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The CA1 pyramidal neurons are embedded in an intricate local circuitry that contains a variety of interneurons. The roles these interneurons play in the regulation of the excitatory synaptic plasticity remains largely understudied. Recent experiments showed that repeated cholinergic activation of 𝛼7 nACh receptors expressed in oriens-lacunosum-moleculare (OLM𝛼2) interneurons could induce LTP in SC-CA1 synapses. We used a biophysically realistic computational model to examine mechanistically how cholinergic activation of OLMa2 interneurons increases SC to CA1 transmission. Our results suggest that, when properly timed, activation of OLMa2 interneurons cancels the feedforward inhibition onto CA1 pyramidal cells by inhibiting fast-spiking interneurons that synapse on the same dendritic compartment as the SC, i.e., by disinhibiting the pyramidal cell dendritic compartment. Our work further describes the pairing of disinhibition with SC stimulation as a general mechanism for the induction of synaptic plasticity. We found that locally-reduced GABA release (disinhibition) paired with SC stimulation could lead to increased NMDAR activation and intracellular calcium concentration sufficient to upregulate AMPAR permeability and potentiate the excitatory synapse. Our work suggests that inhibitory synapses critically modulate excitatory neurotransmission and induction of plasticity at excitatory synapses. Our work also shows how cholinergic action on OLM interneurons, a mechanism whose disruption is associated with memory impairment, can down-regulate the GABAergic signaling into CA1 pyramidal cells and facilitate potentiation of the SC-CA1 synapse.

CA3-CA1 synapses in the hippocampus are the initial locus of episodic memory. The action of acetylcholine alters cellular excitability, modifies neuronal networks, and triggers secondary signaling that directly affects long-term plasticity (LTP) (the cellular underpinning of memory). It is therefore considered a critical regulator of learning and memory in the brain. Its action via M4 metabotropic receptors in the presynaptic terminal of the CA3 neurons and M1 metabotropic receptors in the postsynaptic spines of CA1 neurons produce rich dynamics across multiple timescales. We developed a model to describe the activation of postsynaptic M1 receptors that leads to IP3 production from membrane PIP2 molecules. The binding of IP3 to IP3 receptors in the endoplasmic reticulum (ER) ultimately causes calcium release. This calcium release from the ER activates potassium channels like the calcium-activated SK channels and alters different aspects of synaptic signaling. In an independent signaling cascade, M1 receptors also directly suppress SK channels and the voltage-activated KCNQ2/3 channels, enhancing post-synaptic excitability. In the CA3 presynaptic terminal, we model the reduction of the voltage sensitivity of voltage-gated calcium channels (VGCCs) and the resulting suppression of neurotransmitter release by the action of the M4 receptors. Our results show that the reduced initial release probability because of acetylcholine alters short-term plasticity (STP) dynamics. We characterize the dichotomy of suppressing neurotransmitter release from CA3 neurons and the enhanced excitability of the postsynaptic CA1 spine. Mechanisms underlying STP operate over a few seconds, while those responsible for LTP last for hours, and both forms of plasticity have been linked with very distinct functions in the brain. We show that the concurrent suppression of neurotransmitter release and increased sensitivity conserves neurotransmitter vesicles and enhances the reliability in plasticity. Our work establishes a relationship between STP and LTP coordinated by neuromodulation with acetylcholine.

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that depend upon evaluation of errors in performance and reward prediction errors, respectively. In mammals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction errors; but there has been little consideration of how direct policy learning might inform our understanding. I’ll discuss our recent work on classical conditioning in naïve mice (https://www.biorxiv.org/content/10.1101/2021.05.31.446464v1) that provides multiple lines of evidence that phasic dopamine signaling regulates policy learning from performance errors in addition to its well-known roles in value learning. This work points towards new opportunities for unraveling the mechanisms of basal ganglia control over behavior under both adaptive and maladaptive learning conditions.

Many studies have investigated the major glutamatergic inputs to the cerebellum, mossy fibres and climbing fibres, however far less is known about its neuromodulatory inputs. In particular, anatomical studies have described cholinergic input to the cerebellum, yet little is known about its role(s). In this talk, I will present our recent findings which demonstrate that manipulating acetylcholine receptors in the cerebellum causes effects at both a cellular and behavioural level. Activating acetylcholine receptors alters the intrinsic properties and synaptic inputs of cerebellar output neurons, and blocking these receptors results in deficits in a range of behavioural tasks.

Neuromodulators control information processing in cortical microcircuits by regulating the cellular and synaptic physiology of neurons. Computational models and detailed simulations of neocortical microcircuitry offer a unifying framework to analyze the role of neuromodulators on network activity. In the present study, to get a deeper insight in the organization of the cortical neuropil for modeling purposes, we quantify the fiber length per cortical volume and the density of varicosities for catecholaminergic, serotonergic and cholinergic systems using immunocytochemical staining and stereological techniques. The data obtained are integrated into a biologically detailed digital reconstruction of the rodent neocortex (Markram et al, 2015) in order to model the influence of modulatory systems on the activity of the somatosensory cortex neocortical column. Simulations of ascending modulation of network activity in our model predict the effects of increasing levels of neuromodulators on diverse neuron types and synapses and reveal a spectrum of activity states. Low levels of neuromodulation drive microcircuit activity into slow oscillations and network synchrony, whereas high neuromodulator concentrations govern fast oscillations and network asynchrony. The models and simulations thus provide a unifying in silico framework to study the role of neuromodulators in reconfiguring network activity.

The vagus nerve is a major pathway by which the brain and the body communicate. Electrical stimulation of the vagus nerve (VNS) is widely used as a therapeutic intervention for epilepsy and there is compelling evidence that it can enhance recovery following stroke. Our work demonstrates that VNS exerts a robust excitatory effect on the brain. First, we establish that VNS triggers an increase in arousal state as measured by behavioral state change. This behavioral state change is linked to an increase in excitatory activity within the cortex. We also show that cholinergic and noradrenergic neuromodulatory pathways are activated by VNS, providing a potential mechanism by which VNS may trigger cortical activation. Importantly, the effect of VNS on neuromodulation and cortical excitation persists in anesthetized mice, demonstrating that VNS-induced cortical activation cannot be fully explained by associated behavioral changes.

Animals produce behavior by responding to a mixture of cues that arise both externally (sensory) and internally (neural dynamics and states). These cues are continuously produced and can be combined in different ways depending on the needs of the animal. However, the integration of these external and internal cues remains difficult to understand in natural behaviors. To address this gap, we have developed an unsupervised method to identify internal states from behavioral data, and have applied it to the study of a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using cues from their partner. This sensory-driven behavior dynamically modulates courtship directed at their partner. We use our unsupervised method to identify how the animal integrates sensory information into distinct underlying states. We then use this to identify the role of courtship neurons in either integrating incoming information or directing the production of the song, roles that were previously hidden. Our results reveal how animals compose behavior from previously unidentified internal states, a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity, and motor outputs.

In addition to its role in the learning and expression of conditioned behavior, the amygdala has long been implicated in the regulation of persistent states, such as anxiety and drive. Yet, it is not evident what projections of the neuronal activity capture the functional role of the network across such different timescales, specifically when behavior and neuronal space are complex and high-dimensional. We applied a data-driven dynamical approach for the analysis of calcium imaging data from the basolateral amygdala, collected while mice performed complex, self-paced behaviors, including spatial exploration, free social interaction, and goal directed actions. The seemingly complex network dynamics was effectively described by a hierarchical, modular structure, that corresponded to behavior on multiple timescales. Our results describe the response of the network activity to perturbations along different dimensions and the interplay between slow, state-like representation and the fast processing of specific events and actions schemes. We suggest hierarchical dynamical models offer a unified framework to capture the involvement of the amygdala in transitions between persistent states underlying such different functions as sensory associative learning, action selection and emotional processing. * Work done in collaboration with Jan Gründemann, Sol Fustinana, Alejandro Tsai and Julien Courtin (@theLüthiLab)

Every autumn, monarch butterflies migrate from North America to their overwintering sites in Central Mexico. To maintain their southward direction, these butterflies rely on celestial cues as orientation references. The position of the sun combined with additional skylight cues are integrated in the central complex, a region in the butterfly’s brain that acts as an internal compass. However, the central complex does not solely guide the butterflies on their migration but also helps monarchs in their non-migratory form manoeuvre on foraging trips through their habitat. By comparing the activity of input neurons of the central complex between migratory and non-migratory butterflies, we investigated how a different lifestyle affects the coding of orientation information in the brain.

During natural behavior animals dynamically switch between different behaviors, yet little is known about how the brain performs behavioral-switches. Navigation is a complex dynamic behavior that enables testing these kind of behavioral switches: It requires the animal to know its own allocentric (world-centered) location within the environment, while also paying attention to incoming sudden events such as obstacles or other conspecifics – and therefore the animal may need to rapidly switch from representing its own allocentric position to egocentrically representing ‘things out-there’. Here we used an ethological task where two bats flew together in a very large environment (130 meters), and had to switch between two behaviors: (i) navigation, and (ii) obstacle-avoidance during ‘cross-over’ events with the other bat. Bats increased their echolocation click-rate before a cross-over, indicating spatial attention to the other bat. Hippocampal CA1 neurons represented the bat’s own position when flying alone (allocentric place-coding); surprisingly, when meeting the other bat, neurons switched very rapidly to jointly representing the inter-bat distance × position (egocentric × allocentric coding). This switching to a neuronal representation of the other bat was correlated on a trial-by-trial basis with the attention signal, as indexed by the bat’s echolocation calls – suggesting that sensory attention is controlling these major switches in neural coding. Interestingly, we found that in place-cells, the different place-fields of the same neuron could exhibit very different tuning to inter-bat distance – creating a non-separable coding of allocentric position × egocentric distance. Together, our results suggest that attentional switches during navigation – which in bats can be measured directly based on their echolocation signals – elicit rapid dynamics of hippocampal spatial coding. More broadly, this study demonstrates that during natural behavior, when animals often switch between different behaviors, neural circuits can rapidly and flexibly switch their core computations.

The main pillars of social behaviors involve (1) mating, where males copulate with female partners to reproduce, and (2) aggression, where males fight conspecific male competitors in territory guarding. Decades of study have identified two key regions in the hypothalamus, the medial preoptic nucleus (MPN) and the ventrolateral part of ventromedial hypothalamus (VMHvl) , that are essential for male sexual and aggressive behaviors, respectively. However, it remains ambiguous what area directs excitatory control of the hypothalamic activity and generates the initiation signal for social behaviors. Through neural tracing, in vivo optical recording and functional manipulations, we identified the estrogen receptor alpha (Esr1)-expressing cells in the posterior amygdala (PA) as a main source of excitatory inputs to the MPN and VMHvl, and key hubs in mating and fighting circuits in males. Importantly, two spatially-distinct populations in the PA regulate male sexual and aggressive behaviors, respectively. Moreover, these two subpopulations in the PA display differential molecular phenotypes, projection patterns and in vivo neural responses. Our work also observed the parallels between these social behavior circuits and basal ganglia circuits to control motivated behaviors, which Larry Swanson (2000) originally proposed based on extensive developmental and anatomical evidence.

During sexual behavior, copulation related sensory information and modulatory signals from the brain must be integrated and converted into the motor and secretory outputs that characterize ejaculation (Lenschow and Lima, Current Opinion in Neurobiology, 2020). Studies in humans and rats suggest the existence of interneurons in the lumbar spinal cord that mediates that step: the spinal ejaculation generator (SEG). My work aimed at gaining mechanistic insights about the neuronal circuits controlling ejaculation thereby applying cutting-edge techniques. More specifically, we mapped anatomically and functionally the spinal circuit for ejaculation starting from the main muscle being involved in sperm expulsion: the bulbospongiosus muscle (BSM). Combining viral tracing strategies with electrophysiology, we specifically show that the BSM motoneurons receive direct synaptic input from a group of interneurons located in between lumbar segment 2 and 3 and expressing the peptide galanin. Electrically and optogenetically activating the galanin positive cells (the SEG) lead to the activation of the motoneurons innervating the BSM and the muscle itself. Finally, inhibition of SEG cells using DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) in sexual behaving animals is currently conducted to reveal whether ejaculation can be prevented.

Many aspects of collective behavior depend on interactions between conspecifics. This is especially true for the collective motion of locusts, which swarm in millions while maintaining synchrony among individuals. However, whether locusts share and maintain the same socio-behavioral patterns – between groups, individuals and situations – remains an open question. Studying marching locusts under lab conditions, we found that (1) different groups behave differently; (2) locusts within a group homogenize their behavior; and (3) individuals have different socio-behavioral tendencies and context-dependent states. These variability levels suggest that behavioral differences within and among individuals exist, affect others, and shape the collective behavior of the entire group.

Colonies of ants and other eusocial insects are superorganisms, which perform sophisticated cognitive-like functions at the level of the group. In my talk I will review our efforts to establish the clonal raider ant Ooceraea biroi as a lab model system for the systematic study of the principles underlying collective information processing in ant colonies. I will use results from two separate projects to demonstrate the potential of this model system: In the first, we analyze the foraging behavior of the species, known as group raiding: a swift offensive response of a colony to the detection of a potential prey by a scout. By using automated behavioral tracking and detailed analysis we show that this behavior is closely related to the army ant mass raid, an iconic collective behavior in which hundreds of thousands of ants spontaneously leave the nest to go hunting, and that the evolutionary transition between the two can be explained by a change in colony size alone. In the second project, we study the emergence of a collective sensory response threshold in a colony. The sensory threshold is a fundamental computational primitive, observed across many biological systems. By carefully controlling the sensory environment and the social structure of the colonies we were able to show that it also appear in a collective context, and that it emerges out of a balance between excitatory and inhibitory interactions between ants. Furthermore, by using a mathematical model we predict that these two interactions can be mapped into known mechanisms of communication in ants. Finally, I will discuss the opportunities for understanding collective behavior that are opening up by the development of methods for neuroimaging and neurocontrol of our ants.

Neocortical-hippocampal interactions during off-line periods such as slow-wave sleep are implicated in memory processing. In particular, recent memory traces are replayed in hippocampus during some sharp-wave ripple (SWR) events, and these replay events are positively correlated with neocortical memory trace reactivation. A prevalent model is that SWR arise ‘spontaneously’ in CA3 and propagate recent memory ‘indices’ outward to the neocortex to enable memory consolidation there; however, the spatiotemporal distribution of neocortical activation relative to SWR is incompletely understood. We used wide-field optical imaging to study voltage and glutamate release transients in dorsal neocortex in relation to CA1 multiunit activity (MUA) and SWR of sleeping and urethane anesthetized mice. Modulation of voltage and glutamate release signals in relation to SWRs varied across superficial neocortical regions, and it was largest in posteromedial regions surrounding retrosplenial cortex (RSC), which receives strong hippocampal output connections. Activity tended to spread sequentially from more medial towards more lateral regions. Contrary to the unidirectional hypothesis, activation exhibited a continuum of timing relative to SWRs, varying from neocortex leading to neocortex lagging the SWRs (± ~250 msec). The timing continuum was correlated with the skewness of peri-SWR hippocampal MUA and with a tendency for some SWR to occur in clusters. Thus, contrary to the model in which SWRs arise spontaneously in hippocampus, neocortical activation often precedes SWRs and may thus constitute a trigger event in which neocortical information seeds associative reactivation of hippocampal ‘indices’.

The hippocampal formation has been implicated in both cognitive functions as well as the sensing and control of endocrine states. To identify a candidate activity pattern which may link such disparate functions, we simultaneously measured electrophysiological activity from the hippocampus and interstitial glucose concentrations in the body of freely behaving rats. We found that clusters of sharp wave-ripples (SPW-Rs) recorded from both dorsal and ventral hippocampus reliably predicted a decrease in peripheral glucose concentrations within ~10 minutes. This correlation was less dependent on circadian, ultradian, and meal-triggered fluctuations, it could be mimicked with optogenetically induced ripples, and was attenuated by pharmacogenetically suppressing activity of the lateral septum, the major conduit between the hippocampus and subcortical structures. Our findings demonstrate that a novel function of the SPW-R is to modulate peripheral glucose homeostasis and offer a mechanism for the link between sleep disruption and blood glucose dysregulation seen in type 2 diabetes and obesity.

How is it that some memories fade in a day while others last forever? The formation of long-lasting (remote) memories depends on the coordinated activity between the hippocampus and frontal cortices, but the timeline of these interactions is debated. Astrocytes, star-shaped glial cells, sense and modify neuronal activity, but their role in remote memory is scarcely explored. We manipulated the activity of hippocampal astrocytes during memory acquisition and discovered it impaired remote, but not recent, memory retrieval. We also revealed a massive recruitment of cortical-projecting hippocampal neurons during memory acquisition, a process that is specifically inhibited by astrocytic manipulation. Finally, we directly inhibited this projection during memory acquisition to prove its necessity for the formation of remote memory. Our findings reveal that the foundation of remote memory can be established during acquisition with projection-specific effect of astrocytes.

Empathy plays a critical role in social interactions, and many species, including rodents, display evolutionarily conserved behavioral antecedents of empathy. In both humans and rodents, the anterior cingulate cortex (ACC) encodes information about the affective state of others. However, little is known about which downstream targets of the ACC contribute to empathy behaviors. We optimized a protocol for the social transfer of pain behavior in mice and compared the ACC-dependent neural circuitry responsible for this behavior with the neural circuitry required for the social transfer of two related states: analgesia and fear. We found that a 1-hour social interaction between a bystander mouse and a cagemate experiencing inflammatory pain led to congruent mechanical hyperalgesia in the bystander. This social transfer led to activation of neurons in the ACC and several downstream targets, including the nucleus accumbens (NAc), which was revealed by monosynaptic rabies virus tracing to be directly connected to the ACC. Bidirectional manipulation of activity in ACC-to-NAc inputs influenced the acquisition of socially transferred pain. Further, the social transfer of analgesia also depended upon ACC-NAc inputs. By contrast, the social transfer of fear instead required activity in ACC projections to the basolateral amygdala. This shows that mice rapidly adopt the sensory-affective state of a social partner, regardless of the valance of the information (pain, fear, or pain relief). We find that the ACC generates specific and appropriate empathic behavioral responses through distinct downstream targets. More sophisticated understanding of evolutionarily conserved brain mechanisms of empathy will also expedite the development of new therapies for the empathy-related deficits associated with a broad range of neuropsychiatric disorders.

Technological advances have increased the availability of recordings from large populations of neurons across multiple brain areas. Coupling these recordings with dimensionality reduction techniques, recent work has led to new proposals for how populations of neurons can send and receive signals selectively and flexibly. Advancement of these proposals depends, however, on untangling the bidirectional, parallel communication between neuronal populations. Because our current data analytic tools struggle to achieve this task, we have recently validated and presented a novel dimensionality reduction framework: DLAG, or Delayed Latents Across Groups. DLAG decomposes the time-varying activity in each area into within- and across-area latent variables. Across-area variables can be decomposed further into feedforward and feedback components using automatically estimated time delays. In this talk, I will review the DLAG framework. Then I will discuss new insights into the moment-by-moment nature of feedforward and feedback communication between visual cortical areas V1 and V2 of macaque monkeys. Overall, this work lays the foundation for dissecting the dynamic flow of signals across populations of neurons, and how it might change across brain areas and behavioral contexts.

Behavior arises from the coordinated activity of numerous distinct brain regions. Modern experimental tools allow access to neural populations brain-wide, yet understanding such large-scale datasets necessitates scalable computational models to extract meaningful features of inter-region communication. In this talk, I will introduce Current-Based Decomposition (CURBD), an approach for inferring multi-region interactions using data-constrained recurrent neural network models. I will first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. I will then apply CURBD to understand the brain-wide flow of information leading to behavioral state transitions in larval zebrafish. These examples will establish CURBD as a flexible, scalable framework to infer brain-wide interactions that are inaccessible from experimental measurements alone.