Seminars

In this webinar on spatial navigation circuits, three researchers—Ann Hermundstad, Ila Fiete, and Barbara Webb—discussed how diverse species solve navigation problems using specialized yet evolutionarily conserved brain structures. Hermundstad illustrated the fruit fly’s central complex, focusing on how hardwired circuit motifs (e.g., sinusoidal steering curves) enable rapid, flexible learning of goal-directed navigation. This framework combines internal heading representations with modifiable goal signals, leveraging activity-dependent plasticity to adapt to new environments. Fiete explored the mammalian head-direction system, demonstrating how population recordings reveal a one-dimensional ring attractor underlying continuous integration of angular velocity. She showed that key theoretical predictions—low-dimensional manifold structure, isometry, uniform stability—are experimentally validated, underscoring parallels to insect circuits. Finally, Webb described honeybee navigation, featuring path integration, vector memories, route optimization, and the famous waggle dance. She proposed that allocentric velocity signals and vector manipulation within the central complex can encode and transmit distances and directions, enabling both sophisticated foraging and inter-bee communication via dance-based cues.

Subsecond timing underlies nearly all sensory and motor activities across species and is critical to survival. While subsecond temporal information has been found across cortical and subcortical regions, it is unclear if it is generated locally and intrinsically or if it is a read out of a centralized clock-like mechanism. Indeed, mechanisms of subsecond timing at the circuit level are largely obscure. Primary sensory areas are well-suited to address these question as they have early access to sensory information and provide minimal processing to it: if temporal information is found in these regions, it is likely to be generated intrinsically and locally. We test this hypothesis by training mice to perform an audio-visual temporal pattern sensory discrimination task as we use 2-photon calcium imaging, a technique capable of recording population level activity at single cell resolution, to record activity in primary visual cortex (V1). We have found significant changes in network dynamics through mice’s learning of the task from naive to middle to expert levels. Changes in network dynamics and behavioral performance are well accounted for by an intrinsic model of timing in which the trajectory of q network through high dimensional state space represents temporal sensory information. Conversely, while we found evidence of other temporal encoding models, such as oscillatory activity, we did not find that they accounted for increased performance but were in fact correlated with the intrinsic model itself. These results provide insight into how subsecond temporal information is encoded mechanistically at the circuit level.

Higher cortical areas carry a wide range of sensory, cognitive, and motor signals supporting complex goal-directed behavior. These signals mix in heterogeneous responses of single neurons, making it difficult to untangle underlying mechanisms. I will present two approaches for revealing interpretable circuit mechanisms from heterogeneous neural responses during cognitive tasks. First, I will show a flexible nonparametric framework for simultaneously inferring population dynamics on single trials and tuning functions of individual neurons to the latent population state. When applied to recordings from the premotor cortex during decision-making, our approach revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Second, I will show an approach for inferring an interpretable network model of a cognitive task—the latent circuit—from neural response data. We developed a theory to causally validate latent circuit mechanisms via patterned perturbations of activity and connectivity in the high-dimensional network. This work opens new possibilities for deriving testable mechanistic hypotheses from complex neural response data.

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within A1 but controlled by top-down inputs from mPFC, can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

We recorded and analyzed the population activity of hundreds of neurons in the medial premotor areas (MPC) of rhesus monkeys performing an isochronous tapping task guided by brief flashing stimuli or auditory tones. The animals showed a strong bias towards visual metronomes, with rhythmic tapping that was more precise and accurate than for auditory metronomes. The population dynamics in state space as well as the corresponding neural sequences shared the following properties across modalities: the circular dynamics of the neural trajectories and the neural sequences formed a regenerating loop for every produced interval, producing a relative time representation; the trajectories converged in similar state space at tapping times while the moving bumps restart at this point, resetting the beat-based clock; the tempo of the synchronized tapping was encoded by a combination of amplitude modulation and temporal scaling in the neural trajectories. In addition, the modality induced a displacement in the neural trajectories in auditory and visual subspaces without greatly altering time keeping mechanism. These results suggest that the interaction between the amodal internal representation of pulse within MPC and a modality specific external input generates a neural rhythmic clock whose dynamics define the temporal execution of tapping using auditory and visual metronomes.

New tasks are often similar in structure to old ones. Animals that take advantage of such conserved or “abstract” task structures can master new tasks with minimal training. To understand the neural basis of this abstraction, we developed a novel behavioural paradigm for mice: the “ABCD” task, and recorded from their medial frontal neurons as they learned. Animals learned multiple tasks where they had to visit 4 rewarded locations on a spatial maze in sequence, which defined a sequence of four “task states” (ABCD). Tasks shared the same circular transition structure (… ABCDABCD …) but differed in the spatial arrangement of rewards. As well as improving across tasks, mice inferred that A followed D (i.e. completed the loop) on the very first trial of a new task. This “zero-shot inference” is only possible if animals had learned the abstract structure of the task. Across tasks, individual medial Frontal Cortex (mFC) neurons maintained their tuning to the phase of an animal’s trajectory between rewards but not their tuning to task states, even in the absence of spatial tuning. Intriguingly, groups of mFC neurons formed modules of coherently remapping neurons that maintained their tuning relationships across tasks. Such tuning relationships were expressed as replay/preplay during sleep, consistent with an internal organisation of activity into multiple, task-matched ring attractors. Remarkably, these modules were anchored to spatial locations: neurons were tuned to specific task space “distances” from a particular spatial location. These newly discovered “Spatially Anchored Task clocks” (SATs), suggest a novel algorithm for solving abstraction tasks. Using computational modelling, we show that SATs can perform zero-shot inference on new tasks in the absence of plasticity and guide optimal policy in the absence of continual planning. These findings provide novel insights into the Frontal mechanisms mediating abstraction and flexible behaviour.

It was long hypothesized that natural systems might take advantage of the extended temporal and spatial correlations close to the critical point to improve their computational capabilities. However, on the other side, different distances to criticality were inferred from the recordings of nervous systems. In my talk, I discuss how including additional constraints on the processing time can shift the optimal operating point of the recurrent networks. Moreover, the data from the visual cortex of the monkeys during the attentional task indicate that they flexibly change the closeness to the critical point of the local activity. Overall it suggests that, as we would expect from common sense, the optimal state depends on the task at hand, and the brain adapts to it in a local and fast manner.

Our group focuses on brain computation, physiology and evolution, with a particular focus on network dynamics, sleep (evolution and mechanistic underpinnings), cortical computation (through the study of ancestral cortices), and sensorimotor processing. This talk will describe our recent results on the remarkable camouflage behavior of cuttlefish (action) and on brain activity in REM and NonREM in lizards (sleep). Both topics will focus on aspects of circuit dynamics.

Our group focuses on brain computation, physiology and evolution, with a particular focus on network dynamics, sleep (evolution and mechanistic underpinnings), cortical computation (through the study of ancestral cortices), and sensorimotor processing. This talk will describe our recent results on the remarkable camouflage behavior of cuttlefish (action) and on brain activity in REM and NonREM in lizards (sleep). Both topics will focus on aspects of circuit dynamics.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. To address these questions, we used a combination of extracellular recordings, neuronal decoding approaches, and optogenetic manipulations to show that threat representations and the initiation of avoidance behaviour are dynamically encoded in the overall population activity of dmPFC neurons. These data indicate that although dmPFC population activity at stimulus onset encodes sustained threat representations and discriminates threat- from non-threat cues, it does not predict action outcome. In contrast, transient dmPFC population activity prior to action initiation reliably predicts avoided from non-avoided trials. Accordingly, optogenetic inhibition of prefrontal activity critically constrained the selection of adaptive defensive responses in a time-dependent manner. These results reveal that the adaptive selection of active fear responses relies on a dynamic process of information linking threats with defensive actions unfolding within prefrontal networks.

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)

This talk will focus on different aspects of the Local Field Potential (LFP) signal. Classically, LFP fluctuations are related to changes in the functional state of the cortex. Yet, the mechanisms linking LFP changes with the state of the cortex are not well understood. The presentation will start with a brief explanation of the main oscillatory components of the LFP signal, how these different oscillatory components are generated at cortical microcircuits, and how their dynamics can be studied across multiple areas. Thereafter, a case study of a patient with akinetic mutism will be presented, linking cortical states with the behavior of the patient, as well as some preliminary results about how the LF cortical microcircuit dynamic changes modulate different cortical states and how these changes are reflected in the LFP signal

Regulation of information flow in the brain is critical for many forms of behavior. In the process of sensory based decision-making, decisions about future actions are held in memory until enacted, making them potentially vulnerable to distracting sensory input. Therefore, gating of information flow from sensory to motor areas could protect memory from interference during decision-making, but the underlying network mechanisms are not understood. I will present our recent experimental and modeling work describing how information flow from the sensory cortex can be gated by state-dependent frontal cortex dynamics during decision-making in mice. Our results show that communication between brain regions can be regulated via attractor dynamics, which control the degree of commitment to an action, and reveal a novel mechanism of gating of neural information.

Birdsong emerges when a set of highly interconnected brain areas manage to generate a complex output. The similarities between birdsong production and human speech have positioned songbirds as unique animal models for studying learning and production of this complex motor skill. In this work, we developed a low dimensional model for a neural network in which the variables were the average activities of different neural populations within the nuclei of the song system. This neural network is active during production, perception and learning of birdsong. We performed electrophysiological experiments to record neural activity from one of these nuclei and found that the low dimensional model could reproduce the neural dynamics observed during the experiments. Also, this model could reproduce the respiratory motor patterns used to generate song. We showed that sparse activity in one of the neural nuclei could drive a more complex activity downstream in the neural network. This interdisciplinary work shows how low dimensional neural models can be a valuable tool for studying the emergence of complex motor tasks