Juxtacellular recordings from identified neurons in the mouse locus coeruleus.

The locus coeruleus (LC) is the primary source of noradrenergic transmission in the mammalian central nervous system. This small pontine nucleus consists of a densely packed nuclear core-which contains the highest density of noradrenergic neurons-embedded within a heterogeneous surround of non-noradrenergic cells. This local heterogeneity, together with the small size of the LC, has made it particularly difficult to infer noradrenergic cell identity based on extracellular sampling of in vivo spiking activity. Moreover, the relatively high cell density, background activity and synchronicity of LC neurons have made spike identification and unit isolation notoriously challenging. In this study, we aimed at bridging these gaps by performing juxtacellular recordings from single identified neurons within the mouse LC complex. We found that noradrenergic neurons (identified by tyrosine hydroxylase, TH, expression; TH-positive) and intermingled putatively non-noradrenergic (TH-negative) cells displayed similar morphologies and responded to foot shock stimuli with excitatory responses; however, on average, TH-positive neurons exhibited more prominent foot shock responses and post-activation firing suppression. The two cell classes also displayed different spontaneous firing rates, spike waveforms and temporal spiking properties. A logistic regression classifier trained on spontaneous electrophysiological features could separate the two cell classes with 76% accuracy. Altogether, our results reveal in vivo electrophysiological correlates of TH-positive neurons, which can be useful for refining current approaches for the classification of LC unit activity.

Plasticity of neuronal dynamics in the lateral habenula for cue-punishment associative learning.

The brain's ability to associate threats with external stimuli is vital to execute essential behaviours including avoidance. Disruption of this process contributes instead to the emergence of pathological traits which are common in addiction and depression. However, the mechanisms and neural dynamics at the single-cell resolution underlying the encoding of associative learning remain elusive. Here, employing a Pavlovian discrimination task in mice we investigate how neuronal populations in the lateral habenula (LHb), a subcortical nucleus whose excitation underlies negative affect, encode the association between conditioned stimuli and a punishment (unconditioned stimulus). Large population single-unit recordings in the LHb reveal both excitatory and inhibitory responses to aversive stimuli. Additionally, local optical inhibition prevents the formation of cue discrimination during associative learning, demonstrating a critical role of LHb activity in this process. Accordingly, longitudinal in vivo two-photon imaging tracking LHb calcium neuronal dynamics during conditioning reveals an upward or downward shift of individual neurons' CS-evoked responses. While recordings in acute slices indicate strengthening of synaptic excitation after conditioning, support vector machine algorithms suggest that postsynaptic dynamics to punishment-predictive cues represent behavioral cue discrimination. To examine the presynaptic signaling in LHb participating in learning we monitored neurotransmitter dynamics with genetically-encoded indicators in behaving mice. While glutamate, GABA, and serotonin release in LHb remain stable across associative learning, we observe enhanced acetylcholine signaling developing throughout conditioning. In summary, converging presynaptic and postsynaptic mechanisms in the LHb underlie the transformation of neutral cues in valued signals supporting cue discrimination during learning.

Neural rate difference model can account for lateralization of high-frequency stimuli.

Lateralization of complex high-frequency sounds is conveyed by interaural level differences (ILDs) and interaural time differences (ITDs) in the envelope. In this work, the authors constructed an auditory model and simulate data from three previous behavioral studies obtained with, in total, over 1000 different amplitude-modulated stimuli. The authors combine a well-established auditory periphery model with a functional count-comparison model for binaural excitatory-inhibitory (EI) interaction. After parameter optimization of the EI-model stage, the hemispheric rate-difference between pairs of EI-model neurons relates linearly with the extent of laterality in human listeners. If a certain ILD and a certain envelope ITD each cause a similar extent of laterality, they also produce a similar rate difference in the same model neurons. After parameter optimization, the model accounts for 95.7% of the variance in the largest dataset, in which amplitude modulation depth, rate of modulation, modulation exponent, ILD, and envelope ITD were varied. The model also accounts for 83% of the variances in each of the other two datasets using the same EI model parameters.