuromorphic Systems

10. Neuromorphic systems Silicon Retina Silicon Cochlea Learning and adaptation silicon systems Koala-obstacle/tracking robot

11. Audio Systems Audio front ends Signal processing systems Hearing aids Cochlear implants Electronic Nose “Sniff out” odors Chemical sensors Drug traffic control Bio terror detection Sensorimotor Systems Intelligent robotics Intelligent controls Locomotive systems Spiking camera

12. Biology-Inspired “Neuromorphic” Vision  Very successful branch of neuromorphic engineering: sensory transduction  vision  Neuromorphic vision sensors sense and process visual information in a pixel-level manner Biological Paradigm Functional Model Electrical Model VLSI Design Neuromorphic Vision Sensor

13. Neural Disorder Control Parkinson’s disease Seizure prediction and control

14. Research works: Blue brain IBM developing the “Blue brain” IBM, in partnership with scientists at Switzerland’s Ecole Polytechnique Federale De Lausanne’s(EPFL) Brain and Mind Institute will begin simulating the brain’s biological systems..

15. SpiNNaker is a novel massively-parallel computer architecture, inspired by the fundamental structure and function of the human brain, which itself is composed of billions of simple computing elements, communicating using unreliable spikes. Research works: SpiNNaker

16. BrainScale (Brain-inspired multiscale computation in neuromorphic hybrid systems) was an EU FET-Proactive FP7 funded research project. The project started on 1 January 2011 and ended on 31 March 2015. It was a collaboration of 19 research groups from 10 European countries. The hardware development on the neuromorphic computing systems is continued in the Human Brain Project (HBP) in the Neuromorphic Computing Platform.FET-ProactiveHuman Brain Project (HBP) Neuromorphic Computing Platform Research works: Brain Scale project

17. Neurogrid is a multi-chip system developed by Kwabena Boahen and his group at Stanford University. Objective is to emulate neurons Composed of a 4x4 array of Neurocores Each Neurocore contains a 256x256 array of neuron circuits with up to 6,000 synapse connections Research works: Neurogrid

18. Fast Analog Computing with Emergent Transient States (FACETS) A project designed by an international collective of scientists and engineers funded by the European Union Recently developed a chip containing 200,000 neuron circuits connected by 50 million synapses. Research works: FACETS Project

19. Researchers at the Massachusetts Institute of Technology have made a huge leap forward with a new chip that mimics the way the neurons of the brain interact with one another. Research works: MIT silicon synapse

20. Neuromorphic Chips

21. Future : Silicon Cognition

22. 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

23. Brain Machine Interface (BMI)  A brain-machine interface is a direct communication pathway between a human or animal brain ( or brain cell culture) and an external device.  Sometimes called a direct neural interface or a brain computer interface (BCI).

24. Motivation for BMI Research  In USA, more than 200,000 patients live with the motor sequelae (consequences) of serious injury. There are two ways to help them restore some motor function:  Repair the damaged nerve axons.  Build neuroprosthetic device.

25. Using BCI Typing words by mind Help impaired hands to grasp by mind Play videogames by mind

26. Structure of the bidirectional BMI

27. Online Calibration Process 1. تولید دیتای کالیبراسیون توسط برنامه M1-S12. دریافت دیتای تولید شده و چیدن آن در ماتریس داده ها 3. قرار دادن دیتای تولید شده در برنامه کالیبراسیون 4. تولید ماتریس D(distance) و Force و مشخص شدن ناحیه بندی دیتاها برای داده های کالیبراسیون جهت استفاده در متن برنامه اصلی

28. Offline Test Process 1. دریافت داده از برنامه M1-S1 2. محاسبه ماتریس D برای این داده با استفاده از داده های کالیبراسیون 3. دیکد کردن این داده با استفاده از ماتریس D بدست آمده و مشخص نمودن ناحیه آن 4. محاسبه Force با استفده از ناحیه دیکد شده با استفاده از ماتریس Force تولد شده در کالیبراسیون 5. عمال Force تولید شده و محاسبه موقعیت جدید جهت مشخص نمودن تحریک جدید 6. انکد کردن این موقعیت جدید جهت اعمال به برنامه S1-M1 7. اعمال این دیتای انکد شده به برنامه S1- M1 جهت دریافت داده جدید

29. Simulation Results

35. Advantages  BCIs will help creating a Direct communication pathway between a human or animal brain and any external devices like computers.  BCI has increased the possibility of treatment of disabilities related to nervous system along with the old technique of Neuroprosthetics.  Techniques like EEG, MEG and neurochips have come into discussions since the BCI application have started developing.  This has provided a new work area for scientists and researchers around the world.

36. Disadvantages  In case of Invasive BCI there is a risk of formation of scar tissue.  There is a need of extensive training before user can use techniques like EEG  BCI techniques still require much enhancement before they can be used by users as they are slow.  Ethical implications of BCI will arise in future  BCI techniques are costly. It requires a lot of money to set up the BCI environment.

37. Our Publications  Ranjbar, M., & Amiri, M. (2015). An analog astrocyte–neuron interaction circuit for neuromorphic applications. Journal of Computational Electronics, 14(3), 694-706.  Ranjbar, M., & Amiri, M. (2015). Analog implementation of neuron–astrocyte interaction in tripartite synapse. Journal of Computational Electronics, 1-13.  Piri, M., Amiri, M., & Amiri, M. (2015). A bio-inspired stimulator to desynchronize epileptic cortical population models: A digital implementation framework. Neural Networks, 67, 74-83.  Nazari, S., Faez, K., Karami, E., & Amiri, M. (2014). A digital neurmorphic circuit for a simplified model of astrocyte dynamics. Neuroscience letters, 582, 21-26.

38.  Nazari, S., Faez, K., Amiri, M., & Karami, E. (2015). A novel digital implementation of neuron–astrocyte interactions. Journal of Computational Electronics, 14(1), 227-239.  Nazari, S., Faez, K., Amiri, M., & Karami, E. (2015). A digital implementation of neuron–astrocyte interaction for neuromorphic applications. Neural Networks, 66, 79-90.  Nazari, S., Amiri, M., Faez, K., & Amiri, M. (2015). Multiplier- less digital implementation of neuron–astrocyte signalling on FPGA. Neurocomputing.  Nazari, S., Faez, K., & Amiri, M. A multiplier-less digital design of a bio-inspired stimulator to suppress synchronized regime in a large-scale, sparsely connected neural network. Neural Computing and Applications, 1-16.  Ranjbar, M., & Amiri, M. On the role of astrocyte analog circuit in neural frequency adaptation. Neural Computing and Applications, 1-13.

39. THANK YOU!