Topic: linear mappings

Animals can learn and perform many behaviors without interference. However, it is unclear how changes in neural activity during learning avoid interference with activity underlying similar behaviors. Recently, Losey et al. (bioRxiv, 2022) identified a possible explanation by examining monkey motor cortical population activity under a brain-computer interface (BCI) paradigm where linear mappings controlled a computer cursor. The activity produced for a known mapping was altered after learning a new mapping in a way that made the activity more beneficial for this new mapping. That is, the activi

The mapping between neural activity and external behavioral variables can change across experimental conditions and behaviors. For example, in prefrontal cortex, the coding of a stimulus’s direction is different soon after stimulus presentation, versus during the delay period. Similarly, for a monkey performing a wrist movement task, the same linear mappings (decoders) cannot simultaneously predict movements when pushing against a spring, versus freely moving. While neural network-based decoders can have the capacity to accurately perform mappings across changing conditions, they are challengi