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.

To localize ourselves in an environment for spatial navigation, we rely on vision and self-motion inputs, which only provide noisy and partial information. It is unknown how the resulting uncertainty affects navigation behavior and neural representations. Here we show that spatial uncertainty underlies key effects of environmental geometry on navigation behavior and grid field deformations. We develop an ideal observer model, which continually updates probabilistic beliefs about its allocentric location by optimally combining noisy egocentric visual and self-motion inputs via Bayesian filtering. This model directly yields predictions for navigation behavior and also predicts neural responses under population coding of location uncertainty. We simulate this model numerically under manipulations of a major source of uncertainty, environmental geometry, and support our simulations by analytic derivations for its most salient qualitative features. We show that our model correctly predicts a wide range of experimentally observed effects of the environmental geometry and its change on homing response distribution and grid field deformation. Thus, our model provides a unifying, normative account for the dependence of homing behavior and grid fields on environmental geometry, and identifies the unavoidable uncertainty in navigation as a key factor underlying these diverse phenomena.

Mammals instinctively explore and form mental maps of their spatial environments. Models of cognitive mapping in neuroscience mostly depict map-learning as a process of random or biased diffusion. In practice, however, animals explore spaces using structured, purposeful, sensory-guided actions. We have used threat-evoked escape behavior in mice to probe the relationship between ethological exploratory behavior and abstract spatial cognition. First, we show that in arenas with obstacles and a shelter, mice spontaneously learn efficient multi-step escape routes by memorizing allocentric subgoal locations. Using closed-loop neural manipulations to interrupt running movements during exploration, we next found that blocking runs targeting an obstacle edge abolished subgoal learning. We conclude that mice use an action-driven learning process to identify subgoals, and these subgoals are then integrated into an allocentric map-like representation. We suggest a conceptual framework for spatial learning that is compatible with the successor representation from reinforcement learning and sensorimotor enactivism from cognitive science.

Spike-driven adaptation involves intracellular mechanisms that are initiated by neural firing and lead to the subsequent reduction of spiking rate followed by a recovery back to baseline. We report on long (>0.5 second) recovery times from adaptation in a thalamic-like structure in weakly electric fish. This adaptation process is shown via modeling and experiment to encode in a spatially invariant manner the time intervals between event encounters, e.g. with landmarks as the animal learns the location of food. These cells also come in two varieties, ones that care only about the time since the last encounter, and others that care about the history of encounters. We discuss how the two populations can share in the task of representing sequences of events, supporting path integration and converting from ego-to-allocentric representations. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus. Finally we discuss how all the cells of this gateway to the pallium exhibit mixed selectivity of social features of their environment. The data and computational modeling further reveal that, in contrast to a long-held belief, these gymnotiform fish are endowed with a corollary discharge, albeit only for social signalling.

Spike-driven adaptation involves intracellular mechanisms that are initiated by spiking and lead to the subsequent reduction of spiking rate. One of its consequences is the temporal patterning of spike trains, as it imparts serial correlations between interspike intervals in baseline activity. Surprisingly the hidden adaptation states that lead to these correlations themselves exhibit quasi-independence. This talk will first discuss recent findings about the role of such adaptation in suppressing noise and extending sensory detection to weak stimuli that leave the firing rate unchanged. Further, a matching of the post-synaptic responses to the pre-synaptic adaptation time scale enables a recovery of the quasi-independence property, and can explain observations of correlations between post-synaptic EPSPs and behavioural detection thresholds. We then consider the involvement of spike-driven adaptation in the representation of intervals between sensory events. We discuss the possible link of this time-stamping mechanism to the conversion of egocentric to allocentric coordinates. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus.

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.

For spatial orientation, heading information can be processed in two different frames of reference, a self-centered egocentric or a viewpoint allocentric frame of reference. Using the most efficient frame of reference is in particular important if an animal migrates over large distances, as it the case for the monarch butterfly (Danaus plexippus). These butterflies employ a sun compass to travel over more than 4,000 kilometers to their destination in central Mexico. We developed tetrode recordings from the heading-direction network of tethered flying monarch butterflies that were allowed to orient with respect to a sun stimulus. We show that the neurons switch their frame of reference depending on the animal’s locomotion state. In quiescence, the heading-direction cells encode a sun bearing in an egocentric reference frame, while during active flight, the heading-direction is encoded within an allocentric reference frame. By switching to an allocentric frame of reference during flight, monarch butterflies convert the sun to a global compass cue for long-distance navigation, an ideal strategy for maintaining a migratory heading.