Single neurons

Neural systems are context-sensitive: the encoding of a stimulus depends on the temporal, spatial and semantic context within which it is embedded. In many systems, typical features that are represented adjust according to the signal-to-noise ratio, and the coding range adapts to the typical range of stimuli. What are the rules that govern these processes? What mechanisms support it? How long does adaptation take?

Alongside fast adaptive processes that normalize response functions to the scale of the stimulus, there are slower processes that depend on the history of stimulus statistics. We explore this from several perspectives: finding functional descriptions of the behavior, determining underlying mechanisms, and deriving normative models that may explain it.

We aim to translate complex conductance-based models into functional models that give an intuitive understanding of the input features that drive neuronal responses. We have shown that during development, single neurons acquire the ability to normalize inputs. In collaboration with the Moody lab, we examine how this changes the network’s ability to transmit information on different timescales.