The article provides an overview of neuromorphic silicon neuron (SiN) circuits, which are hybrid analog/digital Very Large Scale Integration (VLSI) circuits designed to emulate the electrophysiological behavior of real neurons. These circuits operate in real-time, offering advantages over digital simulations in terms of energy efficiency and speed for large-scale neural system simulations. The paper discusses various design strategies and techniques used to implement SiN circuits, including current-mode, sub-threshold, voltage-mode, and switched-capacitor (S-C) designs. It also compares different design methodologies and presents experimental results from a wide range of fabricated VLSI chips. Key topics covered include: 1. **Synapse and Soma Blocks**: These blocks are responsible for receiving and integrating spikes, converting them into currents, and generating output spikes. 2. **Thermodynamically Equivalent Models**: These models use transistor circuits to represent the dynamics of gating particles in biological neurons. 3. **Phenomenological Models**: These models abstract the behavior of conductance and channel dynamics using differential equations. 4. **Spike-Event Generation**: Circuits like the Axon-Hillock circuit are used to generate discrete spike events. 5. **Spike-Frequency Adaptation and Adaptive_THRESHOLDS**: Mechanisms to reduce firing rates over time, such as spike-frequency adaptation and adaptive threshold mechanisms. 6. **Axons and Dendritic Trees**: Models of dendritic compartments and axons to simulate signal propagation and parallel processing. 7. **Additional Useful Building Blocks**: Techniques like Digi-MOS and very low current mirrors for calibration and power reduction. The article also describes specific SiN implementations, including sub-threshold biophysically realistic models, compact integrate-and-fire circuits for event-based systems, and generalized integrate-and-fire neuron circuits. Examples include the thalamic relay neuron, a sub-threshold Hodgkin–Huxley based neuron, the octopus retina neuron, and the dynamic vision sensor differencing neuron. These circuits are designed to meet different application requirements, from high-speed modeling of large-scale neural systems to real-time behaving systems and brain-machine interfaces.The article provides an overview of neuromorphic silicon neuron (SiN) circuits, which are hybrid analog/digital Very Large Scale Integration (VLSI) circuits designed to emulate the electrophysiological behavior of real neurons. These circuits operate in real-time, offering advantages over digital simulations in terms of energy efficiency and speed for large-scale neural system simulations. The paper discusses various design strategies and techniques used to implement SiN circuits, including current-mode, sub-threshold, voltage-mode, and switched-capacitor (S-C) designs. It also compares different design methodologies and presents experimental results from a wide range of fabricated VLSI chips. Key topics covered include: 1. **Synapse and Soma Blocks**: These blocks are responsible for receiving and integrating spikes, converting them into currents, and generating output spikes. 2. **Thermodynamically Equivalent Models**: These models use transistor circuits to represent the dynamics of gating particles in biological neurons. 3. **Phenomenological Models**: These models abstract the behavior of conductance and channel dynamics using differential equations. 4. **Spike-Event Generation**: Circuits like the Axon-Hillock circuit are used to generate discrete spike events. 5. **Spike-Frequency Adaptation and Adaptive_THRESHOLDS**: Mechanisms to reduce firing rates over time, such as spike-frequency adaptation and adaptive threshold mechanisms. 6. **Axons and Dendritic Trees**: Models of dendritic compartments and axons to simulate signal propagation and parallel processing. 7. **Additional Useful Building Blocks**: Techniques like Digi-MOS and very low current mirrors for calibration and power reduction. The article also describes specific SiN implementations, including sub-threshold biophysically realistic models, compact integrate-and-fire circuits for event-based systems, and generalized integrate-and-fire neuron circuits. Examples include the thalamic relay neuron, a sub-threshold Hodgkin–Huxley based neuron, the octopus retina neuron, and the dynamic vision sensor differencing neuron. These circuits are designed to meet different application requirements, from high-speed modeling of large-scale neural systems to real-time behaving systems and brain-machine interfaces.