This can be likened to the way that words such as ‘light’ and ‘night’ have similar structures but different meanings. analyzed the responses of groups of 20 cells to each stimulus and found that physically similar firing patterns were not particularly likely to encode the same stimulus. #Entropy synonym seriesDividing the 10 second clip into short segments provided a series of 500 stimuli, which the network had been exposed to more than 600 times. First, 10 second video clips of natural scenes and artificial stimuli were played on a loop to a sample of retina taken from a salamander, and the responses of nearly 100 neurons in the sample were recorded for two hours. This has brought us a step closer to cracking the neural code. have now modeled the effects of noise in a network of neurons in the retina (found at the back of the eye), and, in doing so, have provided insights into how the brain solves this problem. This means that the brain cannot simply link a single unchanging pattern of firing with each stimulus, because these firing patterns are often distorted by biophysical noise. Furthermore, neurons are inherently noisy and their response to identical stimuli may vary considerably in the number of spikes and their timing. This task is challenging in part because of its scale-vast numbers of stimuli are encoded by huge numbers of neurons that can send their spikes in many different combinations. #Entropy synonym codeCracking this code would thus enable us to predict the patterns of nerve impulses that would occur in response to specific stimuli, and ‘decode’ which stimuli had produced particular patterns of impulses. The information is encoded as patterns of electrical pulses or ‘spikes’, which other brain regions must be able to decipher. Our ability to perceive the world is dependent on information from our senses being passed between different parts of the brain. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns. This organization is highly reminiscent of the design of engineered codes. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons.
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