Path integration

Foraging, the set of behaviors associated with seeking valuable resources (e.g., food, water) while avoiding danger (e.g., predators), is ubiquitous among organisms, but its neural circuit basis is presently unclear. Most existing work on navigation, a key component of foraging, involves animals in small, fully observable arenas with few-to-no obstacles, partly because recording neural activity in naturalistic settings is challenging. How do animals successfully forage in complex naturalistic environments, especially given realistic neural circuit constraints on capacity and connectivity? To s

…hexagonal firing pattern and enable path integration. But how does predictive coding arise

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 t

…These tasks include path integration, navigation, Bayesian reasoning, sampling, memorization, and logical inference. The resulting spiking neural network models capture various hippocampal cell types (grid, place, border, etc.), behavioural errors, and a variety of observed neural dyn

Central place foraging insects like the honeybee (Apis mellifera) are the masters of visual navigation of the insect world: They are able to reliably return to their nest under a wide range of visual conditions after only a few learning flights, (a feat so far unrivalled by any artificial system). Furthermore, they perform complex navigational tasks, such as ‘computing’ novel shortcuts between previously known but unrelated food sources, supporting claims about the existence of a ‘cognitive map’ in honeybees. This raises the question which internal representations of the world (and the animal’

What circuit mechanisms underlie the encoding and retrieval of spatial memory? In standard models, neurons have discrete fields that map onto locations, which are associated with actions [1]. Given that spatial-memory regions (such as hippocampus and entorhinal cortex) have recurrent architectures, it is unclear if this feedforward association mechanism is the only way in which spatial memories can be stored. Here, we propose a new mechanism for spatial memory that is derived from reverse-engineering recurrent neural networks (RNNs). We train RNNs on a spatial navigation task posed as a rein

Noisy and ambiguous environments require using the arising uncertainty strategically for efficient behavior. Recent behavioral evidence confirms such strategic use during spatial navigation. Despite this, leading neural models for navigation ignore such uncertainty. I will focus on two examples for revising these models. First, I will show that common ring attractor architectures are able to track uncertainty in heading direction only if they are tuned to feature slow dynamics. Second, I will show that approximate computations that only partially track uncertainty are sufficient to perform cl

…(MEC) encoding self-motion cues (Path Integration) analogous to the 'where' pathway, while the Lateral Entorhinal Cortex (LEC) integrates sensory inputs analogous to the 'what' pathway. Object-dependent spatial cells, including Object Vector cells, Object Sensitive

…in general or have a dedicated role in path integration. Here, we aim to distinguish these possibilities by investigating how grid and non-grid cell firing varies as a function of performance in a virtual reality location memory task that can be solved either with a cued-based strategy or exclusively by path integration. We take advantage of spontaneous variation within and between recording sessions, which gives a complementary

…One such navigation mechanism is path integration, whereby animals recall their starting point by continuously measuring distances and directions of their movements, summing these to form a single memory-stored home vector. While path integration is a primary navigation mechanism for many invertebrates, little is known about the temporal degradation of this vector memory. To investigate how long the home v

…space and that supports wayfinding and path integration. In my talk I will present a series of recent neuroimaging studies in humans performed in my Lab aimed at investigating the idea that this same neural navigation system (the “brain GPS”) is also used to organize and navigate concepts and memories, and that abstract and spatial representations rely on a common neural fabric. I will argue that this might represent a novel example of “cortical recycling”, where the neuronal machine

…behaviour is centered, and can use path integration to maintain an accurate estimate of their distance and direction (a vector) to their nest. Some species, such as bees and ants, can also store the vector information for multiple salient locations in the world, such as food sources, in a common coordinate system. They can also use remembered views of the terrain around salient locations or along travelled routes to guide return. Recent modelling of these abilities shows convergence on a small set of algorithms and assumptions tha

…navigation in terms of perceptual cue integration but omit planning and sequential control and, therefore, cannot explain how trajectories and their endpoint variability arise. Because a vast array of natural sensorimotor behaviors in humans, includ

Insects demonstrate a remarkable ability to navigate the world -- foraging for food and returning to their nests over large distances. Simple neural circuits have been shown to underlie insect navigation behaviours, encoding the animal's head direction and velocity vector as a sinusoidal activity pattern\cite{heinze2007maplike,lyuBuildingAllocentricTravelling2022}. We offer novel mathematical insights into the benefits of this sinusoidal encoding, suggesting that it might offer an evolutionary advantage in terms of noise resilience. Previous work has recognised that encoding vectors as sinusoi

Low dimensional attractor dynamics have now been established as a core mechanism by which neural populations represent, remember, and compute with continuous-valued external variables. Arriving at this state in the field has relied on the intuition-guided construction of neural circuit models with continuous attractor dynamics that led to population-level predictions about dynamics, followed by validation from large-scale recordings. Recent deep learning approaches require less intuition but extensive training to generate networks with continuous attractor and integrator dynamics, however extr

Path integration is an internal computation of position by integrating velocities over time. In the absence of external landmarks, path integration accumulates error, causing the internal representation of position to drift relative to the external world [1,2]. In addition to the well-established role of landmarks in correcting this error, recent experiments identified a new role of landmarks: recalibrating the path-integration gain, a factor mapping the animal’s movement through space to an updating of position in its internal representation [3]. Here, we set out to understand the mechanisms

A cognitive compass is a critical component of spatial navigation and path integration. Experiments in the fruit fly and recent theoretical work show that an accurate compass can be built from a handful of neurons. Many previous models have used ring attractors to model head direction systems. However, these models relied on hand-designed neural network connectivity patterns, and we lack a principled methodology for quantitatively relating connectomics data to theoretical ring attractor models. Here we introduce a novel theoretical framework for analyzing the space of threshold-linear ring a

Cognitive maps are hypothesized memory systems that organize sensory experience into knowledge that can be used flexibly in novel scenarios, such as finding new routes during navigation. Evidence for this hypothesis has been largely limited to experiments with fully observable sensory information. However, to serve as a genuine internal source of knowledge, cognitive maps should also be able to support mental computations in the absence of external cues. To test this hypothesis, we developed a mental navigation task in which monkeys must produce vectors that correspond to moving between pairs

During navigation, animals estimate their position using path integration and landmarks. In a series of two studies, we used virtual reality and electrophysiology to dissect how these inputs combine to generate the brain’s spatial representations. In the first study (Campbell et al., 2018), we focused on the medial entorhinal cortex (MEC) and its set of navigationally-relevant cell types, including grid cells, border cells, and speed cells. We discovered that attractor dynamics could explain an array of initially puzzling MEC responses to virtual reality manipulations. This theoretical framewo

Several species of insects have been observed to perform accurate path integration, constantly updating a vector memory of their location relative to a starting position, which they can use to take a direct return path. Foraging insects such as bees and ants are also able to store and recall the vectors to return to food locations, and to take novel shortcuts between these locations. Other insects, such as dung beetles, are observed to integrate multimodal directional cues in a manner well described by vector addition. All these processes appear to be functions of the Central Complex, a highly

…velocity signals, a phenomenon called path integration (PI). Ring attractor models for angular PI have received strong experimental support. To function as integrators, HD circuits require precisely tuned connectivity, which is costly to pass down genetically. This suggests that synaptic plasticity is crucial in setting up these circuits. We propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic c

…updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells that maintain periodic representations of position and orientation, respectively. However, these representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We construct 1D ring a

Neural circuit computations are modulated by combinations of internal and external factors. For example, neurons in the medial entorhinal cortex (MEC) change their firing patterns or “remap” during a reward-seeking task, responding to changing task conditions and environmental cues. Why does this remapping occur? What unifying principles (if any) underlie remapping? Recent work proposes that remapping reflects hidden state inference—changes to an animal’s internal beliefs about the world. Recent experimental evidence suggests that remapping can indeed arise from latent behavioral or cognitive

During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains incompletely understood. We combine electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three navigation-relevant areas: primary visual cortex (V1), retrosplenial cortex (RSC), and medial entorhinal cortex (MEC). Compared with V1 and RSC, path integration influences position estimates more in MEC, and conflicts between path integration and landmarks