Biology subjects

Kanitscheider, I.

Publications and source records attributed to Kanitscheider, I..

4 recordsLinked to original sources

bioRxiv2024

Weber's law and natural inference

For most extensive sensory variables such as speed or numerosity, the discrimination thresholds of human subjects are proportional to the value around which the discrimination is performed, a scaling known as Webers law. Many theories have been proposed for this law, which all rely on the assumption that neurons are noisy. By contrast, we argue here that noisy neurons are not required to explain Webers law. Instead, we propose that it is the unavoidable consequence of the statistics of natural sensory inputs. In natural environments, sensory measurements are typically scaled by global variables such as contrast in vision or loudness in audition. These global scaling parameters induce positive correlations among measurements which in turn lead to Webers scaling. This theory makes testable experimental predictions and accounts for the fact that tuning curves to speed and numerosity in vivo are approximately log normal.

animal behavior and cognition↗

bioRxiv2022

Spatial reasoning via recurrent neural dynamics in mouse retrosplenial cortex

From visual perception to language, sensory stimuli change their meaning depending on prior experience. Recurrent neural dynamics can interpret stimuli based on externally cued context, but it is unknown whether similar dynamics can compute and employ internal hypotheses to resolve ambiguities. Here, we show that mouse retrosplenial cortex (RSC) can form hypotheses over time and perform spatial reasoning through recurrent dynamics. In our task, mice navigated using ambiguous landmarks that are identified through their mutual spatial relationship, requiring sequential refinement of hypotheses. Neurons in RSC and in artificial neural networks encoded mixtures of hypotheses, location, and sensory information, and were constrained by robust low dimensional dynamics. RSC encoded hypotheses as locations in activity space with divergent trajectories for identical sensory inputs, enabling their correct interpretation. Our results indicate that interactions between internal hypotheses and external sensory data in recurrent circuits can provide a substrate for complex sequential cognitive reasoning.

neuroscience↗

bioRxiv2018

Sources of path integration error in young and aging humans

Path integration is a vital function in navigation: it enables the continuous tracking of ones position in space by integrating self-motion cues. Path integration abilities vary across individuals but tend to deteriorate in old age. The specific causes of path integration errors, however, remain poorly characterized. Here, we combined tests of path integration performance with a novel analysis based on the Langevin diffusion equation, which allowed us to decompose errors into distinct causes that can corrupt path integration computations. Across age groups, the dominant errors were due to noise and a bias in speed estimation. Noise-driven errors accumulated with travel distance not elapsed time, suggesting that the dominant noise originates in the velocity input rather than within the integrator. Age-related declines were traced primarily to a growth in this unbiased noise. Together, these findings shed light on the contributors to path integration error and the mechanisms underlying age-related navigational deficits.

neuroscience↗

bioRxiv2017

Emergence of dynamically reconfigurable hippocampal responses by learning to perform probabilistic spatial reasoning

Navigation in natural environments is computationally difficult: Location errors from motion estimation noise accumulate over time, while landmarks can be spatially extended and often look alike, thus providing ambiguous data. The brain contains a number of spatially tuned neurons coding for various navigational variables, but current models do not explain how these circuits could implement navigational computations that involve non-trivial spatial reasoning. We show, using a function-first approach, that neural circuits trained to efficiently solve spatial reasoning problems with performance on par with sequential probabilistic strategies reproduce some key properties of hippocampal coding, including heterogeneous tuning, conjunctive tuning, and low-dimensional dynamics. In addition, the models predict the emergence of tuning to key latent variables that are neither present in the input data nor trained as the end result of the task, and exhibit a spontaneous dynamical reconfiguration of tuning across time during a task as the computational demands evolve, reminiscent of some of the more complex dynamics observed in the hippocampus including a switch between location and displacement coding modes. These results provide a new functional framework for understanding the rich phenomenology and potential capabilities of navigation codes in the hippocampus and associated brain areas.

neuroscience↗