Demixing population activity in higher cortical areas.

摘要: Neural responses in higher cortical areas often display a baffling complexity. In animals performing behavioral tasks, single neurons will typically encode several parameters simultaneously, such as stimuli, rewards, decisions, etc. When dealing with this large heterogeneity of responses, cells are conventionally classified into separate response categories using various statistical tools. However, classical approach usually fails to account for the distributed nature representations areas. Alternatively, principal component analysis or related techniques can be employed reduce complexity data set while retaining distributional aspect population activity. These methods, however, fail explicitly extract task from neural responses. Here we suggest coordinate transformation that seeks ameliorate these problems by combining advantages both methods. Our basic insight is variance firing rates have different origins (such changes stimulus, reward, passage time), and that, instead lumping them together, does, need treat sources separately. We present method an orthogonal captured falls subspaces maximized within subspaces. Using simulated examples, show how used demix heterogeneous may help lift fog