This blog is to keep record of my work towards a brain inspired machine. It can help keep things tracked. I will post my work and resources I will find regarding same topic here.
These are the courses/areas one should have studied before delving deep into Neuromorphic Engineering. Ones more i would like to say that its not compulsory but its better if you inderstand these topics. Nowadays everything is intern disciplinary. These courses will give you introduction to Computational Neurosciences and Analog/Digital VLSI.
1. Computational Neurosceinces(coursera)
2. Fundamentals of Digital Image and Video Processing.(Coursera)(Extra)
3. Image&Video Processing(||)(Extra)
4. Machine Learning(||)(Extra)
5.Neural Networks for ML(||)
6.DSP(MIT ocw) and Revisit Circuits,signals and systems.
7.Exploring Neural Data(||)
8.Understanding the Brain(||)
9. Microelectronic Circuits(UC Berkely)
10. Intro to Analog IC Design( course)
11. CMOS Analog IC Design.(||)
12. Physical IC Design (||)
13. CMOS Mixed Signal circuit Design(||)
14. Advanced Analog IC DEsign(||)
15. Cadence/VLSI(||)
16.Synapse Neuron and Brain(Coursera).
There are some sources for Learning Neuromorphic Engineering.
Like ETH Zurich. ETHZ
And also for some other resources visit.
neuromorphicengineering
NOTE: Courses 2, 3 and 4 are not necessary but when you have a proper idea about them you can think about an application in multiple ways. It helps in correlating many theories.
The first course covers many aspects for starters. In first few videos concepts related to Brain and Neurons are taught. Second few weeks its more of Information Coding and theory. Next few weeks it covers RC circuit equivalent of neurons and dynamic analysis. Last couple of weeks deal with machine learning concepts. In total the course is good package for starters. It will be better to understand the concepts in RC, RLC, RL circuits and systems analysis to develop intuition and proper understanding of the RC equivalent of Neurons. A good book written by the professor who taught us Circuits and Networks, Signals and Systems, Analog MOS Cirucits at NIT-Calicut for B.Tech 2010-2014 batch can be referred for Dynamic system and network analysis. I will post some problems from his tutorials which can give some good understanding of dynamic system analysis.
With the clock ticking very fast for Moores Law. Single chip's capability to compute will be limited by size of transistor. I think its the time to look for alternative forms of computing. Von Neuman machines are completely opposite to Human brain in many aspects. Human brain is slow but present machines are fast. We are good with patterns but its hard for a machine inherently to recognize patterns. With the Invention of Memristor the fourth fundamental circuit element Neuromorphic Engineering has gained importance. Corporate giants like IBM, Qualcomm, HRL Laboratories have been working in this area investing billions of dollars.
To know more about the area please see this TrueNorth, NeuromorphicEngineering, Memristor. Dharmendra Modha
Recently started the book On Intelligence by Jeff Hawkins. I will discuss some of the points from there as soon as i complete that.
These are the courses/areas one should have studied before delving deep into Neuromorphic Engineering. Ones more i would like to say that its not compulsory but its better if you inderstand these topics. Nowadays everything is intern disciplinary. These courses will give you introduction to Computational Neurosciences and Analog/Digital VLSI.
1. Computational Neurosceinces(coursera)
2. Fundamentals of Digital Image and Video Processing.(Coursera)(Extra)
3. Image&Video Processing(||)(Extra)
4. Machine Learning(||)(Extra)
5.Neural Networks for ML(||)
6.DSP(MIT ocw) and Revisit Circuits,signals and systems.
7.Exploring Neural Data(||)
8.Understanding the Brain(||)
9. Microelectronic Circuits(UC Berkely)
10. Intro to Analog IC Design( course)
11. CMOS Analog IC Design.(||)
12. Physical IC Design (||)
13. CMOS Mixed Signal circuit Design(||)
14. Advanced Analog IC DEsign(||)
15. Cadence/VLSI(||)
16.Synapse Neuron and Brain(Coursera).
There are some sources for Learning Neuromorphic Engineering.
Like ETH Zurich. ETHZ
And also for some other resources visit.
neuromorphicengineering
NOTE: Courses 2, 3 and 4 are not necessary but when you have a proper idea about them you can think about an application in multiple ways. It helps in correlating many theories.
The first course covers many aspects for starters. In first few videos concepts related to Brain and Neurons are taught. Second few weeks its more of Information Coding and theory. Next few weeks it covers RC circuit equivalent of neurons and dynamic analysis. Last couple of weeks deal with machine learning concepts. In total the course is good package for starters. It will be better to understand the concepts in RC, RLC, RL circuits and systems analysis to develop intuition and proper understanding of the RC equivalent of Neurons. A good book written by the professor who taught us Circuits and Networks, Signals and Systems, Analog MOS Cirucits at NIT-Calicut for B.Tech 2010-2014 batch can be referred for Dynamic system and network analysis. I will post some problems from his tutorials which can give some good understanding of dynamic system analysis.
With the clock ticking very fast for Moores Law. Single chip's capability to compute will be limited by size of transistor. I think its the time to look for alternative forms of computing. Von Neuman machines are completely opposite to Human brain in many aspects. Human brain is slow but present machines are fast. We are good with patterns but its hard for a machine inherently to recognize patterns. With the Invention of Memristor the fourth fundamental circuit element Neuromorphic Engineering has gained importance. Corporate giants like IBM, Qualcomm, HRL Laboratories have been working in this area investing billions of dollars.
To know more about the area please see this TrueNorth, NeuromorphicEngineering, Memristor. Dharmendra Modha
Recently started the book On Intelligence by Jeff Hawkins. I will discuss some of the points from there as soon as i complete that.
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