Neuromorphic Computing Circuit Design

# Neuromorphic Computing Circuit Design

## Core Concepts

Neuromorphic computing aims to mimic the structure and function of the biological brain. This differs significantly from traditional von Neumann architectures. Key concepts include:

*   **Spiking Neural Networks (SNNs):**  Information is encoded in the *timing* of spikes, rather than continuous values. This leads to event-driven, power-efficient computation.
*   **Neurons & Synapses:**  The fundamental building blocks.  Circuits must emulate the behavior of biological neurons (integrate-and-fire, leaky integrate-and-fire, Izhikevich models) and synapses (plasticity, short-term plasticity).
*   **Plasticity:**  The ability of synapses to change their strength over time, crucial for learning.  Implemented via various mechanisms (STDP, Hebbian learning).
*   **Event-Driven Computation:**  Computation only occurs when a neuron fires a spike, reducing energy consumption.