Some Structural Complexity Aspects of Neural Computation

Recent work by Siegelmann and Sontag hod demonatrated that polynomial time on linear saturated recurrent neural networks equab polynomial time on standard computational models: !bring machines if the weights of the net are rationab, and nonuniform circuits if the weights are reab. Here we develop further connections between the languages recognized by such neural nets and other complen'ty classes. We present connections to spacebounded classes, simulation of parallel computational models such as Vector Machines, and a discussion of the charaderkations of various nonuniform classes in terms of Kolmogorov complezity.