Topic: spatial representations

Investigate how eye movements shape perception with Martina Poletti and Michele Rucci at the University of Rochester. Research spans spatial representations, foveal and peripheral processing, attention, retinal anatomy, emmetropization and myopia. The laboratory combines human psychophysics, controlled retinal stimulation, computational modelling, precise eye and head tracking, retinal imaging and EEG during active vision. Researchers work within the Center for Visual Science and Brain and Cognitive Sciences, with collaborations in optics, neuroscience and ophthalmology. Applications comprise

Humans share with other animals a complex neuronal machinery that evolved to support navigation in the physical 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

The hippocampus is a key player in learning and memory. Research into this brain structure has long emphasized its plasticity and flexibility, though recent reports have come to appreciate its remarkably stable firing patterns. How novel information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional featu

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

A complete neuroscience requires multi-level theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. Unfortunately, we don't have cognitive models of behavior whose components can be decomposed into the neural dynamics that give rise to behavior, leaving an explanatory gap. Here, we decompose SUSTAIN, a clustering model of concept learning, into neuron-like units (SUSTAIN-d; decomposed). Instead of abstract constructs (clusters), SUSTAIN-d has a pool of neuron-like units. With millions of units, a key challenge is how to bridge from abstract c

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

Research on analogical processing and reasoning has provided strong evidence that the use of adequate educational analogies has strong and positive effects on the learning of mathematics. In this talk I will show some experimental results suggesting that analogies based on spatial representations might be particularly effective to improve mathematics learning. Since fostering mathematics learning also involves addressing psychosocial factors such as the development of mathematical anxiety, providing social incentives to learn, and fostering engagement and motivation, I will argue that one area

There has been a long-lasting debate on whether the function of the hippocampal formation is to store and retrieve episodic memory, supported by experiments in humans, or to code for space, suggested by spatially tuned cells (place cells, grid cells, etc.) recorded in nonhuman animals. In an attempt to reconcile the two views, we use a memory-augmented neural network in a reinforcement learning setup to solve a navigation task in a simulated maze. The key feature of the model is that it learns autonomously what and when to store into, and retrieve from, an external memory buffer. Once the mode

Throughout each day the brain captures snapshots of distinct experiences, forming episodic memories that often last a lifetime. This function depends on the hippocampus – a brain region that is evolutionarily conserved across vertebrates. My lab studies the relationship between hippocampal activity and episodic memory using a unique model organism – the black-capped chickadee. Chickadees are specialist food-caching birds that store thousands of food items at concealed locations in their environment and use memory later in time to retrieve their caches. I will describe our effort in designing b

Our understanding of the hippocampus has been framed by two landmark discoveries: the discovery by Scoville and Milner that hippocampal damage causes profound and persistent amnesia and the discovery by O’Keefe and Dostrovsky of hippocampal place cells in rodents. However, it has been unclear to what extent spatial representations are present in the primate brain and how to reconcile these representations with the known mnemonic function of this region. I will discuss a series of experiments that have examined neural activity in the hippocampus in monkeys performing behavioral tasks including

Recent research involving bats navigating lengthy tunnels has confirmed that hippocampal place cells can be active at multiple locations, with considerable variability in place field size and peak rate. In a recurrent networks, such variability implies quenched disorder in the synaptic weights, impeding the establishment of a continuous manifold of fixed points. If most positions in space are not any longer fixed points, is the popular notion of continuous attractors still relevant? What would be the neural dynamics underlying retrieval of spatial representations?We study the dynamics of recur