Neural Amplifiers: Interfaces and Design

This lecture covers the design and implementation of neural amplifiers for neural interfaces, focusing on low-noise amplification, signal processing, and data reduction. It discusses the requirements for neural amplifiers, including input-referred noise, dynamic range, input impedance, and frequency bands of interest. The lecture also explores the trade-offs in neural amplifier design, such as noise, power dissipation, input/output impedance, speed, linearity, voltage swings, and gain. Additionally, it delves into the capacitive feedback architecture of neural amplifiers and their transfer functions, emphasizing key parameters like gain, frequency response, and design considerations.