Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network

Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware

Spiking Neural Networks (SNNs) are widely deployed to solve complex pattern recognition, function approximation and image classification tasks. With the growing size and complexity of these networks, hardware implementation becomes challenging because scaling up the size of a single array (crossbar) of fully connected neurons is no longer feasible due to strict energy budget. Modern neromorphic hardware integrates small-sized crossbars with time-multiplexed interconnects. Partitioning SNNs becomes essential in order to map them on neuromorphic hardware with the major aim to reduce the global c…

Neural Signal Multiplexing via Compressed Sensing

Transport of neural signals in the brain is challenging, owing to neural interference and neural noise. There is experimental evidence of multiplexing of sensory information across population of neurons, particularly in the vertebrate visual and olfactory systems. Recently, it has been discovered that in lateral intraparietal cortex of the brain, decision signals are multiplexed with decision-irrelevant visual signals. Furthermore, it is well known that several cortical neurons exhibit chaotic spiking patterns. Multiplexing of chaotic neural signals and their successful demultiplexing in the n…

通过生物合理脉冲网络中的误差调节可塑性联合编码'what'和'when'预测

Joint encoding of "what" and "when" predictions through error-modulated plasticity in biologically-plausible spiking networks

该研究通过生物合理脉冲网络中的误差调节可塑性，展示了如何联合编码'what'和'when'预测，实现灵活的预测认知。