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 underlying functional architecture as unitary, such
that each synaptic input makes a single contribution to the neuronal response. Here,
we show that the input-output transformation of CA1 pyramidal cells is instead best
captured by two distinct functional architectures operating in parallel. We used statistically
principled methods to fit flexible, yet interpretable, models of the transformation
of input spikes into the somatic ‘‘output’’ voltage and to automatically select among
alternative functional architectures. With dendritic Na + channels blocked, responses
are accurately captured by a single static and global nonlinearity. In contrast, dendritic
Na+ -dependent integration requires a functional architecture with multiple dynamic
nonlinearities and clustered connectivity. These two architectures incorporate distinct
morphological and biophysical properties of the neuron and its synaptic organization.
