Biology subjects

Schuessler, F.

Publications and source records attributed to Schuessler, F..

3 recordsLinked to original sources

bioRxiv2026

Innate development of cognitive functions and motor programs by chemoaffinity

Humans and animals are equipped with a rich innate repertoire of cognitive and behavioral skills [1-3]. Yet, the developmental programs that establish the underlying neural structures are unknown. During early development, neural connectivity is shaped by molecular axon guidance and cell adhesion programs that connect neurons based on the affinity between presynaptic receptors and postsynaptic ligands [4, 5]. Here, we show how such chemoaffinity-based connectivity rules can also establish innate cognitive functions and motor programs by structuring recurrent neuronal networks prior to experience. Different networks develop depending on the statistics of receptor and ligand expression. We illustrate this mechanism in computational models of chemoaffinity-based development that establish i) continuous attractor networks for path integration [6] with a toroidal grid cell topology [7], ii) networks with an exponentially large number of discrete attractors and sequences [8] as categorical, hierarchical, or temporal priors [9, 10], and iii) networks for arbitrary innate motor trajectories. Hence, chemoaffinity may shape not only the anatomical organization of the brain but also its innate cognitive and motor functions.

neuroscience↗

bioRxiv2024

Mechanical problem solving in mice

Behavioral learning is a complex phenomenon that involves cognitive, perceptual, motor and emotional contributions. Yet, most animal studies focus on reductionist tasks that aim to isolate one of these aspects [1, 2, 3, 4]. Here, we use a lockbox -- a complex, multi-step mechanical puzzle -- to study learning dynamics in freely behaving mice. The mice engaged spontaneously with the task and learned to solve it within just a few trials. To dissect different contributions to this rapid form of learning, we combined deep learning-based behavioral tracking in a multi-camera setup with probabilistic inference and computational modeling. We find that the learning progress of the mice was initially dominated by the acquisition of motor skills, i.e., the increased ability to manipulate the individual mechanisms, while a cognitive strategy for the task sequence emerged only later. The lockbox paradigm may hence offer a promising framework for studying the interaction between low-level motor learning and high-level decision-making strategies in a single, ethologically relevant task.

animal behavior and cognition↗

bioRxiv2018

Propagation of orientation selectivity in a spiking network model of layered primary visual cortex

We studied the propagation of orientation selectivity over layers in a model of rodent primary visual cortex in terms of the underlying connectivity. While significant progress has been made in measuring the activity in the different sub-populations as well as the connectivity between those in experiments, a comprehensive theoretical explanation of how network structure and its dynamics are related is still missing. We suggest a model of layered mouse visual cortex by extending the model suggested by Potjans and Diesmann (2014) with thalamic input that has an orientation bias according to Sadeh et al. (2014). We then studied the response properties of the network to such non-homogeneous input. We found that, without further assumptions, the connectivity derived from experimental data leads to layer-specific distributions of orientation selectivity very similar to what has been observed in mouse experiments. Interestingly, the network settles in a dynamical operating point, in which the efficacy of the different projections and their contribution to orientation tuning deviate strongly from the expectations based on the underlying anatomical connectivity. To understand the processes that shape the observed dynamics, it is essential to perform the analysis of the microcircuit on the system level. With that perspective, we find in particular that the difference in tuning of L2/3 and L4 neurons is an immediate, albeit unexpected consequence of the specific network connectivity. Furthermore, we introduce a novel method for predicting the effects of optogenetic stimulation of specific neuronal sub-populations and demonstrate its power in network simulations.\n\nSignificance StatementUnderstanding the precise roles of neuronal sub-populations in shaping the activity of neuronal networks is a fundamental objective of neuroscience research. To this end, our work makes three important contributions. First, we show that the experimentally extracted connectivity suffices to explain the degree of selectivity of sub-populations in mouse visual cortex to visual stimulation. Second, we introduce a novel system-level approach for the analysis of input-output relations of recurrent networks, which lead to distinct activity patterns. Third, we present a method for the design of optogenetic experiments that can be used to devise specific stimuli which result in a desired and predictable change of neuronal activity.

neuroscience↗