Biology subjects

Lienkaemper, C.

Publications and source records attributed to Lienkaemper, C..

3 recordsLinked to original sources

bioRxiv2025

When non-canonical olfaction is optimal

The early olfactory system is canonically described by a "one-receptor-to-one-neuron" model: each olfactory sensory neuron (OSN) expresses a single type of olfactory receptor. Although the olfactory systems of many model organisms approximately follow this canonical organization, a number of exceptions are known. In particular, Aedes aegypti mosquitoes co-express multiple types of olfactory receptors in many OSNs. Why do some olfactory systems follow the canonical organization while others violate it? We approach this question from the normative perspective of efficient coding. We find that the canonical and non-canonical organizations optimally encode odor signals in different types of olfactory environment. Non-canonical olfaction is beneficial when relevant sources emit correlated odorants and the environment contains odorants from ethologically irrelevant odor sources. Our theory explains previous observations of receptor co-expression and provides a framework from which to understand the structure of early olfactory systems.

neuroscience↗

bioRxiv2024

CA1 Engram Cell Dynamics Before and After Learning

A fundamental question in neuroscience is how memory formation shapes brain activity at the level of neuronal populations. Recent studies of hippocampal engram cells--neurons identified by learning-induced immediate early gene (IEG) expression--propose that these populations form the cellular substrate for memory. Previous experimental work suggests that cells are recruited into engrams via elevated intrinsic excitability and that learning drives coactivity among these cells to support retrieval. Despite this, an understanding of how engram dynamics evolve across learning and recall remains incomplete. Here, we combined activity-dependent genetic tagging with longitudinal two-photon calcium imaging to track CA1 engram population dynamics before and after fear conditioning. Our results reveal that engram activity is modulated by intrinsic dynamics, behavioral state, and stimulus-cued reactivation. Consistent with the idea that intrinsic dynamics bias engram allocation, spontaneous activity during rest predicted future engram membership--up to two days prior to Fos expression. In parallel, we found sequential activity during locomotion recruited both engram and non-engram cells, but that engram cells were less modulated by velocity. Furthermore, after fear conditioning, within the engram population, we identified a subset of cells that increased their correlations after fear learning, specifically during quiet rest. We also used a trace fear conditioning paradigm to show that conditioned stimulus presentation drove elevated activity and stable correlations amongst engram cells, demonstrating learning-dependent reactivation. Finally, computational modeling of CA3-CA1 circuit dynamics demonstrated that a network with strong excitatory-inhibitory balance, capable of CA3-driven reactivation, is consistent with our experimental results. Together, these results show that engram population dynamics are shaped by spontaneous states, behavior, and memory.

neuroscience↗

bioRxiv2017

The geometry of partial fitness orders and an efficient method for detecting genetic interactions

We present an efficient computational approach for detecting genetic interactions from fitness comparison data together with a geometric interpretation using polyhedral cones associated to partial orderings. Genetic interactions are defined by linear forms with integer coefficients in the fitness variables assigned to genotypes. These forms generalize several popular approaches to study interactions, including Fourier-Walsh coefficients, interaction coordinates, and circuits. We assume that fitness measurements come with high uncertainty or are even unavailable, as is the case for many empirical studies, and derive interactions only from comparisons of genotypes with respect to their fitness, i.e. from partial fitness orders. We present a characterization of the class of partial fitness orders that imply interactions, using a graph-theoretic approach. Our characterization then yields an efficient algorithm for testing the condition when certain genetic interactions, such as sign epistasis, are implied. This provides an exponential improvement of the best previously known method. We also present a geometric interpretation of our characterization, which provides the basis for statistical analysis of partial fitness orders and genetic interactions.

evolutionary biology↗