The rapid advances in computer technology over the past years have made it feasible to handle a considerable amount of multi-channel long-term recording data. Comprehensive software toolkits are available for the specific task of analyzing large matrices of spike data post hoc using Mathworks Matlab (Egert et al., 2002). Here, we present a framework that complements this approach with the ability to continuously interact with the experimental preparation while at the same time recording all relevant data for subsequent evaluation. Beyond the ease of integration with post-processing and control scripts, we consider the main strengths of our approach to be its generic applicability, low cost and relative ease of implementation. In this, it stands out among the many alternative closed-loop approaches, ranging from purpose built simulators of nerve cell network components (Marder et al., 1994) through embedded programmable digital signal processor (DSP) architectures such as RT-NEURON all the way to a real-time operating system on standard PC hardware [e.g., real-time Linux (Wagenaar et al., 2005), RT-LAB, and others].