Topic: input-output transformation

The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the under-lying functional architecture as unitary, performing a single type of elementary computation, either once, or cascaded multiple times. Here, we show that the input-output transformation of CA1 pyramidal cells during phase precession in the ‘theta-state’ is instead best captured by two distinct functional architectures operating in parallel. We use statistically principled methods to f