Memory capacity of neural networks learning within bounds

We present a model of long term memory : learning within irreversible bounds. The best bound values and memory capacity are determined numerically. We show that it is possible in general to calculate analytically the memory capacity by solving the random walk problem associated to a given learning rule. Our estimations — done for several learning rules — are in excellent agreement with numerical and analytical statistical mechanics results.