AI Insight
Neurons in the brain respond unreliably to the same stimulus, showing variable activity across repeated presentations. However, the brain compensates for this single-neuron unreliability through population coding, where the same information is represented across many neurons simultaneously. New analysis suggests that adding more neurons to these populations may continue to improve the quality of visual signal representation, even when neurons share common noise sources.
Why it matters
This finding has implications for understanding how the brain achieves reliable perception despite noisy neural components. The results could inform the development of more robust artificial neural networks and brain-computer interfaces that need to decode information from neural populations.
Understand the Science
Our brains internally represent the outside world through the coordinated activity of billions of neurons. A single neuron responds unreliably: Even when the same stimulus is shown repeatedly, its activity varies from trial to trial. Neuron populations can compensate for this variability by representing the same information across many neurons, a strategy known as population coding.
Source: Adding neurons may keep improving visual signals despite shared noise, analysis predicts