Biology subjects

Klindt, D.

Publications and source records attributed to Klindt, D..

5 recordsLinked to original sources

bioRxiv2026

High-dimensional population codes reveal interpretable and diverse features underlying visual perception

Despite large-scale recordings, neuroscience has identified representations for only a small fraction of visual features distinguishable by humans. This is increasingly attributed to mixed selectivity, where individual neurons respond to multiple stimuli, requiring population-level readouts of human-recognizable ('interpretable') representations. To identify interpretable representations at scale, we 1)computationally extract population codes from macaque electrophysiology datasets spanning V4 and IT cortex and vision models and 2) introduce an automated method to measure representation interpretability and diversity, then isolate a set of unique, meaningful features encoded by neurons versus populations. Across datasets, populations represent more interpretable, diverse features than neurons. This advantage grows with the number of sampled neurons and image diversity. Finally, we demonstrate through targeted ablations that interpretable representations play a causal role in downstream behavior. Overall, our framework enables a more comprehensive account of the features represented across biological and artificial visual systems and their contributions to visual perception.

neuroscience↗

bioRxiv2026

A geometric and dynamical theory of latent computations in biological neural networks

Many neural recordings have revealed low-dimensional sets of behaviorally relevant variables encoded within large-scale neural activity patterns. However, dimensionality reduction analyses alone cannot yield causal explanations for how networks stably implement computations that are resilient to the substantial variability of single neuron dynamics. Further, existing methods for dimensionality reduction often rely on simplifying assumptions about network structure that limit their applicability and explanatory power. To provide a theoretical framework describing the dynamics of low-dimensional computation in high-dimensional neural networks, here we introduce the concept of latent processing units (LPUs), which are architecture-agnostic computational elements operating within biological neural circuitry. Six theorems governing coding and computation by LPUs collectively provide explanations for a range of common biological findings: low-dimensional sets of coding variables can generate high-dimensional neural dynamics; many neurons have activity patterns that represent behaviorally relevant variables but exert little influence on downstream circuits; linear readouts of neural population activity commonly permit near-optimal decoding; the drift of neural representations is often substantial even while network computations remain intact. Overall, our treatment of LPUs, as enacted in network dynamics, unifies the geometric and dynamical views of neural computation under a joint framework and provides systems neuroscience with a causal account of how the brain executes reliable computations.

neuroscience↗

bioRxiv2020

Mouse retinal specializations reflect knowledge of natural environment statistics

Pressures for survival drive sensory circuit adaption to a species habitat, making it essential to statistically characterise natural scenes. Mice, a prominent visual system model, are dichromatic with enhanced sensitivity to green and UV. Their visual environment, however, is rarely considered. Here, we built a UV-green camera to record footage from mouse habitats. We found chromatic contrast to greatly diverge in the upper but not the lower visual field, an environmental difference that may underlie the species superior colour discrimination in the upper visual field. Moreover, training an autoencoder on upper but not lower visual field scenes was sufficient for the emergence of colour-opponent filters. Furthermore, the upper visual field was biased towards dark UV contrasts, paralleled by more light-offset-sensitive cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution. Lead contactFurther information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Thomas Euler (thomas.euler@cin.uni-tuebingen.de)

neuroscience↗

bioRxiv2020

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retinas cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal func{-}tion are black-box CNN models which are agnostic to such biological knowledge. In particular, these models typically neglect the role of the many inhibitory circuits involving amacrine cells and the biophysical mechanisms underlying synaptic release. Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms. Fit to the responses of bipolar cells, the model generalized well to new stimuli including natural movie sequences, performing on par with or better than a benchmark black-box model. In pharmacology experiments, the model replicated in silico the effect of blocking specific amacrine cell populations with high fidelity, indicating that it had learned key circuit functions. Also, more in depth comparisons showed that connectivity patterns learned by the model were well matched to connectivity patterns extracted from connectomics data. Thus, our model provides a biologically interpretable data-driven account of temporal processing in the inner retina, filling the gap between purely black-box and detailed biophysical modeling.

neuroscience↗

bioRxiv2019

The temporal structure of the inner retina at a single glance

The retina decomposes visual stimuli into parallel channels that encode different features of the visual environment. Central to this computation is the synaptic processing in a dense and thick layer of neuropil, the so-called inner plexiform layer (IPL). Here, different types of bipolar cells stratifying at distinct depths relay the excitatory feedforward drive from photoreceptors to amacrine and ganglion cells. Current experimental techniques for studying processing in the IPL do not allow imaging the entire IPL simultaneously in the intact tissue. Here, we extend a two-photon microscope with an electrically tunable lens allowing us to obtain optical vertical slices of the IPL, which provide a complete picture of the response diversity of bipolar cells at a \"single glance\". The nature of these axial recordings additionally allowed us to isolate and investigate batch effects, i.e. inter-experimental variations resulting in systematic differences in response speed. As a proof of principle, we developed a simple model that disentangles biological from experimental causes of variability, and allowed us to recover the characteristic gradient of response speeds across the IPL with higher precision than before. Our new framework will make it possible to study the computations performed in the central synaptic layer of the retina more efficiently.

neuroscience↗