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Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities of the neurons in the ensemble.
Neural decoding is a neuroscience field concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already been encoded and represented in the brain by networks of neurons. [1]
The focus of this article is a comprehensive view of modeling a neural network (technically neuronal network based on neuron model). Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic (ion and neuron), mesoscopic (functional or population), or macroscopic (system) levels.
The Polychrony toolset is an open-source development environment for critical/embedded systems based on SIGNAL, a real-time polychronous dataflow language. It provides a unified model-driven environment to perform design exploration by using top-down and bottom-up design methodologies formally supported by design model transformations from specification to implementation and from synchrony to ...
Digital pattern generators are today available as stand-alone units, add-on hardware modules for other equipment such as a [logic analyzer] or as PC-based equipment.. Stand-alone units are self-contained devices that include everything from the user interface to define the patterns that should be generated to the electronic equipment that actually generates the output signal.
A message stimulus coming from the environment or from another agent to an agent is called a signal. Signals received by a process agent are first placed in a queue (the input port). When the state machine is waiting in a state, if the first signal in the input port is enabled for that state it starts a transition leading to another state.
The signal is typically measured most strongly by the electrodes covering the parietal lobe. The presence, magnitude, topography and timing of this signal are often used as metrics of cognitive function in decision-making processes. While the neural substrates of this ERP component still remain hazy, the reproducibility and ubiquity of this ...
Such linear mapping from EEG to stimulus is an example of neural decoding. [6] A major problem however is the many variations that the very same message can have under diverse physical conditions (speaker or noise, for example). Hence one can have the same EEG signal, but it is uncertain, at least in acoustic terms, what stimulus to map it to.