Neural Encoding

Neural encoding asks how sensory input, language, actions, and memories are reflected in patterns of neural activity, including which neurons respond and how timing and population structure carry information. Researchers build encoding models from vision, language, and audio representations, using methods such as regression, attention, latent-variable models, and cross-subject transfer to predict fMRI, EEG, ECoG, and single-neuron responses. A related line studies rate, phase, and spike-timing codes, as well as sparse and event-based encoders for spiking networks and low-power hardware. A central challenge is that accurate prediction or representational alignment does not establish a shared brain–model mechanism, while generalization, interpretability, and task-specific coding often remain in tension.