A sparse coding system. The sparse coding system comprises a neural network including a plurality of neurons each having a respective feature associated therewith and each being configured to be electrically connected to every other neuron in the network and to a portion of an input dataset. The plurality of neurons are arranged in a plurality of neuron clusters each comprising a respective subset of the plurality of neurons, and the neurons in each cluster are electrically connected to one another in a bus structure, and the plurality of clusters are electrically connected together in a ring structure. Also provided is a sparse coding system that comprises an inference module configured to extract features from an input image containing an object, wherein the inference module comprises an implementation of a sparse coding algorithm, and a classifier configured to classify the object in the input image based on the extracted features.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A sparse coding system, comprising: a neural network including a plurality of neurons each having a respective feature associated therewith and each being configured to be electrically connected to every other neuron in the network and to a portion of an input dataset, wherein the plurality of neurons are arranged in a plurality of neuron clusters each comprising a respective subset of the plurality of neurons, and further wherein the neurons in each cluster are electrically connected to one another in a bus structure, and the plurality of clusters are electrically connected together in a ring structure. 2. The system of claim 1, wherein the bus structure is a multi-dimensional bus structure comprising a plurality of rows, a plurality of columns, and a plurality of logic OR gates, wherein each OR gate is associated with a respective row or column and electrically connects the neurons in that row or column to one another. 3. The system of claim 1, wherein the bus structure is a multi-dimensional bus structure having A rows and B columns of neurons, and further wherein the bus structure comprises A horizontal buses each connecting B neurons in a respective row of the bus structure, and B vertical buses each connecting A neurons in a respective column of the bus structure. 4. The system of claim 1, further comprising a memory, and wherein each connection between two neurons has a respective weight W associated therewith and each connection between a neuron and at least a portion of the input dataset has a respective weight Q associated therewith, and further wherein each weight Q and W is stored in the memory of the system. 5. The system of claim 4, wherein the weights Q and W are quantized to a fixed-point number to reduce memory storage. 6. The system of claim 4, wherein the memory is partitioned into a first portion and a second portion, and further wherein both the first and second portions are used during a learning operation performed by the system, and only one of the first and second portions is used during an inference operation performed by the system. 7. The system of claim 6, wherein the first portion of the memory comprises the most significant bits (MSBs) of the Q and W weights, and the second portion of the memory comprises the least significant bits (LSBs) of the Q and W weights. 8. The system of claim 1, wherein the neural network is configured to perform a learning operation based on a first batch of neurons to fire. 9. The system of claim 1, wherein the neural network is configured to perform a learning operation, and wherein parameter updates during the learning operation are carried out using a message passing approach. 10. The system of claim 1, wherein each neuron is configured to generate a binary spike output. 11. The system of claim 1, the neural network comprises an inference module configured to extract features from an image represented by the input dataset, wherein the image contains an object. 12. The system of claim 11, further comprising a classifier configured to classify the object in the input image based on the extracted features. 13. The system of claim 1, wherein the bus structure comprises an arbitration-free bus structure. 14. The system of claim 1, further comprising a power supply, and wherein a supply voltage supplied by the power supply to the neural network is scaled to take advantage of the error resilience of the sparse coding system. 15. A sparse coding system, comprising: an inference module configured to extract features from an input image containing an object, wherein the inference module comprises an implementation of a sparse coding algorithm; and a classifier configured to classify the object in the input image based on the extracted features, wherein the inference module and classifier are integrated on a single chip. 16. The system of claim 15, wherein the inference module comprises at least one neural network comprising a plurality of neurons each having a respective feature associated therewith and each being configured to be connected to every other neuron in the network and at least a portion of the input image. 17. The system of claim 16, wherein the at least one neural network has a scalable multi-layer architecture. 18. The system of claim 17, wherein the at least one neural network comprises a plurality of neuron clusters each comprising a respective subset of the plurality of neurons, and further wherein the neurons in each cluster are electrically connected to one another in a bus structure, and the plurality of clusters are electrically connected together in a ring structure. 19. The system of claim 18, wherein the bus structure is a multi-dimensional bus structure comprising a plurality of rows, a plurality of columns, and a plurality of logic OR gates, wherein each OR gate is associated with a respective row or column and electrically connects the neurons in that row or column to one another. 20. The system of claim 16, wherein the inference module further comprises a memory, and wherein each connection between two neurons in the neural network has a respective weight W associated therewith and each connection between a neuron in the neural network and at least a portion of the input image has a respective weight Q associated therewith, and further wherein each weight W and Q is stored in the memory. 21. The system of claim 20, wherein the memory is partitioned into a first portion and a second portion, and further wherein both the first and second portions are used during a learning operation performed by the inference module and only one of the first and second portions is used during an inference operation performed by the inference module. 22. The system of claim 15, wherein the classifier comprises an event-driven implementation of a classifier. 23. The system of claim 15, wherein the classifier comprises one or more adders and does not comprise any multipliers. 24. An object recognition system comprising the system of claim 15, wherein the inference module comprises a front-end of the object recognition system and the classifier comprises a back-end of the object recognition system.