Uncovering structured responses of neural populations recorded from macaque monkeys with linear support vector machines

When a mammal, such as a macaque monkey, sees a complex natural image, many neurons in its visual cortex respond simultaneously. Here, we provide a protocol for studying the structure of population responses in laminar recordings with a machine learning model, the linear support vector machine. To unravel the role of single neurons in population responses and the structure of noise correlations, we use a multivariate decoding technique on time-averaged responses. For complete details on the use and execution of this protocol, please refer to Koren et al. (2020a).