The importance of mixed selectivity in complex cognitive tasks.

Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input-output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons.

Dopamine neurons modulate neural encoding and expression of depression-related behaviour.

Major depression is characterized by diverse debilitating symptoms that include hopelessness and anhedonia. Dopamine neurons involved in reward and motivation are among many neural populations that have been hypothesized to be relevant, and certain antidepressant treatments, including medications and brain stimulation therapies, can influence the complex dopamine system. Until now it has not been possible to test this hypothesis directly, even in animal models, as existing therapeutic interventions are unable to specifically target dopamine neurons. Here we investigated directly the causal contributions of defined dopamine neurons to multidimensional depression-like phenotypes induced by chronic mild stress, by integrating behavioural, pharmacological, optogenetic and electrophysiological methods in freely moving rodents. We found that bidirectional control (inhibition or excitation) of specified midbrain dopamine neurons immediately and bidirectionally modulates (induces or relieves) multiple independent depression symptoms caused by chronic stress. By probing the circuit implementation of these effects, we observed that optogenetic recruitment of these dopamine neurons potently alters the neural encoding of depression-related behaviours in the downstream nucleus accumbens of freely moving rodents, suggesting that processes affecting depression symptoms may involve alterations in the neural encoding of action in limbic circuitry.

Diverging neural pathways assemble a behavioural state from separable features in anxiety.

Behavioural states in mammals, such as the anxious state, are characterized by several features that are coordinately regulated by diverse nervous system outputs, ranging from behavioural choice patterns to changes in physiology (in anxiety, exemplified respectively by risk-avoidance and respiratory rate alterations). Here we investigate if and how defined neural projections arising from a single coordinating brain region in mice could mediate diverse features of anxiety. Integrating behavioural assays, in vivo and in vitro electrophysiology, respiratory physiology and optogenetics, we identify a surprising new role for the bed nucleus of the stria terminalis (BNST) in the coordinated modulation of diverse anxiety features. First, two BNST subregions were unexpectedly found to exert opposite effects on the anxious state: oval BNST activity promoted several independent anxious state features, whereas anterodorsal BNST-associated activity exerted anxiolytic influence for the same features. Notably, we found that three distinct anterodorsal BNST efferent projections-to the lateral hypothalamus, parabrachial nucleus and ventral tegmental area-each implemented an independent feature of anxiolysis: reduced risk-avoidance, reduced respiratory rate, and increased positive valence, respectively. Furthermore, selective inhibition of corresponding circuit elements in freely moving mice showed opposing behavioural effects compared with excitation, and in vivo recordings during free behaviour showed native spiking patterns in anterodorsal BNST neurons that differentiated safe and anxiogenic environments. These results demonstrate that distinct BNST subregions exert opposite effects in modulating anxiety, establish separable anxiolytic roles for different anterodorsal BNST projections, and illustrate circuit mechanisms underlying selection of features for the assembly of the anxious state.

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear "mixed" selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training.SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli-and in particular, to combinations of stimuli ("mixed selectivity")-is a topic of interest. Even though models with random feedforward connectivity are capable of creating computationally relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training.

A prefrontal cortex-brainstem neuronal projection that controls response to behavioural challenge.

The prefrontal cortex (PFC) is thought to participate in high-level control of the generation of behaviours (including the decision to execute actions); indeed, imaging and lesion studies in human beings have revealed that PFC dysfunction can lead to either impulsive states with increased tendency to initiate action, or to amotivational states characterized by symptoms such as reduced activity, hopelessness and depressed mood. Considering the opposite valence of these two phenotypes as well as the broad complexity of other tasks attributed to PFC, we sought to elucidate the PFC circuitry that favours effortful behavioural responses to challenging situations. Here we develop and use a quantitative method for the continuous assessment and control of active response to a behavioural challenge, synchronized with single-unit electrophysiology and optogenetics in freely moving rats. In recording from the medial PFC (mPFC), we observed that many neurons were not simply movement-related in their spike-firing patterns but instead were selectively modulated from moment to moment, according to the animal's decision to act in a challenging situation. Surprisingly, we next found that direct activation of principal neurons in the mPFC had no detectable causal effect on this behaviour. We tested whether this behaviour could be causally mediated by only a subclass of mPFC cells defined by specific downstream wiring. Indeed, by leveraging optogenetic projection-targeting to control cells with specific efferent wiring patterns, we found that selective activation of those mPFC cells projecting to the brainstem dorsal raphe nucleus (DRN), a serotonergic nucleus implicated in major depressive disorder, induced a profound, rapid and reversible effect on selection of the active behavioural state. These results may be of importance in understanding the neural circuitry underlying normal and pathological patterns of action selection and motivation in behaviour.

Optical neural interfaces.

Genetically encoded optical actuators and indicators have changed the landscape of neuroscience, enabling targetable control and readout of specific components of intact neural circuits in behaving animals. Here, we review the development of optical neural interfaces, focusing on hardware designed for optical control of neural activity, integrated optical control and electrical readout, and optical readout of population and single-cell neural activity in freely moving mammals.

Making Sense of Optogenetics.

This review, one of a series of articles, tries to make sense of optogenetics, a recently developed technology that can be used to control the activity of genetically-defined neurons with light. Cells are first genetically engineered to express a light-sensitive opsin, which is typically an ion channel, pump, or G protein-coupled receptor. When engineered cells are then illuminated with light of the correct frequency, opsin-bound retinal undergoes a conformational change that leads to channel opening or pump activation, cell depolarization or hyperpolarization, and neural activation or silencing. Since the advent of optogenetics, many different opsin variants have been discovered or engineered, and it is now possible to stimulate or inhibit neuronal activity or intracellular signaling pathways on fast or slow timescales with a variety of different wavelengths of light. Optogenetics has been successfully employed to enhance our understanding of the neural circuit dysfunction underlying mood disorders, addiction, and Parkinson's disease, and has enabled us to achieve a better understanding of the neural circuits mediating normal behavior. It has revolutionized the field of neuroscience, and has enabled a new generation of experiments that probe the causal roles of specific neural circuit components.

Infralimbic parvalbumin neural activity facilitates cued threat avoidance.

The infralimbic cortex (IL) is essential for flexible behavioral responses to threatening environmental events. Reactive behaviors such as freezing or flight are adaptive in some contexts, but in others a strategic avoidance behavior may be more advantageous. IL has been implicated in avoidance, but the contribution of distinct IL neural subtypes with differing molecular identities and wiring patterns is poorly understood. Here, we study IL parvalbumin (PV) interneurons in mice as they engage in active avoidance behavior, a behavior in which mice must suppress freezing in order to move to safety. We find that activity in inhibitory PV neurons increases during movement to avoid the shock in this behavioral paradigm, and that PV activity during movement emerges after mice have experienced a single shock, prior to learning avoidance. PV neural activity does not change during movement toward cued rewards or during general locomotion in the open field, behavioral paradigms where freezing does not need to be suppressed to enable movement. Optogenetic suppression of PV neurons increases the duration of freezing and delays the onset of avoidance behavior, but does not affect movement toward rewards or general locomotion. These data provide evidence that IL PV neurons support strategic avoidance behavior by suppressing freezing.

Serotonin neurons in mating female mice are activated by male ejaculation.

Sexual stimulation triggers changes in female physiology and behavior, including sexual satiety and preparing the uterus for pregnancy. Serotonin (5-HT) is an important regulator of reproductive physiology and sexual receptivity, but the relationship between sexual stimulation and 5-HT neural activity in females is poorly understood. Here, we investigated dorsal raphe 5-HT neural activity in female mice during sexual behavior. We found that 5-HT neural activity in mating females peaked specifically upon male ejaculation and remained elevated above baseline until disengagement. Artificial intravaginal mechanical stimulation was sufficient to elicit increased 5-HT neural activity but the delivery of ejaculatory fluids was not. Distal penis expansion ("penile cupping") at ejaculation and forceful expulsion of ejaculatory fluid each provided sufficient mechanical stimulation to elicit 5-HT neuron activation. Our study identifies a female ejaculation-specific signal in a major neuromodulatory system and shows that intravaginal mechanosensory stimulation is necessary and sufficient to drive this signal.

Serotonin neurons in mating female mice are activated by male ejaculation.

Sexual stimulation triggers changes in female physiology and behavior, including sexual satiety and preparing the uterus for pregnancy. Serotonin is an important regulator of reproductive physiology and sexual receptivity, but the relationship between sexual stimulation and serotonin neural activity in females is poorly understood. Here, we investigated dorsal raphe serotonin neural activity in females during sexual behavior. We found that serotonin neural activity in mating females peaked specifically upon male ejaculation, and remained elevated above baseline until disengagement. Artificial intravaginal mechanical stimulation was sufficient to elicit increased 5-HT neural activity but the delivery of ejaculatory fluids was not. Distal penis erectile enlargement ("penile cupping") at ejaculation and forceful expulsion of ejaculatory fluid each provided sufficient mechanical stimulation to elicit serotonin neuron activation. Our study identifies a female ejaculation-specific signal in a major neuromodulatory system and shows that intravaginal mechanosensory stimulation is necessary and sufficient to drive this signal.

Working memory control dynamics follow principles of spatial computing.

Working memory (WM) allows us to remember and selectively control a limited set of items. Neural evidence suggests it is achieved by interactions between bursts of beta and gamma oscillations. However, it is not clear how oscillations, reflecting coherent activity of millions of neurons, can selectively control individual WM items. Here we propose the novel concept of spatial computing where beta and gamma interactions cause item-specific activity to flow spatially across the network during a task. This way, control-related information such as item order is stored in the spatial activity independent of the detailed recurrent connectivity supporting the item-specific activity itself. The spatial flow is in turn reflected in low-dimensional activity shared by many neurons. We verify these predictions by analyzing local field potentials and neuronal spiking. We hypothesize that spatial computing can facilitate generalization and zero-shot learning by utilizing spatial component as an additional information encoding dimension.

Dynamic changes to signal allocation rules in response to variable social environments in house mice.

Urine marking is central to mouse social behavior. Males use depletable and costly urine marks in intrasexual competition and mate attraction. We investigate how males alter signaling decisions across variable social landscapes using thermal imaging to capture spatiotemporal marking data. Thermal recording reveals fine-scale adjustments in urinary motor patterns in response to competition and social odors. Males demonstrate striking winner-loser effects in scent mark allocation effort and timing. Competitive experience primes temporal features of marking and modulates responses to scent familiarity. Males adjust signaling effort, mark latency, and marking rhythm, depending on the scent identities in the environment. Notably, recent contest outcome affects how males respond to familiar and unfamiliar urine. Winners increase marking effort toward unfamiliar relative to familiar male scents, whereas losers reduce marking effort to unfamiliar but increase to familiar rival scents. All males adjust their scent mark timing after a contest regardless of fight outcome, and deposit marks in more rapid bursts during marking bouts. In contrast to this dynamism, initial signal investment predicts aspects of scent marking days later, revealing the possibility of alternative marking strategies among competitive males. These data show that mice flexibly update their signaling decisions in response to changing social landscapes.

Phase-dependent neuronal coding of objects in short-term memory.

The ability to hold multiple objects in memory is fundamental to intelligent behavior, but its neural basis remains poorly understood. It has been suggested that multiple items may be held in memory by oscillatory activity across neuronal populations, but yet there is little direct evidence. Here, we show that neuronal information about two objects held in short-term memory is enhanced at specific phases of underlying oscillatory population activity. We recorded neuronal activity from the prefrontal cortices of monkeys remembering two visual objects over a brief interval. We found that during this memory interval prefrontal population activity was rhythmically synchronized at frequencies around 32 and 3 Hz and that spikes carried the most information about the memorized objects at specific phases. Further, according to their order of presentation, optimal encoding of the first presented object was significantly earlier in the 32 Hz cycle than that for the second object. Our results suggest that oscillatory neuronal synchronization mediates a phase-dependent coding of memorized objects in the prefrontal cortex. Encoding at distinct phases may play a role for disambiguating information about multiple objects in short-term memory.

Tonic activity in lateral habenula neurons acts as a neutral valence brake on reward-seeking behavior.

Survival requires both the ability to persistently pursue goals and the ability to determine when it is time to stop, an adaptive balance of perseverance and disengagement. Neural activity in the lateral habenula (LHb) has been linked to negative valence, but its role in regulating the balance between engaged reward seeking and disengaged behavioral states remains unclear. Here, we show that LHb neural activity is tonically elevated during minutes-long periods of disengagement from reward-seeking behavior, both when due to repeated reward omission (negative valence) and when sufficient reward has been consumed (positive valence). Furthermore, we show that LHb inhibition extends ongoing reward-seeking behavioral states but does not prompt task re-engagement. We find no evidence for similar tonic activity changes in ventral tegmental area dopamine neurons. Our findings support a framework in which tonic activity in LHb neurons suppresses engagement in reward-seeking behavior in response to both negatively and positively valenced factors.

Reduced variability of bursting activity during working memory.

Working memories have long been thought to be maintained by persistent spiking. However, mounting evidence from multiple-electrode recording (and single-trial analyses) shows that the underlying spiking is better characterized by intermittent bursts of activity. A counterargument suggested this intermittent activity is at odds with observations that spike-time variability reduces during task performance. However, this counterargument rests on assumptions, such as randomness in the timing of the bursts, which may not be correct. Thus, we analyzed spiking and LFPs from monkeys' prefrontal cortex (PFC) to determine if task-related reductions in variability can co-exist with intermittent spiking. We found that it does because both spiking and associated gamma bursts were task-modulated, not random. In fact, the task-related reduction in spike variability could largely be explained by a related reduction in gamma burst variability. Our results provide further support for the intermittent activity models of working memory as well as novel mechanistic insights into how spike variability is reduced during cognitive tasks.

Task-dependent changes in short-term memory in the prefrontal cortex.

The prefrontal cortex (PFC) is important for flexible, context-dependent behavioral control. It also plays a critical role in short-term memory maintenance. Though many studies have investigated these functions independently, it is unclear how these two very different processes are realized by a single brain area. To address this, we trained two monkeys on two variants of an object sequence memory task. These tasks had the same memory requirements but differed in how information was read out and used. For the "recognition" task, the monkeys had to remember two sequentially presented objects and then release a bar when a matching sequence was recognized. For the "recall" task, the monkeys had to remember the same sequence of objects but were instead required to recall the sequence and reproduce it with saccadic eye movements when presented with an array of objects. After training, we recorded the activity of PFC neurons during task performance. We recorded 222 neurons during the recognition task, 177 neurons during the recall task, and 248 neurons during the switching task (interleaved blocks of recognition and recall). Task context had a profound influence on neural selectivity for objects. During the recall task, the first object was encoded more strongly than the second object, while during the recognition task, the second object was encoded more strongly. In addition, most of the neurons encoded both the task and the objects, evidence for a single population responsible for these two critical prefrontal functions.

Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain.

1300 nm three-photon calcium imaging has emerged as a useful technique to allow calcium imaging in deep brain regions. Application to large-scale neural activity imaging entails a careful balance between recording fidelity and perturbation to the sample. We calculated and experimentally verified the excitation pulse energy to achieve the minimum photon count required for the detection of calcium transients in GCaMP6s-expressing neurons for 920 nm two-photon and 1320 nm three-photon excitation. By considering the combined effects of in-focus signal attenuation and out-of-focus background generation, we quantified the cross-over depth beyond which three-photon microscopy outpeforms two-photon microscopy in recording fidelity. Brain tissue heating by continuous three-photon imaging was simulated with Monte Carlo method and experimentally validated with immunohistochemistry. Increased immunoreactivity was observed with 150 mW excitation power at 1 and 1.2 mm imaging depths. Our analysis presents a translatable model for the optimization of three-photon calcium imaging based on experimentally tractable parameters.

Intense threat switches dorsal raphe serotonin neurons to a paradoxical operational mode.

Survival depends on the selection of behaviors adaptive for the current environment. For example, a mouse should run from a rapidly looming hawk but should freeze if the hawk is coasting across the sky. Although serotonin has been implicated in adaptive behavior, environmental regulation of its functional role remains poorly understood. In mice, we found that stimulation of dorsal raphe serotonin neurons suppressed movement in low- and moderate-threat environments but induced escape behavior in high-threat environments, and that movement-related dorsal raphe serotonin neural dynamics inverted in high-threat environments. Stimulation of dorsal raphe Î³-aminobutyric acid (GABA) neurons promoted movement in negative but not positive environments, and movement-related GABA neural dynamics inverted between positive and negative environments. Thus, dorsal raphe circuits switch between distinct operational modes to promote environment-specific adaptive behaviors.

Depression: the search for separable behaviors and circuits.

Major depressive disorder can manifest as different combinations of symptoms, ranging from a profound and incapacitating sadness, to a loss of interest in daily life, to an inability to engage in effortful, goal-directed behavior. Recent research has focused on defining the neural circuits that mediate separable features of depression in patients and preclinical animal models, and connections between frontal cortex and brainstem neuromodulators have emerged as candidate targets. The development of methods permitting recording and manipulation of neural circuits defined by connectivity has enabled the investigation of prefrontal-neuromodulatory circuit dynamics in animal models of depression with exquisite precision, a systems-level approach that has brought new insights by integrating these fields of depression research.

Gamma and beta bursts during working memory readout suggest roles in its volitional control.

Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (~50-120 Hz) and beta (~20-35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.