On decoding the responses of a population of neurons from short time windows
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Dynamics of the firing probability of noisy integrate-and-fire neurons
Neural Computation
Bayesian computation in recurrent neural circuits
Neural Computation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Neural Computation
Coding static natural images using spiking event times: do neurons Cooperate?
IEEE Transactions on Neural Networks
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The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.