 Analyzed in this fashion, both biological and artificial neural networks are interpreted naturally as vector-to-vector transformers. The input vector consists of values reflecting activity patterns in axons synapsing on the network’s neurons from outside (e.g., from sensory transducers or other neural networks). The output vector consists of values reflecting the activity patterns generated in the network’s neurons that project beyond the net (e.g., to motor effectors or other neural networks). Given that each neuron’s activity depends partly upon their total input, and its total input depends partly on synaptic weights (e.g., presynaptic neurotransmitter release rate, number and efficacy of postsynaptic receptors, availability of enzymes in synaptic cleft), the capacity of biological networks to change their synaptic weights make them plastic vector-to-vector transformers. In principle, a biological network with plastic synapses can come to implement any vector-to-vector transformation that its composition permits (number of input units, output units, processing layers, recurrency, cross-connections, etc.) (discussed in Paul Churchland, 1987, with references to the primary scientific literature).