Topic: navigation

Postdoctoral research with Christian Doeller at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. The group studies structured representations supporting navigation, episodic and working memory, learning and decision-making. Projects connect hippocampal population coding with knowledge representations and possible translation to Alzheimer’s disease. Approaches include high-field fMRI, MEG, EEG, virtual reality, psychophysics and computational models, including machine learning and deep neural networks. The project can be shaped around interdisciplinary intere

Chris Smith investigates autonomous transport through a ride on a self-driving bus in Cambridge. In collaboration with the Royal Institute of Navigation, the programme discusses the engineering, operation and navigation of driverless vehicles. Contributors include Nick Bailey, Daniel Clarke, Jamie Wilson, Jim Hutchinson, Ramsey Faragher and Edward Cameron, representing the test service, local transport partnership, vehicle manufacturer, automation supplier and navigation community.

Daniel Levenstein discusses spontaneous activity in the hippocampus and cortex, especially during sleep, and its relationship to learning, memory and navigation. He explains how AI models can help investigate these dynamics, alongside questions about model systems, cognitive maps and the relationship between neuroscience experiments and theory.

Many animals rely on persistent internal representations of continuous angular variables for working memory, motor control, and navigation. Theories have proposed that such representations are maintained by a class of recurrently connected networks called ring attractor networks. These networks rely on large numbers of neurons to maintain continuous and stable representations and to accurately integrate incoming signals. The head direction system of the fruit fly, however, seems to achieve these properties with a remarkably small network. These findings challenge our understanding of ring attr

The hippocampus is thought to build a cognitive map that supports navigation, memory, and planning, but what defines such a map and how it is used remain debated. In this talk, I will present computational models in which hippocampal-like representations emerge in recurrent neural networks trained to predict sequences of sensory observations. While spatially tuned units reliably arise, they are not sufficient to form a cognitive map. Instead, map-like representations emerge when recurrent dynamics support multi-step prediction, yielding a population-level encoding of environmental geometry. On

In recent years, my lab and others have demonstrated the value of vector symbolic algebras (VSAs) for capturing a wide variety of neural and behavioural results. In this talk I discuss the surprising and compelling variety of tasks and styles of reasoning that are well-suited to descriptions using a specific VSA. 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

Navigation requires orienting oneself relative to landmarks in the environment, evaluating relevant sensory data, remembering goals, and convert all this information into motor commands that direct locomotion. I will present models, highly constrained by connectomic, physiological and behavioral data, for how these functions are accomplished in the fly brain.

Attractors play a key role in a wide range of processes including learning, memory, decision making and navigation. Due to recent innovations in recording methods, there is increasing evidence for the existence of attractor dynamics in the brain. Yet, our understanding of how these attractors emerge or disappear in a biological system is lacking. In vitro cultured cortical neurons have been used extensively as a realistic experimental tool to understand the underlying mechanisms in neuronal assemblies. One of the main characteristics of the activity of such networks are the spontaneous synchro

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision tasks. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with diffe

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

Goal-directed navigation requires precise estimates of spatial relationships between current position and future goal, as well as planning of an associated route or action. While neurons in the hippocampal formation can represent the animal’s position and nearby trajectories, their role in determining the animal’s destination or action has been questioned. We thus hypothesize that brain regions outside the hippocampal formation may play complementary roles in navigation, particularly for guiding goal-directed behaviours based on the brain’s internal cognitive map. In this seminar, I will first

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 w

Bats are extreme aviators and amazing navigators. Many bat species nightly commute dozens of kilometres in search of food, and some bat species annually migrate over thousands of kilometres. Studying bats in their natural environment has always been extremely challenging because of their small size (mostly <50 gr) and agile nature. We have recently developed novel miniature technology allowing us to GPS-tag small bats, thus opening a new window to document their behaviour in the wild. We have used this technology to track fruit-bats pups over 5 months from birth to adulthood. Following the bat

Each spring, billions of Bogong moths escape hot conditions in different regions of southeast Australia by migrating over 1000 km to a limited number of cool caves in the Australian Alps, historically used for aestivating over the summer. At the beginning of autumn the same individuals make a return migration to their breeding grounds to reproduce and die. To steer migration Bogong moths sense the Earth’s magnetic field and correlate its directional information with visual cues. In this presentation, we will show that a critically important visual cue is the distribution of starlight within th

Zebrafish and cichlids are popular models in visual neuroscience, due to their amenability to advanced research tools and their diverse set of visually guided behaviours. It is often asserted that animals’ neural systems are adapted to the statistical regularities in their natural environments, but relatively little is known about the visual spatiotemporal features in the underwater habitats that nurtured these fish. To address this gap, we have embarked on an examination of underwater habitats in northeastern India and Lake Tanganyika (Zambia), where zebrafish and cichlids are native. In this

How nervous systems control behaviour is the main question we seek to answer in neuroscience. Although visual systems have been a popular entry point into the brain, we don’t understand—in any deep sense—how visual perception guides navigation in flies (or any organism). I will present recent progress towards this goal from our lab. We are using anatomical insights from connectomics, genetic methods for labelling and manipulating identified cell types, neurophysiology, behaviour, and computational modeling to explain how the fly brain processes visual motion to regulate behaviour.

Animals often make use of information from multiple sensory systems, such as vision, proprioception, olfaction, and audition to guide their behavior. They must thus integrate these different sensory inputs or select one of them in order to effectively make decisions in the environment. How exactly the brain accomplishes this sensory integration and to what extent each signal contributes under what circumstances remains to be fully understood. We use a deep reinforcement learning model to study sensory integration in a navigation task in two different scenarios. In the first scenario, there is

Hippocampal place cell populations putatively encode allocentric spatial position [1-4]. In bats and rodents, place cells exhibit heterogeneously sized and overlapping receptive (or place) fields [5-9]. These heterogeneous place fields instantiate a multiscale spatial tuning landscape in which the larger place fields of some place cells encompass the relatively more numerous narrowly tuned place fields of other cells (Fig 1a). In this work, we investigate the effectiveness of such multiscale codes for encoding allocentric spatial position. We do so by deriving closed-form expressions for how w

Efficient behavior in our noisy and ambiguous world calls for the strategic use of the arising uncertainty by probabilistic inference [1]. Probabilistic inference in complex dynamic environments is challenging as it requires tracking not only individual latent variables but also the interactions among them. As a result, a neural circuit that optimally encodes these variables would need to be recurrently wired to relay information between them via feedback connections [2]. Given the additional cost of evolving and maintaining these feedback connections, we sought the circumstances under which t

The reciprocal connectivity of the nucleus reuniens (RE) of the ventral midline thalamus with the prefrontal cortex (PFC) and hippocampus (HC) is suggestive of the RE being a critical node within a large-scale brain network underlying spatial cognition. The RE activity has been implicated in navigation, decision making, and spatial memory consolidation. We have previously shown that the RE activity is critical for the retrieval of spatial memory. Yet, the network mechanisms of the RE contribution to the extended prefrontal-thalamo-hippocampal circuit remain to be explored. It has been suggeste