
Populationcoding and Dynamicneurons improved Spiking Actor Network for Reinforcement Learning
With the Deep Neural Networks (DNNs) as a powerful function approximator...
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Sparse and silent coding in neural circuits
Sparse coding algorithms are about finding a linear basis in which signa...
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NonAssociative Learning Representation in the Nervous System of the Nematode Caenorhabditis elegans
Caenorhabditis elegans (C. elegans) illustrated remarkable behavioral pl...
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A network of spiking neurons for computing sparse representations in an energy efficient way
Computing sparse redundant representations is an important problem both ...
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A biologically plausible neural network for Slow Feature Analysis
Learning latent features from time series data is an important problem i...
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Neuron's Eye View: Inferring Features of Complex Stimuli from Neural Responses
Experiments that study neural encoding of stimuli at the level of indivi...
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Colour Terms: a Categorisation Model Inspired by Visual Cortex Neurons
Although it seems counterintuitive, categorical colours do not exist as...
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Online computation of sparse representations of time varying stimuli using a biologically motivated neural network
Natural stimuli are highly redundant, possessing significant spatial and temporal correlations. While sparse coding has been proposed as an efficient strategy employed by neural systems to encode sensory stimuli, the underlying mechanisms are still not well understood. Most previous approaches model the neural dynamics by the sparse representation dictionary itself and compute the representation coefficients offline. In reality, faced with the challenge of constantly changing stimuli, neurons must compute the sparse representations dynamically in an online fashion. Here, we describe a leaky linearized Bregman iteration (LLBI) algorithm which computes the time varying sparse representations using a biologically motivated network of leaky rectifying neurons. Compared to previous attempt of dynamic sparse coding, LLBI exploits the temporal correlation of stimuli and demonstrate better performance both in representation error and the smoothness of temporal evolution of sparse coefficients.
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