Cortical cholinergic signaling controls the detection of cues.

The cortical cholinergic input system has been described as a neuromodulator system that influences broadly defined behavioral and brain states. The discovery of phasic, trial-based increases in extracellular choline (transients), resulting from the hydrolysis of newly released acetylcholine (ACh), in the cortex of animals reporting the presence of cues suggests that ACh may have a more specialized role in cognitive processes. Here we expressed channelrhodopsin or halorhodopsin in basal forebrain cholinergic neurons of mice with optic fibers directed into this region and prefrontal cortex. Cholinergic transients, evoked in accordance with photostimulation parameters determined in vivo, were generated in mice performing a task necessitating the reporting of cue and noncue events. Generating cholinergic transients in conjunction with cues enhanced cue detection rates. Moreover, generating transients in noncued trials, where cholinergic transients normally are not observed, increased the number of invalid claims for cues. Enhancing hits and generating false alarms both scaled with stimulation intensity. Suppression of endogenous cholinergic activity during cued trials reduced hit rates. Cholinergic transients may be essential for synchronizing cortical neuronal output driven by salient cues and executing cue-guided responses.

Acetylcholine Release in Prefrontal Cortex Promotes Gamma Oscillations and Theta-Gamma Coupling during Cue Detection.

The capacity for using external cues to guide behavior ("cue detection") constitutes an essential aspect of attention and goal-directed behavior. The cortical cholinergic input system, via phasic increases in prefrontal acetylcholine release, plays an essential role in attention by mediating such cue detection. However, the relationship between cholinergic signaling during cue detection and neural activity dynamics in prefrontal networks remains unclear. Here we combined subsecond measures of cholinergic signaling, neurophysiological recordings, and cholinergic receptor blockade to delineate the cholinergic contributions to prefrontal oscillations during cue detection in rats. We first confirmed that detected cues evoke phasic acetylcholine release. These cholinergic signals were coincident with increased neuronal synchrony across several frequency bands and the emergence of theta-gamma coupling. Muscarinic and nicotinic cholinergic receptors both contributed specifically to gamma synchrony evoked by detected cues, but the effects of blocking the two receptor subtypes were dissociable. Blocking nicotinic receptors primarily attenuated high-gamma oscillations occurring during the earliest phases of the cue detection process, while muscarinic (M1) receptor activity was preferentially involved in the transition from high to low gamma power that followed and corresponded to the mobilization of networks involved in cue-guided decision making. Detected cues also promoted coupling between gamma and theta oscillations, and both nicotinic and muscarinic receptor activity contributed to this process. These results indicate that acetylcholine release coordinates neural oscillations during the process of cue detection.SIGNIFICANCE STATEMENT The capacity of learned cues to direct attention and guide responding ("cue detection") is a key component of goal-directed behavior. Rhythmic neural activity and increases in acetylcholine release in the prefrontal cortex contribute to this process; however, the relationship between these neuronal mechanisms is not well understood. Using a combination of in vivo neurochemistry, neurophysiology, and pharmacological methods, we demonstrate that cue-evoked acetylcholine release, through distinct actions at both nicotinic and muscarinic receptors, triggers a procession of neural oscillations that map onto the multiple stages of cue detection. Our data offer new insights into cholinergic function by revealing the temporally orchestrated changes in prefrontal network synchrony modulated by acetylcholine release during cue detection.

Running speed and REM sleep control two distinct modes of rapid interhemispheric communication.

Rhythmic gamma-band communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, using multisite recordings in both rats and mice, we show that even faster ∼140 Hz rhythms are robustly anti-phase across cortical hemispheres, visually resembling splines, the interlocking teeth on mechanical gears. Splines are strongest in superficial granular retrosplenial cortex, a region important for spatial navigation and memory. Spline-frequency interhemispheric communication becomes more coherent and more precisely anti-phase at faster running speeds. Anti-phase splines also demarcate high-activity frames during REM sleep. While splines and associated neuronal spiking are anti-phase across retrosplenial hemispheres during navigation and REM sleep, gamma-rhythmic interhemispheric communication is precisely in-phase. Gamma and splines occur at distinct points of a theta cycle and thus highlight the ability of interhemispheric cortical communication to rapidly switch between in-phase (gamma) and anti-phase (spline) modes within individual theta cycles during both navigation and REM sleep.

Transient 23-30 Hz oscillations in mouse hippocampus during exploration of novel environments.

The hippocampus is a key brain structure for the encoding of new experiences and environments. Hippocampal activity shows distinct oscillatory patterns, but the relationships between oscillations and memory are not well understood. Here we describe bursts of hippocampal approximately 23-30 Hz (beta2) oscillations in mice exploring novel, but not familiar, environments. In marked contrast to the relatively invariant approximately 8 Hz theta rhythm, beta2 power was weak during the very first lap of the novel environment, increased sharply as the mice reencountered their start point, then persisted for only a few minutes. Novelty-evoked oscillations reflected precise synchronization of individual neurons, and participating pyramidal cells showed a selective enhancement of spatial specificity. Through focal viral manipulations, we found that novelty-evoked oscillations required functional NMDA receptors in CA3, a subregion critical for fast oscillations in vitro. These findings suggest that beta2 oscillations indicate a hippocampal dynamic state that facilitates the formation of unique contextual representations.

Mesolimbic dopamine signals the value of work.

Dopamine cell firing can encode errors in reward prediction, providing a learning signal to guide future behavior. Yet dopamine is also a key modulator of motivation, invigorating current behavior. Existing theories propose that fast (phasic) dopamine fluctuations support learning, whereas much slower (tonic) dopamine changes are involved in motivation. We examined dopamine release in the nucleus accumbens across multiple time scales, using complementary microdialysis and voltammetric methods during adaptive decision-making. We found that minute-by-minute dopamine levels covaried with reward rate and motivational vigor. Second-by-second dopamine release encoded an estimate of temporally discounted future reward (a value function). Changing dopamine immediately altered willingness to work and reinforced preceding action choices by encoding temporal-difference reward prediction errors. Our results indicate that dopamine conveys a single, rapidly evolving decision variable, the available reward for investment of effort, which is employed for both learning and motivational functions.

Memory formation: from network structure to neural dynamics.

Understanding the neural correlates of brain function is an extremely challenging task, since any cognitive process is distributed over a complex and evolving network of neurons that comprise the brain. In order to quantify observed changes in neuronal dynamics during hippocampal memory formation, we present metrics designed to detect directional interactions and the formation of functional neuronal ensembles. We apply these metrics to both experimental and model-derived data in an attempt to link anatomical network changes with observed changes in neuronal dynamics during hippocampal memory formation processes. We show that the developed model provides a consistent explanation of the anatomical network modifications that underlie the activity changes observed in the experimental data.