Topology ePosters

The brain navigates mazes using the entorhinal grid and hippocampal place codes. While specific grid codes can represent maze structures independent of behavioral policy, little is known about how these task structure-specific grid codes generalize to novel tasks requiring different structures and policies.…

In neuroscience, we record neural activity over time. We can try to make sense of this data by studying the (low-dimensional) space the activity is constrained to. To identify these spaces, one typically either treats the rows (neurons / spike trains) or the columns (timepoints / population vectors) of the activity…

Relational invariance---a network’s ability to capture the geometric relationships between input patterns abstracted away from explicit external variables---has the potential to enable robustness and generalizability in neural networks. Previous work from our group has shown that a combinatorial neuronal code…

Navigation is inherently dynamic. It involves learning the environment, as well as positions in and trajectories through it, and then executing a path to reach a target. Spatial navigation skills vary significantly among individuals. But what differentiates a good navigator from a bad one, or an easy-to-navigate path…

Animals learn complex cognitive maps to navigate through physical environments or reason about abstract concepts. These maps are believed to be encoded by mammalian hippocampal grid and place cells, forming low-dimensional neural manifolds that support flexible navigation. However, beyond simple line and ring…

Continuous attractors offer a unique class of dynamical systems solutions for storing continuous-valued variables in recurrent neural states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general---they are destroyed by most infinitesimal changes…

Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for arbitrarily long time intervals. Unfortunately, because continuous attractors are not structurally stable, they suffer from severe structural instability in general---they are destroyed by…

How does intrinsic network structure shape spontaneous coordinated neural dynamics? Previous work has shown that overlapping tectal neuronal assemblies can be circuit mechanisms for robust visual detection [1]. To better understand how these assemblies give rise to emergent dynamics, we use single-photon calcium…

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…

Hopfield networks are artificial neural networks which store memory patterns on the states of their neurons by choosing recurrent connection weights and update rules such that the energy landscape of the network forms attractors around the memories. How many stable, sufficiently-attracting memory patterns can we store…

The shapes of neuronal morphologies are essential for the dynamical properties of the brain, as their branching patterns govern different functional properties of cell types. However, disagreements on the definition of neuronal classes due to the subjective views of the experts have spawned several efforts to find an…

The peripheral olfactory system analyses odorants through a large number of genetically defined information channels in the antennal lobe (AL), each associated to an olfactory receptor (OR) type. In Drosophila, these channels are combined in two major downstream areas - the mushroom body (MB) and lateral horn (LH).…