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Cowan, N. J.

Publications and source records attributed to Cowan, N. J..

2 recordsLinked to original sources

Closed-Loop Control of Active Sensing Movements Regulates Sensory Slip.

Active sensing involves the production of motor signals for the purpose of acquiring sensory information [1-3]. The most common form of active sensing, found across animal taxa and behaviors, involves the generation of movements--e.g. whisking [4-6], touching [7,8], sniffing [9,10], and eye movements [11]. Active-sensing movements profoundly affect the information carried by sensory feedback pathways [12-15] and are modulated by both top-down goals (e.g. measuring weight vs. texture [1,16]) and bottom-up stimuli (e.g. lights on/off [12]) but it remains unclear if and how these movements are controlled in relation to the ongoing feedback they generate. To investigate the control of movements for active sensing, we created an experimental apparatus for freely swimming weakly electric fish, Eigenmannia virescens, that modulates the gain of reafferent feedback by adjusting the position of a refuge based on real time videographic measurements of fish position. We discovered that fish robustly regulate sensory slip via closed-loop control of active-sensing movements. Specifically, as fish performed the task of maintaining position inside the refuge [17-22], they dramatically up- or down-regulated fore-aft active-sensing movements in relation to a 4-fold change of experimentally modulated reafferent gain. These changes in swimming movements served to maintain a constant magnitude of sensory slip. The magnitude of sensory slip depended on the presence or absence of visual cues. These results indicate that fish use two controllers: one that controls the acquisition of information by regulating feedback from active sensing movements, and another that maintains position in the refuge, a control structure that may be ubiquitous in animals [23,24].

animal behavior and cognition

Recalibration of path integration in hippocampal place cells

Hippocampal place cells are spatially tuned neurons that serve as elements of a \"cognitive map\" in the mammalian brain1. To detect the animals location, place cells are thought to rely upon two interacting mechanisms: sensing the animals position relative to familiar landmarks2,3 and measuring the distance and direction that the animal has travelled from previously occupied locations4-7. The latter mechanism, known as path integration, requires a finely tuned gain factor that relates the animals self-movement to the updating of position on the internal cognitive map, with external landmarks necessary to correct positional error that eventually accumulates8,9. Path-integration-based models of hippocampal place cells and entorhinal grid cells treat the path integration gain as a constant9-14, but behavioral evidence in humans suggests that the gain is modifiable15. Here we show physiological evidence from hippocampal place cells that the path integration gain is indeed a highly plastic variable that can be altered by persistent conflict between self-motion cues and feedback from external landmarks. In a novel, augmented reality system, visual landmarks were moved in proportion to the animals movement on a circular track, creating continuous conflict with path integration. Sustained exposure to this cue conflict resulted in predictable and prolonged recalibration of the path integration gain, as estimated from the place cells after the landmarks were extinguished. We propose that this rapid plasticity keeps the positional update in register with the animals movement in the external world over behavioral timescales (mean 50 laps over 35 minutes). These results also demonstrate that visual landmarks not only provide a signal to correct cumulative error in the path integration system, as has been previously shown4,8,16-19, but also rapidly fine-tune the integration computation itself.

neuroscience