Neuromorphic Engineering: The Scientist’s, Algorithms Designer’s and Computer Architect’s Perspectives on Brain-Inspired Computing

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
About the author
Preface
Foreword: a tale about passion and fear
List of Figures
Before we begin
Glossary
Section I: Introduction and Overview
	Chapter 1: Introducing the perspective of the scientist
		1.1. FROM THE NEURON DOCTRINE TO EMERGENT BEHAVIOR
			1.1.1. The unity and diversity of neurons
			1.1.2. Neural coding
			1.1.3. Networks and emergent behavior
			1.1.4. Neuronal abstractions
			1.1.5. Data-driven neuroscience
		1.2. BRAIN MODELING
			1.2.1. Brain modeling in biological systems
			1.2.2. Computational brain modeling
				1.2.2.1. Bottom-up modeling
				1.2.2.2. Top-down modeling
			1.2.3. Brain modeling with neuromorphic hardware
		1.3. GLOSSARY
		1.4. FURTHER READING
	Chapter 2: Introducing the perspective of the computer architect
		2.1. LIMITS OF INTEGRATED CIRCUITS
			2.1.1. Transistor density
			2.1.2. Processing speed
			2.1.3. Distributed computing
		2.2. EMERGING COMPUTING PARADIGMS
			2.2.1. Quantum computing
			2.2.2. Molecular computing
			2.2.3. DNA computing
			2.2.4. Programmable microfluidics
		2.3. BRAIN-INSPIRED HARDWARE
			2.3.1. The why
			2.3.2. The how
				2.3.2.1. Probabilistic representation
				2.3.2.2. In-memory computing
			2.3.3. The what
			2.3.4. Neuromorphic frameworks
				2.3.4.1. Neuromorphic sensing
				2.3.4.2. Neuromorphic interfaces
				2.3.4.3. General purpose neuromorphic computers
				2.3.4.4. Memristors
		2.4. GLOSSARY
		2.5. FURTHER READING
	Chapter 3: Introducing the perspective of the algorithm designer
		3.1. FROM ARTIFICIAL TO SPIKING NEURAL NETWORKS
			3.1.1. Network architecture
			3.1.2. Neuromorphic applications
				3.1.2.1. Neuromorphic learning
				3.1.2.2. Neuro-robotics
				3.1.2.3. Cognitive models
		3.2. NEUROMORPHIC SOFTWARE DEVELOPMENT
		3.3. GLOSSARY
		3.4. FURTHER READING
Section II: The Scientist’s Perspective
	Chapter 4: Biological description of neuronal dynamics
		4.1. POTENTIALS AND SPIKES
			4.1.1. The resting potential
			4.1.2. The action potential
			4.1.3. Spike propagation
			4.1.4. Synapses
		4.2. POWER AND PERFORMANCE ESTIMATES OF THE BRAIN
		4.3. GLOSSARY
		4.4. FURTHER READING
	Chapter 5: Models of point neuronal dynamic
		5.1. THE LEAKY INTEGRATE AND FIRE MODEL
			5.1.1. Membrane voltage for various input patterns
			5.1.2. Flat input
			5.1.3. Step current input
			5.1.4. Numerical modeling of pulse input
			5.1.5. Arbitrary input
		5.2. THE IZHIKEVICH NEURON MODEL
		5.3. THE HODGKIN-HUXLEY MODEL
		5.4. SYNAPSE MODELING
		5.5. SIMULATING POINT NEURONS
			5.5.1. Biological plausibility vs. computational resources
			5.5.2. Large scale simulations of point processes
		5.6. CASE STUDY: A SNN FOR PERCEPTUAL FILLING-IN
			5.6.1. Perceptual filling-in
			5.6.2. Mathematical formulation
			5.6.3. Feed-forward SNN for perceptual filling-in
			5.6.4. Recurrent SNN for perceptual filling-in
			5.6.5. Is it biologically plausible?
		5.7. GLOSSARY
		5.8. FURTHER READING
	Chapter 6: Models of morphologically detailed neurons
		6.1. WHY MORPHOLOGICALLY DETAILED MODELING?
		6.2. THE CABLE EQUATION
			6.2.1. Passive Dendrite
			6.2.2. Axon
			6.2.3. Simulating the cable equation
			6.2.4. Partition length
		6.3. THE COMPARTMENTAL MODEL
			6.3.1. Reconstructed morphology and dynamic
		6.4. CASE STUDY: DIRECTIONAL SELECTIVE SAC
		6.5. GLOSSARY
		6.6. FURTHER READING
	Chapter 7: Models of network dynamic and learning
		7.1. NEURAL CIRCUIT TAXONOMY FOR BEHAVIOR
		7.2. RECONSTRUCTION AND SIMULATION OF NEURAL NETWORKS
			7.2.1. Detailed modeling
			7.2.2. Simulating detailed models
		7.3. CASE STUDY: SACS’ LATERAL INHIBITION IN DIRECTION SELECTIVITY
		7.4. NEUROMORPHIC AND BIOLOGICAL LEARNING
			7.4.1. Biological backpropagation-inspired learning
			7.4.2. Biological unsupervised learning
				7.4.2.1. Hebbian learning
				7.4.2.2. Spike timing-dependent plasticity
				7.4.2.3. BCM learning
				7.4.2.4. Oja’s learning
		7.5. GLOSSARY
		7.6. FURTHER READING
Section III: The Computer Architect’s Perspective
	Chapter 8: Neuromorphic hardware
		8.1. TRANSISTORS AND MICRO-POWER CIRCUITRY
		8.2. THE SILICON NEURON
			8.2.1. The pulse current-source synapse
			8.2.2. The reset and discharge synapse
			8.2.3. The charge and discharge synapse
			8.2.4. The log-domain integrator synapse
			8.2.5. The axon-hillock neuron
			8.2.6. Voltage-amplifier LIF neuron
		8.3. CASE STUDY: HARDWARE AND SOFTWARE CO-SYNTHESIS
			8.3.1. Circuit design
			8.3.2. Circuit analysis
				8.3.2.1. Architectural design
				8.3.2.2. Neuron control
			8.3.3. NEF-inspired design
		8.4. GLOSSARY
		8.5. FURTHER READING
	Chapter 9: Communication and hybrid circuit design
		9.1. COMMUNICATING SILICON NEURONS
		9.2. FROM HYBRID TO DIGITAL CIRCUITRY
		9.3. GLOSSARY
		9.4. FURTHER READING
	Chapter 10: In-memory computing with memristors
		10.1. FROM TRANSISTORS TO MEMRISTORS
		10.2. A NEW FUNDAMENTAL CIRCUIT ELEMENT
		10.3. MEMRISTORS FOR NEUROMORPHIC ENGINEERING
		10.4. GLOSSARY
		10.5. FURTHER READING
Section IV: The Algorithms Designer’s Perspective
	Chapter 11: Introduction to neuromorphic programming
		11.1. THEORY OF NEUROMORPHIC COMPUTING
			11.1.1. Neuromorphic computing as Turing complete
			11.1.2. A complexity theory for neuromorphic computing
		11.2. UNDERSTANDING NEUROMORPHIC PROGRAMMING
		11.3. GLOSSARY
		11.4. FURTHER READING
	Chapter 12: The Neural Engineering Framework (NEF)
		12.1. THE FUNDAMENTAL PRINCIPLES OF NEF
			12.1.1. Representation
				12.1.1.1. Encoding
				12.1.1.2. Decoding
				12.1.1.3. Decoder analysis
				12.1.1.4. Representation of high dimensional stimulus
			12.1.2. Transformation
				12.1.2.1. Linear transformation
				12.1.2.2. Linear transformations
				12.1.2.3. Non-linear transformations
				12.1.2.4. Addition
				12.1.2.5. Multiplication
			12.1.3. Dynamics
				12.1.3.1. The recurrent connection
				12.1.3.2. Synthesis
				12.1.3.3. Neuromorphic integration
				12.1.3.4. Neuromorphic oscillators
				12.1.3.5. Neuromorphic attractors
		12.2. CASE STUDY: MOTION DETECTION IN A SPIKING CAMERA USING OSCILLATION INTERFERENCE
			12.2.1. Spiking camera
			12.2.2. Gabor functions
			12.2.3. Damped oscillators
			12.2.4. Motion detection
		12.3. GLOSSARY
		12.4. FURTHER READING
	Chapter 13: Learning spiking neural networks
		13.1. INTRODUCTION TO LEARNING SNNS
		13.2. LEARNING SPIKING NEURAL NETWORKS WITH NEF
			13.2.1. The prescribed error sensitivity rule
			13.2.2. PES learning for classical conditioning
		13.3. FROM DNN TO DEEP SNN
			13.3.1. MNIST classification with deep SNN
				13.3.1.1. Convolutional neural networks
				13.3.1.2. CNN architecture
				13.3.1.3. Results
		13.4. GLOSSARY
		13.5. FURTHER READING
Bibliography
Index