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Abstract Can the transcriptomic profile of a neuron predict its physiological properties? Using a Patch-seq dataset of the primary visual cortex, we addressed this question by focusing on spike rate adaptation (SRA), a well-known phenomenon that depends on small conductance calcium (Ca)-dependent potassium (SK) channels. We first show that in parvalbumin-expressing (PV) and somatostatin-expressing (SST) interneurons (INs), expression levels of genes encoding the ion channels underlying action potential generation are correlated with the half-width (HW) of spikes. Surprisingly, the SK encoding gene is not correlated with the degree of SRA (dAdap). Instead, genes that encode proteins upstream from the SK current are correlated with dAdap, a finding validated by a different dataset from the mouse’s primary motor cortex that includes pyramidal cells and interneurons, as well as physiological datasets from multiple regions of macaque monkeys. Finally, we construct a minimal model to reproduce observed heterogeneity across cells, with testable predictions. 

Abstract Networks throughout physics and biology leverage spatiotemporal dynamics for computation. However, the connection between structure and computation remains unclear. Here, we study a complex-valued neural network (cv-NN) with linear interactions and phase-delays. We report the cv-NN displays sophisticated spatiotemporal dynamics, which we then use, in combination with a nonlinear readout, for computation. The cv-NN can instantiate dynamics-based logic gates, encode short-term memories, and mediate secure message passing through a combination of interactions and phase-delays. The computations in this system can be fully described in an exact, closed-form mathematical expression. Finally, using direct intracellular recordings of neurons in slices from neocortex, we demonstrate that computations in the cv-NN are decodable by living biological neurons as the nonlinear readout. These results demonstrate that complex-valued linear systems can perform sophisticated computations, while also being exactly solvable. Taken together, these results open future avenues for design of highly adaptable, bio-hybrid computing systems that can interface seamlessly with other neural networks. 

The stress response necessitates an immediate boost in vital physiological functions from their homeostatic operation to an elevated emergency response. However, the neural mechanisms underlying this state-dependent change remain largely unknown. Using a combination of in vivo and ex vivo electrophysiology with computational modeling, we report that corticotropin releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN), the effector neurons of hormonal stress response, rapidly transition between distinct activity states through recurrent inhibition. Specifically, in vivo optrode recording shows that under non-stress conditions, CRH PVN neurons often fire with rhythmic brief bursts (RB), which, somewhat counterintuitively, constrains firing rate due to long (~2 s) interburst intervals. Stressful stimuli rapidly switch RB to continuous single spiking (SS), permitting a large increase in firing rate. A spiking network model shows that recurrent inhibition can control this activity-state switch, and more broadly the gain of spiking responses to excitatory inputs. In biological CRH PVN neurons ex vivo, the injection of whole-cell currents derived from our computational model recreates the in vivo-like switch between RB and SS, providing direct evidence that physiologically relevant network inputs enable state-dependent computation in single neurons. Together, we present a novel mechanism for state-dependent activity dynamics in CRH PVN neurons. 

Abstract Neuronal spiking activity encoding working memory (WM) is robust in primate association cortices but weak or absent in early sensory cortices. This may be linked to changes in the proportion of neuronal types across areas that influence circuits’ ability to generate recurrent excitation. We recorded neuronal activity from areas middle temporal (MT), medial superior temporal (MST), and the lateral prefrontal cortex (LPFC) of monkeys performing a WM task and classified neurons as narrow (NS) and broad spiking (BS). The ratio NS/BS decreased from MT > MST > LPFC. We analyzed the Allen Institute database of ex vivo mice/human intracellular recordings to interpret our data. Our analysis suggests that NS neurons correspond to parvalbumin (PV) or somatostatin (SST) interneurons while BS neurons are pyramidal (P) cells or vasoactive intestinal peptide (VIP) interneurons. We labeled neurons in monkey tissue sections of MT/MST and LPFC and found that the proportion of PV in cortical layers 2/3 decreased, while the proportion of CR cells increased from MT/MST to LPFC. Assuming that primate CR/CB/PV cells perform similar computations as mice VIP/SST/PV cells, our results suggest that changes in the proportion of CR and PV neurons in layers 2/3 cells may favor the emergence of activity encoding WM in association areas.