Information processing in the neocortex is performed by GABAergic interneurons that are integrated with excitatory neurons into precisely structured circuits. To reveal how each neuron type shapes sensory representations, we measured spikes and membrane potential of specific types of neurons in the barrel cortex while mice performed an active, whisker-dependent object localization task. Whiskers were tracked with millisecond precision. Fast-spiking (FS) neurons were activated by touch with short latency and by whisking. FS neurons track thalamic input and provide feedforward inhibition. Somatostatin (SOM)-expressing neurons were also excited by touch, but with a delay (5 ms) compared to excitatory (E) and FS neurons. SOM neurons monitor local excitation and provide feedback inhibition. Vasoactive intestinal polypeptide (VIP)-expressing neurons were not driven by touch but elevated their spike rate during whisking, disinhibiting E and FS neurons. Our data reveal rules of recruitment for specific interneuron types, providing foundations for understanding cortical computations.

Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity.