> Symposium

UTSA Neurosciences Institute

One UTSA Plaza San Antonio TX 78249

Hosted by CBI

September 14, 2017

BSB 3.03.02 | UTSA Main Campus

9:00a - 5:00p

2017 Neuroscience Symposium

Neural Codes of Navigation

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André A. Fenton


Center for Neural Science, NYU

Isabel Muzzio

Associate Professor

UTSA Neurosciences Institute

UT San Antonio

David Redish

Distinguished Mcknight University Professor

U Minnesota

James Knierim


Krieger Mind/Brain Institute

Johns Hopkins

September 14    9:00a-5:00p  Bios & Agenda

Speakers presented in alphabetical order:

André A. Fenton PhD

The dynamic structure of cognition: If space were time?

    We will focus on the microstructure of oscillatory events in the local field potential (LFP) and their interactions with the spike trains of spatially tuned cells in rodents as they perform place avoidance tasks on a rotating arena. The tasks require a functional hippocampus and that the subject judiciously uses information from the two dissociated spatial frames, one stationary the other rotating. This dissociation of spatial information during competent navigation has enabled us to investigate which neural properties of the navigation system are crucial for navigation.

    The fundamental physiological characteristics like the power and frequencies in the LFP and the firing rates of place, grid and directional cells are stable across the stationary and rotating conditions. However, we find that the microstructure in the time series of anatomically organized oscillatory events depends on experience, indicates the subject’s current knowledge, and reveals abnormalities when subjects make cognitive errors and express cognitive inflexibility. We find that place cell discharge is organized within the temporal infrastructure of these oscillatory events in the LFP, and that this organization depends on what the animal has learned, is doing or attempting to do. We find in these conditions that the temporal interactions within the network of spatially tuned cells appears to describe the cognitive state and spatial information processing effort of the animals better than the stability of the spatial tuning of the neurons, suggesting that the temporal interactions within the hippocampal and related neocortical networks provide a timing-based functional framework for information processing that is itself imposed to represent the physical space of the environment.

James Knierim PhD

Interaction between self motion & landmarks in hippocampal space codes

    Hippocampal place fields are controlled by a combination of self-motion cues (used for path integration) and visual landmarks. To quantitatively address the interaction between these two sets of cues, we created an experimental environment similar to a virtual reality (VR) system, in which an array of landmarks could be moved in real time based on the animal's own movements. Unlike typical VR systems, the rat physically moved around a circular trajectory, thereby maintaining normal locomotor and vestibular sensory cues. We moved the constellation of spatial landmarks projected onto the inside of a planetarium-style dome by a gain factor based on the rat's instantaneous velocity. A positive gain factor meant that the landmarks moved in the same direction as the rat, generating an illusion that the rat was moving slower than it actually was. In contrast, a negative gain factor meant that the landmarks moved in the opposite direction as the rat, generating an illusion that the rat was moving faster than it actually was. We found that even at extreme positive and negative gains, the place fields were strongly controlled by the landmarks, overriding the rat's speed signals from self-motion cues. However, when the landmarks were turned off, the place fields drifted backward as a coherent spatial map, revealing the influence of self-motion cues.

Isabel Muzzio PhD

Hippocampal representations of reorientation

    Behavioral evidence indicates that most vertebrates, from rodents to humans, primarily rely on spatial geometry, i.e. the shape of the navigable space, to reorient in space when lost. However, little is known about the neural mechanisms underlying this behavior. We recently found that although spatial geometry is used to determine facing direction within a context (heading retrieval), non-geometric visual cues are simultaneously used to identify the context where the animal is lost (place recognition). To determine the cellular correlates of heading retrieval, we first recorded hippocampal CA1 place cells of disoriented mice during foraging under no task contingencies in environments of distinct geometrical shape, each containing a unique polarizing visual cue. We found that the orientation of the hippocampal map was determined by the spatial geometry, not the polarizing visual cue. We then found that the orientation of the hippocampal map predicted reorientation behavior during the execution of a goal-oriented reorientation task on a trial-by-trial basis. Finally, to determine what happens with the hippocampal map in situations of contextual ambiguity, when the animals need to simultaneously recover their heading and determine the identity of the context where they are located, we recorded from hippocampal CA1 place cells of disoriented mice in a novel two chamber reorientation task. We found that the orientation of the place field map within each chamber was determined exclusively by spatial geometry, providing a coding mechanism for heading retrieval. However, firing rates significantly differed between the two chambers, providing a coding mechanism for place recognition. These results suggest that there are separate cognitive systems for place recognition and heading retrieval, which differentially rely on non-geometric features and geometry, respectively. These processes are represented in distinct manners in the hippocampus using a firing rate and a map orientation code.

David Redish PhD

Information processing differences between planning & procedural navigation systems

    The ability to travel from one place to another (i.e. "navigation") is solvable by multiple interacting and competing neural systems.  These systems are differently optimized for different situations and use different information processing algorithms.  From large ensemble neural recordings, we can identify how information is stored, processed, and used in these different systems.  Using behavioral choice tasks designed to access these different algorithms at different times, we can directly observe imagination and planning processes when animals are using planning systems and directly observe the development of habit representations as animals transition between navigation systems.