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In this paper, we will present a Neural Net Benchmark. In many neural algorithms, learning is defined as a change in connection weight values that result in the capture of training information that can later be recalled. A typical learning algorithm is: if neuron A was active and A's activity caused a connected neuron B to fire, then the synaptic connection between A and B should be increased by certain amount. The benchmark systematically tests a neural network for this ability. The benchmark can be written as: (L3, L4, L5, L6, ... ...). L3 tests the net's ability for 3- neuron correlation: if A and B were active and A and B's activity caused a connected neuron C to fire, can the network recall it later? Similarly, L4, L5, ... tests a net for 4-neuron, 5-neuron correlation. We will also present a neural net classification based on the benchmark.
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