A programmable neural processor for pulse-coded hippocampal signal

Rapid advances in submicron microelectronic technologies have made possible the integration of millions of transistors on a single silicon chip for complex signal processing and control functions. The hippocampal region of the brain system can be analyzed with a nonlinear system modeling approach. The input-output relationship of the neural units is represented by the kernel functions of various complexities. The modeling expressions of the first and second order kernels are computed in analog current-mode instead of digital format in order to fully explore massively parallel processing capability of the neural networks. A programmable pulse-coded neural network based on the hippocampal kernel functions can process the pulse information efficiently. The model-based approach saves silicon area and achieves adequate accuracy level. Circuit-level simulation results and experimental data are also presented.