Biology subjects

Giocomo, L.

Publications and source records attributed to Giocomo, L..

6 recordsLinked to original sources

bioRxiv2026

Structured navigation in a goal-directed task reveals flexible spatial coding

Animal survival depends on accurate navigation. The last several decades have revealed that neurons encoding position and orientation can support navigation through the construction of an internal map of space for the external world. However, current experimental approaches for studying this system require tradeoffs between behavioral richness and experimental control; open field tasks often lack structured trials or trajectories and virtual reality paradigms constrain natural movement variables. Here, we introduce a platform that combines freely moving behavior with control over environmental stimuli, trajectories, and reward locations. This platform is compatible with chronic Neuropixels recordings, dynamic optical projection of visual stimuli and real-time pose estimation. Using this platform, we show rats can learn a multistage spatial targeting task and that high density Neuropixels recordings can be performed in conjunction. Recordings in medial entorhinal cortex revealed canonical spatial and head direction tuning, along with population-level activity that tracked distance to task-relevant goals. Together, this platform provides a powerful approach for examining the neural basis of flexible navigation under experimentally controlled yet naturalistic conditions.

neuroscience↗

bioRxiv2026

Gaussian Process Inference Reveals Non-separability of Positionand Velocity Tuning in Grid Cells

Grid cells in medial entorhinal cortex (MEC) support spatial navigation by responding to multiple variables, including position, speed, and head direction. While tuning curves for each of these variables have been examined individually at the level of single-cells, less is known about the conjunctive coding of grid cells for these properties. To investigate this, we analyzed neural recordings of freely foraging rats and constructed four-dimensional (4D) tuning curves across 2D position and 2D velocity. In order to combat the sparse sampling of such a large behavioral space, we applied Gaussian Process (GP) methods to estimate firing rates at un-sampled points. Comparing GP model-derived tuning curves to those predicted by a fully separable model revealed that some cells exhibited significant non-separability of position and velocity tuning, and suggested a data coverage threshold necessary to observe this non-separability. In summary, our use of GPs allowed us to distinguish interactions in position-velocity tuning across a 4D behavioral space that have not been apparent in 2D analyses.

neuroscience↗

bioRxiv2023

One-shot entorhinal maps enable flexible navigation in novel environments

Animals face the substantial challenge of navigating novel environments to find food, shelter, or mates. In mammals, hexagonal grid cells in the medial entorhinal cortex create a map-like population representation of the external environment 1-7. However, it remains unclear how the grid map can adapt to novel environmental features on a rapid, behaviorally relevant time scale. By recording over fifteen thousand grid cells in mice navigating virtual environments, we found grid cell activity was weakly anchored to landmark inputs through a fixed circuit relationship. A computational model based on this fixed circuit assumption accurately predicted grid spatial patterns in environments with novel landmark rearrangements. Finally, a medial entorhinal cortex-dependent task revealed that while grid cell firing patterns remain anchored to landmarks, behavior can adapt to changes in landmark location via a downstream region implementing behavioral time scale synaptic plasticity 8. This fixed but weak anchoring of grid cells to landmarks endows the grid map with powerful computational properties. The fixed nature allows the generation of rapid stable maps for novel environments after a single exposure. The weak nature allows these rapidly formed maps to incur only small distortions between distances traveled in real versus neural space. Overall, such rapid low distortion mapping can then mediate accurate navigational behavior in rapidly changing environments through downstream plasticity.

neuroscience↗

bioRxiv2022

When and why grid cells appear or not in trained path integrators

Recent work has claimed that the emergence of grid cells from trained path-integrator circuits is a more fragile phenomenon than previously reported. In this note we critically assess the main analysis and simulation results underlying this claim, within the proper context of previously published theoretical work. Our assessment reveals that the emergence of grid cells is entirely consistent with this prior theory: hexagonal grid cells robustly emerge precisely when prior theory predicts they should, and dont when prior theory predicts they should not.

neuroscience↗

bioRxiv2020

A unified theory for the computational and mechanistic origins of grid cells

The discovery of entorhinal grid cells has generated considerable interest in how and why hexagonal firing fields might mechanistically emerge in a generic manner from neural circuits, and what their computational significance might be. Here we forge an intimate link between the computational problem of path-integration and the existence of hexagonal grids, by demonstrating that such grids arise generically in biologically plausible neural networks trained to path integrate. Moreover, we develop a unifying theory for why hexagonal grids are so ubiquitous in path-integrator circuits. Such trained networks also yield powerful mechanistic hypotheses, exhibiting realistic levels of biological variability not captured by hand-designed models. We furthermore develop methods to analyze the connectome and activity maps of our trained networks to elucidate fundamental mechanisms underlying path integration. These methods provide an instructive roadmap to go from connectomic and physiological measurements to conceptual understanding in a manner that might be generalizable to other settings.

neuroscience↗

bioRxiv2018

NeuroRoots, a bio-inspired, seamless Brain Machine Interface device for long-term recording.

Minimally invasive electrodes of cellular scale that approach a bio-integrative level of neural recording could enable the development of scalable brain machine interfaces that stably interface with the same neural populations over long period of time.\n\nIn this paper, we designed and created NeuroRoots, a bio-mimetic multi-channel implant sharing similar dimension (10{micro}m wide, 1.5{micro}m thick), mechanical flexibility and spatial distribution as axon bundles in the brain. A simple approach of delivery is reported based on the assembly and controllable immobilization of the electrode onto a 35{micro}m microwire shuttle by using capillarity and surface-tension in aqueous solution. Once implanted into targeted regions of the brain, the microwire was retracted leaving NeuroRoots in the biological tissue with minimal surgical footprint and perturbation of existing neural architectures within the tissue. NeuroRoots was implanted using a platform compatible with commercially available electrophysiology rigs and with measurements of interests in behavioral experiments in adult rats freely moving into maze. We demonstrated that NeuroRoots electrodes reliably detected action potentials for at least 7 weeks and the signal amplitude and shape remained relatively constant during long-term implantation.\n\nThis research represents a step forward in the direction of developing the next generation of seamless brain-machine interface to study and modulate the activities of specific sub-populations of neurons, and to develop therapies for a plethora of neurological diseases.

neuroscience↗