Neural recorders capture electrical signals from brain tissue to study neural activity, but multi-channel systems generate large data volumes that challenge portable applications with limited storage and battery life. This project presents version three of the Neurologger, an ultra-lightweight FPGA-based neural recording system for real-time data acquisition and on board signal processing. The system uses specialized analog front-end chips to acquire neural signals from multiple channels. The FPGA implements processing pipelines for spike detection and sorting across all channels concurrently, achieving deterministic low-latency operation that sequential microcontrollers cannot match. By processing and classifying spikes in real-time, the system can substantially reduce data volume before storage, making it suitable for portable and closed-loop neural applications.