Abstract: A dynamically stable associative learning neural network system include a plurality of synapses (122,22-28), a non-linear function circuit (30) and an adaptive weight circuit (150) for adjusting the weight of each synapse based upon the present signal and the prior history of signals applied to the input of the particular synapse and the present signal and the prior history of signals applied to the input of a predetermined set of other synapses. An embodiment of a conditional-signal neuron circuit (100) receives input signals from conditional stimuli and an unconditional-signal neuron circuit (110) receives input signals from unconditional stimuli. A neural network (200) is formed by a set of conditional-signal and unconditional-signal neuron circuits connected by flow-through synapses to form separate paths between each input (215) and a corresponding output (245).

Abstract: A dynamically stable associative learning neural network system include a plurality of synapses (122,22-28), a non-linear function circuit (30) and an adaptive weight circuit (150) for adjusting the weight of each synapse based upon the present signal and the prior history of signals applied to the input of the particular synapse and the present signal and the prior history of signals applied to the input of a predetermined set of other collateral synapses. A flow-through neuron circuit (1110) embodiment includes a flow-through synapse (122) having a predetermined fixed weight. A neural network is formed by a set of flow-through neuron circuits connected by flow-through synapses to form separate paths between each input (215) and a corresponding output (245). In one embodiment (200), the neuron network is initialized by setting the adjustable synapses at some value near the minimum weight and setting the flow-through neuron circuits at some arbitrarily high weight.

Abstract: A dynamically stable associative learning neural network system include a plurality of synapses and a non-linear function circuit and includes an adaptive weight circuit for adjusting the weight of each synapse based upon the present signal and the prior history of signals applied to the input of the particular synapse and the present signal and the prior history of signals applied to the input of a predetermined set of other collateral synapses. A flow-through neuron circuit embodiment includes a flow-through synapse having a predetermined fixed weight. A neural network is formed employing neuron circuits of both the above types. A set of flow-through neuron circuits are connected by flow-through synapses to form separate paths between each input terminal and a corresponding output terminal. Other neuron circuits having only adjustable weight synapses are included within the network. This neuron network is initialized by setting the adjustable synapses at some value near the minimum weight.