Biology subjects

Giocomo, L. M.

Publications and source records attributed to Giocomo, L. M..

23 records · Page 2Linked to original sources

bioRxiv2020

Distinct algorithms for combining landmarks and path integration in medial entorhinal, visual andretrosplenial cortex

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 unknown. Here, we combined electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three areas that support navigation: primary visual (V1), retrosplenial (RSC) and medial entorhinal cortex (MEC). Path integration influenced position estimates in MEC more than in V1 and RSC. V1 coded position retrospectively, likely reflecting delays in sensory processing, whereas MEC coded position prospectively, and RSC was intermediate between the two. In combining path integration with landmarks, MEC showed signatures of Kalman filtering, and we report a distance-tuned neural population that could implement such filtering through attractor dynamics. Our results show that during navigation, MEC serves as a specialized cortical hub for reconciling path integration and landmarks to estimate position and suggest an algorithm for calculating these estimates.

neuroscience↗

bioRxiv2020

TREBLE: a generalizable framework for high-throughput behavioral analysis

Uncovering relationships between neural activity and behavior represents a critical challenge, one that would benefit from facile tools that can capture complex structures within large datasets. Here we demonstrate a generalizable strategy for capturing such structures across diverse behaviors: Time-REsolved BehavioraL Embedding (TREBLE). Using data from synthetic trajectories, adult and larval Drosophila, and mice we show how TREBLE captures both continuous and discrete behavioral dynamics, can uncover variation across individuals, detect the effects of optogenetic perturbation in unbiased fashion, and reveal structure in pose estimation data. By applying TREBLE to moving mice, and medial entorhinal cortex (MEC) recordings, we show that nearly all MEC neurons encode information relevant to specific movement patterns, expanding our understanding of how navigation is related to the execution of locomotion. Thus, TREBLE provides a flexible framework for describing the structure of complex behaviors and their relationships to neural activity.

animal behavior and cognition↗

bioRxiv2019

Experience dependent contextual codes in the hippocampus

The hippocampus is a medial temporal lobe brain structure that contains circuitry and neural representations capable of supporting declarative memory. Hippocampal place cells fire in one or few restricted spatial locations in a given environment. Between environmental contexts, place cell firing fields remap (turning on/off or moving to a new spatial location), providing a unique population-wide neural code for context specificity. However, the manner by which features associated with a given context combine to drive place cell remapping remains a matter of debate. Here we show that remapping of neural representations in region CA1 of the hippocampus is strongly driven by prior beliefs about the frequency of certain contexts, and that remapping is equivalent to an optimal estimate of the identity of the current context under that prior. This prior-driven remapping is learned early in training and remains robust to changes in behavioral task-demands. Furthermore, a simple associative learning mechanism is sufficient to reproduce these results. Our findings demonstrate that place cell remapping is a generalization of representing an animals location. Rather than simply representing location in physical space, the hippocampus represents an optimal estimate of location in a multi-dimensional stimulus space.

neuroscience↗

bioRxiv2019

Entorhinal velocity signals reflect environmental geometry

The entorhinal cortex contains neural signals for representing self-location, including grid cells that fire in periodic locations and velocity signals that encode an animals speed and head direction. Recent work revealed that the size and shape of the environment influences grid patterns. Whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here, we report that changes to the size and shape of the environment result in re-scaling in entorhinal speed codes. Moreover, head direction cells re-organize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows a dissociation of the coordination between cell types, with grid and speed, but not head direction, cells responding in concert to environmental change. These results align with predictions of grid cell attractor models and point to inherent flexibility in the coding features of multiple functionally-defined entorhinal cell types.

neuroscience↗

bioRxiv2018

Emergent Elasticity in the Neural Code for Space

Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including: (1) systematic path-dependent shifts in the firing field of grid cells towards the most recently encountered landmark, even in a fully learned environment, (2) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers, and (3) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.

neuroscience↗