Efficient coding hypothesis

The efficient coding hypothesis was proposed by Horace Barlow in 1961 as a theoretical model of sensory neuroscience in the brain. Within the brain, neurons communicate with one another by sending electrical impulses referred to as action potentials or spikes. Barlow hypothesized that the spikes in the sensory system formed a neural code for efficiently representing sensory information. By efficient it is understood that the code minimized the number of spikes needed to transmit a given signal. This is somewhat analogous to transmitting information across the internet, where different file formats can be used to transmit a given image. Different file formats require different numbers of bits for representing the same image at a given distortion level, and some are better suited for representing certain classes of images than others. According to this model, the brain is thought to use a code which is suited for representing visual and audio information which is representative of an organism's natural environment .

The efficient coding hypothesis was proposed by Horace Barlow in 1961 as a theoretical model of sensory neuroscience in the brain. Within the brain, neurons communicate with one another by sending electrical impulses referred to as action potentials or spikes. Barlow hypothesized that the spikes in the sensory system formed a neural code for efficiently representing sensory information. By efficient it is understood that the code minimized the number of spikes needed to transmit a given signal. This is somewhat analogous to transmitting information across the internet, where different file formats can be used to transmit a given image. Different file formats require different numbers of bits for representing the same image at a given distortion level, and some are better suited for representing certain classes of images than others. According to this model, the brain is thought to use a code which is suited for representing visual and audio information which is representative of an organism's natural environment . ===Examples of these approaches=== 1. Predicted model approach In one study by Doi et al. in 2012, the researchers created a predicted response model of the retinal ganglion cells that would be based on the statistics of the natural images used, while considering noise and biological constraints. They then compared the actual information transmission as observed in real retinal ganglion cells to this optimal model to determine the efficiency. They found that the information transmission in the retinal ganglion cells had an overall efficiency of about 80% and concluded that "the functional connectivity between cones and retinal ganglion cells exhibits unique spatial structure...consistent with coding efficiency. A study by van Hateren and Ruderman in 1998 used ICA to analyze video-sequences and compared how a computer analyzed the independent components of the image to data for visual processing obtained from a cat in DeAngelis et al. 1993. The researchers described the independent components obtained from a video sequence as the "basic building blocks of a signal", with the independent component filter (ICF) measuring "how strongly each building block is present". They hypothesized that if simple cells are organized to pick out the "underlying structure" of images over time then cells should act like the independent component filters. They found that the ICFs determined by the computer were similar to the "receptive fields" that were observed in actual neurons. 2. 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. To test this, they took recordings from the V1 neurons in awake macaques during "free viewing of natural images and conditions" that simulated natural vision conditions. The researchers hypothesized that the V1 uses sparse code, which is minimally redundant and "metabolically more efficient". 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. They found that the sparseness of the coding increased with the size of the patch. Larger patches encompassed more of the nCRF—indicating that the interactions between these two regions created sparse code. Additionally as stimulus size increased, so did the sparseness. This suggests that the V1 uses sparse code when natural images span the entire visual field. The CRF was defined as the circular area surrounding the locations where stimuli evoked action potentials. They also tested to see if stimulation of the nCRF increased the independence of the responses from the V1 neurons by randomly selecting pairs of neurons. They found that indeed, the neurons were more greatly decoupled upon stimulation of the nCRF. 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 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. A subsequent study of monkey IT neurons found that only a minority were well described by an exponential firing distribution. 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. A study by Dan, Attick, and Reid in 1996 used natural images to test the hypothesis that early on in the visual pathway, incoming visual signals will be decorrelated to optimize efficiency. This decorrelation can be observed as the '"whitening" of the temporal and spatial power spectra of the neuronal signals". The researchers played natural image movies in front of cats and used a multielectrode array to record neural signals. This was achieved by refracting the eyes of the cats and then contact lenses being fitted into them. They found that in the LGN, the natural images were decorrelated and concluded, "the early visual pathway has specifically adapted for efficient coding of natural visual information during evolution and/or development". == Extensions== One of the implications of the efficient coding hypothesis is that the neural coding depends upon the statistics of the sensory signals. These statistics are a function of not only the environment (e.g., the statistics of the natural environment), but also the organism's behavior (e.g., how it moves within that environment). However, perception and behavior are closely intertwined in the perception-action cycle. For example, the process of vision involves various kinds of eye movements. An extension to the efficient coding hypothesis called active efficient coding (AEC) extends efficient coding to active perception. 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. Along these lines, models for the development of active binocular vision, active visual tracking, and accommodation control have been proposed. The brain has limited resources to process information, in vision this is manifested as the visual attentional bottleneck. The bottleneck forces the brain to select only a small fraction of visual input information for further processing, as merely coding information efficiently is no longer sufficient. A subsequent theory, V1 Saliency Hypothesis, has been developed on exogenous attentional selection of visual input information for further processing guided by a bottom-up saliency map in the primary visual cortex.

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