Biology subjects

Keemink, S. W.

Publications and source records attributed to Keemink, S. W..

4 recordsLinked to original sources

bioRxiv2025

Comparing Brain-Score and ImageNet performance with responses to the scintillating grid illusion

Perceptual illusions are widely used to study brain processing, and are essential for elucidating underlying function. Successful brain models should then also be able to reproduce these illusions. Some of the most successful models for vision are several variants of Deep Neural Networks (DNNs). These models can classify images with human-level accuracy, and many behavioral and activation measurements correlate well with humans and animals. For several networks it was also shown that they can reproduce some human illusions. However, this was typically done for a limited number of networks. In addition, it remains unclear whether the presence of illusions is linked to either how accurate or brain-like the DNNs are. Here, we consider the scintillating grid illusion, to which two DNNs have been shown to respond as if they are impacted by the illusion. We develop a measure for measuring Illusion Strength based on model activation correlations, which takes into account the difference in Illusion Strength between illusion and control images. We then compare the Illusion Strength to both model performance (top-1 ImageNet), and how well the model explains brain activity (Brain-score). We show that the illusion was measurable in a wide variety of networks (41 out of 51). However, we do not find a strong correlation between Illusion Strength and Brain-Score, nor performance. Some models have strong illusion scores but not Brain-Score, or vice-versa, but no model does both well. Finally, this differs strongly between model types, particularly between convolutional and transformer-based architectures, with transformers having low illusion scores. Overall, our work shows that Illusion Strength measures an important metric to consider for assessing brain models, and that some models could still be missing out on some processing important for brain functioning.

neuroscience↗

bioRxiv2025

Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems

Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions--including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples--derived from noisy inputs--with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity "bump" representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.

neuroscience↗

bioRxiv2024

Biases in population codes with a few active neurons

Throughout the brain information is coded in the activity of multiple neurons at once, so called population codes. Population codes are a robust and accurate way of coding information. One can evaluate the quality of population coding by trying to read out the code with a decoder, and estimate the encoded stimulus. Coding quality has traditionally been evaluated in terms of the trial-to-trial variation in the estimate. However, codes can also display biases. While most decoders yield unbiased estimators in the limit of many active neurons, we find that when only few neurons are active, biases readily emerge for many decoders. We show that the biases turn out to have a non-trivial dependence on noise and tuning curve properties. We also introduce a technique to estimate the bias and variance of Bayesian decoders. Overall, the work expands our understanding of population coding.

neuroscience↗

bioRxiv2024

Context-dependence of deterministic and nondeterministic contributions to closed-loop steering control

In natural circumstances, sensory systems operate in a closed loop with motor output, whereby actions shape subsequent sensory experiences. A prime example of this is the sensorimotor processing required to align ones direction of travel, or heading, with ones goal, a behavior we refer to as steering. In steering, motor outputs work to eliminate errors between the direction of heading and the goal, modifying subsequent errors in the process. The closed-loop nature of the behavior makes it challenging to determine how deterministic and nondeterministic processes contribute to behavior. We overcome this by applying a nonparametric, linear kernel-based analysis to behavioral data of monkeys steering through a virtual environment in two experimental contexts. In a given context, the results were consistent with previous work that described the transformation as a second-order linear system. Classically, the parameters of such second-order models are associated with physical properties of the limb such as viscosity and stiffness that are commonly assumed to be approximately constant. By contrast, we found that the fit kernels differed strongly across tasks in these and other parameters, suggesting context-dependent changes in neural and biomechanical processes. We additionally fit residuals to a simple noise model and found that the form of the noise was highly conserved across both contexts and animals. Strikingly, the fitted noise also closely matched that found previously in a human steering task. Altogether, this work presents a kernel-based analysis that characterizes the context-dependence of deterministic and non-deterministic components of a closed-loop sensorimotor task. New and noteworthyWe use nonparametric systems identification techniques to assess the context-dependence of deterministic and nondeterministic contributions to a closed-loop behavior. Classical approaches assume a fixed transformation between sensory input and motor output. Here, we reveal strong changes to the measured sensorimotor transformations with behavioral context. In contrast, noise within the transformation exhibited a consistent form across contexts, subjects, and species. Together, this work demonstrates how context affects the systematic and stochastic components of a closed-loop behavior.

neuroscience↗