Neuronal identity is not static: An input-driven perspective

Neuronal classification based on morphology, electrophysiology, and molecular markers is often considered static. Here, we challenge this view, showing that functional classification depends on input patterns. Using single-cell recordings from layer 2/3 barrel cortex neurons in mice, we compared responses to step-and-hold versus dynamic frozen noise inputs that mimic presynaptic activity. Action potential and waveform-based classifications varied significantly, highlighting the dynamic nature of neuronal identity. To assess the contribution of input versus neuronal attributes toward classification, we analyzed four attribute sets, namely action potential, passive biophysical, adaptation currents, and linear input filters derived via spike-triggered averages (STA). Our findings revealed that the STA, which captures a neuron’s selective responsiveness to presynaptic activity, explained the most variance within the population. This highlights input-driven dynamics as key to functional identity, emphasizing the need for physiologically relevant inputs in defining neuronal classes and shifting the focus from static properties to dynamic functional diversity.Author summary: Traditionally, scientists have grouped neurons into fixed types based on their shape, electrical activity, or the molecules they express. In this study, we show that this approach misses an important point: a neuron’s behavior can change depending on the kind of input it receives. By recording the electrical activity of neurons in the mouse brain and testing them with both a static and a changing input, we found that the way neurons are classified can shift dramatically depending on the input they receive. Our results suggest that to truly understand what makes each neuron functionally unique, we need to look at how they respond to a specific pattern in the input rather than static and firing properties. This means that neuronal identity is not fixed, but is shaped by input, and we encourage future research to focus on these dynamic properties when studying neurons.