A spiking neural network in which communication and computation is performed on data represented as points in actual time. Such neural networks provide new ways of performing computation in human-engineered computing systems that employ the same basic paradigm as the mammalian neocortex. Information is encoded based on the relative timing of individual voltage spikes produced, consumed, and computed upon by groups of neurons. Component and interconnection delays are an integral part of the computation process. Multi-connection paths between pairs of neurons are modeled as a single compound path. Multi-layer networks are trained from the input layer proceeding one layer at a time to the output layer. Training involves a computation that is local to each synapse, and synaptic weights are determined by an averaging method. The action of inhibitory neurons is modeled as a bulk process, rather than with individual neurons.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A spiking neural network comprising: multiple interconnected excitatory neurons, each having multiple inputs and an output; information is encoded via spike timing relationships encoded as volleys of spikes on bundles, each bundle consisting of one or more lines; the output of one neuron is connected to the inputs of multiple other neurons; the output of one neuron may have one or more connections to a single other neuron; each of the connections has an associated synaptic weight and delay; synaptic weights are trained via the application of training patterns encoded as spike volleys; an output spike from a pre-synaptic neuron results in a spike response at the connected post-synaptic neuron; the spike response can be expressed as a unimodal function of time, which depends on the synaptic delay and the synaptic weight; the post-synaptic excitatory neuron sums the unimodal functions and produces an output spike at the first time a threshold level is achieved by the sum of the response functions; if the threshold value is not reached, then there is no output spike. 2. The network of claim 1 wherein values are encoded as spike volleys, in which the spike that occurs earliest in time is assigned a parameterized maximum value; spikes occurring later in time have their values determined on a relative basis with respect to the time of the first spike. 3. The network of claim 2 wherein: the unimodal spike response functions are piecewise linear, with a parameterized rise time and a parameterized fall time. 4. The network of claim 3 wherein excitatory neurons are combined to form excitatory columns, each column consisting of one or more excitatory neurons. The neurons in a column operate concurrently on input information encoded as spike volleys, communicated via bundles of lines; the output of an excitatory column is also encoded as a spike volley consisting of the combined spike outputs of all the neurons in the column. 5. The network of claim 4 further comprising: a bulk model for inhibition wherein a parameterized model of feedforward inhibition is implemented. 6. The network of claim 4 further comprising: a bulk model for inhibition wherein a parameterized model of lateral inhibition is implemented. 7. A network of claim 4 wherein multiple connections between a pre-synaptic neuron and a single post-synaptic neuron are modeled individually, each connection having its own delay and weight; the weights are established via a training process that applies a training set of input patterns and adjusts the weights according to an STDP update method. 8. The network of claim 4 wherein multiple connections between a pre-synaptic neuron and a single post-synaptic neuron are modeled as a single compound synaptic connection; each such compound connection has an associated weight that defines a unimodal response function for that compound connection; the weights are established via a training process that averages the input values for members of a training set. 9. The network of claim 7 being applied to the classification problem, wherein training is supervised. 10. The network of claim 7 being applied to the classification problem, wherein training is unsupervised. 11. The network of claim 8 being applied to the classification problem, wherein training is supervised. 12. The network of claim 8 being applied to the classification problem, wherein training is unsupervised. 13. A spiking neural network architecture consisting of multiple layers of excitatory neurons, arranged as excitatory columns, interconnected with bulk inhibition modeled as inhibitory columns; information is passed via bundles consisting of lines which carry information in the form of volleys consisting of spikes. 14. The network of claim 13 wherein all of the network connections are feedforward and excitatory and inhibitory columns appear in an interleaved fashion. 15. The network of claim 13 wherein global actual time is maintained throughout the network model, with conversion to local actual time for neuron computations, followed by re-conversion to global actual time for inter-column communication.