Probabilistic encoding and feature selectivity in the somatosensory pathway
Gollnick, Clare Ann
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Our sensory experiences are encoded in the patterns of activity of the neurons in our brain. While we know we are capable of sensing and responding to a constantly changing sensory environment, we often study neural activity by repeatedly presenting the same stimulus and analyzing the average neural response. It is not understood how the average neural response represents the dynamic neural activity that produces our perceptions. In this work, we use functional imaging of the rodent primary somatosensory cortex, specifically the whisker representations, and apply classic signal-detection methods to test the predictive power of the average neural response. Stimulus features such as intensity are thought to be perceptually separable from the average representation; however, we show that stimulus intensity cannot be reliably decoded from neural activity from only a single experience. Instead, stimulus intensity was encoded only across many experiences. We observed this probabilistic neural code in multiple classic sensory paradigms including complex temporal stimuli (pairs of whisker deflections) and multi-whisker stimuli. These data suggest a novel framework for the encoding of stimulus features in the presence of high-neural variability. Specifically we suggest that our brains can compensate for unreliability by encoding information redundantly across cortical space. This thesis predicts that a somatosensory stimulus is not encoded identically each time it is experienced; instead, our brains use multiple redundant pathways to create a reliable sensory percept.