Efficient coding hypothesis
One goal of sensory neuroscience is to decipher the meaning of these spikes in order to understand how the brain represents and processes information about the outside world.
The development of Barlow's hypothesis was influenced by information theory introduced by Claude Shannon only a decade before.
The spiking code aims to maximize available channel capacity by minimizing the redundancy between representational units.
[2] A key prediction of the efficient coding hypothesis is that sensory processing in the brain should be adapted to natural stimuli.
Neurons in the visual (or auditory) system should be optimized for coding images (or sounds) representative of those found in nature.
While the retinal receptors can receive information at 10^9 bit/s, the optic nerve, which is composed of 1 million ganglion cells transmitting at 1 bit/sec, only has a transmission capacity of 10^6 bit/s.
[5] Thus, the hypothesis states that neurons should encode information as efficiently as possible in order to maximize neural resources.
[6] Additionally, it has been argued that the visual system takes advantage of redundancies in inputs in order to transmit as much information as possible while using the fewest resources.
[5] Simoncelli and Olshausen outline the three major concepts that are assumed to be involved in the development of systems neuroscience: One assumption used in testing the Efficient Coding Hypothesis is that neurons must be evolutionarily and developmentally adapted to the natural signals in their environment.
[8] Central to Barlow's hypothesis is information theory, which when applied to neuroscience, argues that an efficiently coding neural system "should match the statistics of the signals they represent".
[8] However, researchers have thought that ICA is limited because it assumes that the neural response is linear, and therefore insufficiently describes the complexity of natural images.
Researchers have found that the three classes of cone receptors in the retina can accurately code natural images and that color is decorrelated already in the LGN.
[6] The second approach is to measure a neural system responding to a natural environment, and analyze the results to see if there are any statistical properties to this response.
[6] A third approach is to derive the necessary and sufficient conditions under which an observed neural computation is efficient, and test whether empirical stimulus statistics satisfy them.
[14] They then compared the actual information transmission as observed in real retinal ganglion cells to this optimal model to determine the efficiency.
Analyzing actual neural system in response to natural images In a report in Science from 2000, William E. Vinje and Jack Gallant outlined a series of experiments used to test elements of the efficient coding hypothesis, including a theory that the non-classical receptive field (nCRF) decorrelates projections from the primary visual cortex.
[16] They also hypothesized that interactions between the classical receptive field (CRF) and the nCRF produced this pattern of sparse coding during the viewing of these natural scenes.
In order to test this, they created eye-scan paths and also extracted patches that ranged in size from 1-4 times the diameter of the CRF.
In conclusion, the experiments of Vinje and Gallant showed that the V1 uses sparse code by employing both the CRF and nCRF when viewing natural images, with the nCRF showing a definitive decorrelating effect on neurons which may increase their efficiency by increasing the amount of independent information they carry.
They propose that the cells may represent the individual components of a given natural scene, which may contribute to pattern recognition[16] Another study done by Baddeley et al. had shown that firing-rate distributions of cat visual area V1 neurons and monkey inferotemporal (IT) neurons were exponential under naturalistic conditions, which implies optimal information transmission for a fixed average rate of firing.
De Polavieja later argued that this discrepancy was due to the fact that the exponential solution is correct only for the noise-free case, and showed that by taking noise into consideration, one could account for the observed results.
[17] The researchers played natural image movies in front of cats and used a multielectrode array to record neural signals.
It hypothesizes that biological agents optimize not only their neural coding, but also their behavior to contribute to an efficient sensory representation of the environment.
[6] In his review article Simoncelli notes that perhaps we can interpret redundancy in the Efficient Coding Hypothesis a bit differently: he argues that statistical dependency could be reduced over "successive stages of processing", and not just in one area of the sensory pathway.
[6] Yet, recordings by Hung et al. at the end of the visual pathway also show strong layer-dependent correlations to naturalistic objects and in ongoing activity.
[6] Difficult to test: Estimation of information-theoretic quantities requires enormous amounts of data, and is thus impractical for experimental verification.
[9] This shows that efficient coding of noise data offered perceptual benefits and provided the listeners with more information.
