21 Facts About Sparse coding

Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble.

The study of neural Sparse coding involves measuring and characterizing how stimulus attributes, such as light or sound intensity, or motor actions, such as the direction of an arm movement, are represented by neuron action potentials or spikes.

Neural deSparse coding refers to the reverse map, from response to stimulus, and the challenge is to reconstruct a stimulus, or certain aspects of that stimulus, from the spike sequences it evokes.

Whether neurons use rate Sparse coding or temporal Sparse coding is a topic of intense debate within the neuroscience community, even though there is no clear definition of what these terms mean.

Rate Sparse coding is a traditional Sparse coding scheme, assuming that most, if not all, information about the stimulus is contained in the firing rate of the neuron.

Consequently, rate Sparse coding is inefficient but highly robust with respect to the ISI 'noise'.

In rate Sparse coding, learning is based on activity-dependent synaptic weight modifications.

Rate Sparse coding was originally shown by ED Adrian and Y Zotterman in 1926.

Rate Sparse coding models suggest that these irregularities are noise, while temporal Sparse coding models suggest that they encode information.

The interplay between stimulus and enSparse coding dynamics makes the identification of a temporal code difficult.

In temporal Sparse coding, learning can be explained by activity-dependent synaptic delay modifications.

Issue of temporal Sparse coding is distinct and independent from the issue of independent-spike Sparse coding.

The main drawback of such a Sparse coding scheme is its sensitivity to intrinsic neuronal fluctuations.

Specificity of temporal Sparse coding requires highly refined technology to measure informative, reliable, experimental data.

Population Sparse coding is a method to represent stimuli by using the joint activities of a number of neurons.

Experimental studies have revealed that this Sparse coding paradigm is widely used in the sensor and motor areas of the brain.

Population Sparse coding has a number of other advantages as well, including reduction of uncertainty due to neuronal variability and the ability to represent a number of different stimulus attributes simultaneously.

Population Sparse coding is much faster than rate Sparse coding and can reflect changes in the stimulus conditions nearly instantaneously.

Typically an enSparse coding function has a peak value such that activity of the neuron is greatest if the perceptual value is close to the peak value, and becomes reduced accordingly for values less close to the peak value.

Sparse coding code is when each item is encoded by the strong activation of a relatively small set of neurons.

In contrast to sensor-sparse coding, sensor-dense coding implies that all information from possible sensor locations is known.