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The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent sp ...
This paper proposes a novel algorithmic framework to solve image restoration problems under sparsity assumptions. As usual, the reconstructed image is the minimum of an objective functional that consists of a data fidelity term and an l1 regularization. Ho ...
Institute of Electrical and Electronics Engineers2013
Cortical neurons continuously transform sets of incoming spike trains into output spike trains. This input-output transformation is referred to as single-neuron computation and constitutes one of the most fundamental process in the brain. A deep understand ...
The ability to move in complex environments is a fundamental requirement for robots to be a part of our daily lives. Increasing the controller complexity may be a desirable choice in order to obtain an improved performance. However, these two aspects may p ...
An important feature of the nervous system is its ability to adapt to new stimuli. This adaptation allows for optimal encoding of the incoming information by dynamically changing the coding strategy based upon the incoming inputs to the neuron. At the leve ...
Starfish and octopuses control their infinite degree of- freedom arms with panache—capabilities typical of nature where the distribution of reflex-like intelligence throughout soft muscular networks greatly outperforms anything hard, heavy, and man-made. D ...
Naturally, neuroscientists look at the brain from the outside when measuring how the flow of information unfolds over space and time. A neuron, on the other hand, can only 'see' through its connections, and they are spatiotemporally limited. Hence, the neu ...
Neurons are spatially extended structures that receive and process inputs on their dendrites. It is generally accepted that neuronal computations arise from the active integration of synaptic inputs along a dendrite between the input location and the locat ...
In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge su ...
In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge su ...
