Biology subjects

Muresan, R. C.

Publications and source records attributed to Muresan, R. C..

4 recordsLinked to original sources

bioRxiv2023

Randomness impacts specific priors building, visual exploration, and perception in object recognition

1.Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack understanding on the role visual information sampling plays in this process, and its relation to the individuals priors. To bridge this gap, the eye-movements of 18 adult participants were recorded during a free-viewing object-recognition task using Dots stimuli (Moca et al., 2011). Participants viewed the stimuli in one of three orders: from most visible to least (Descending), least visible to most (Ascending) or in a randomised order (Random). This dictated the strength of their priors along the experiment. Adding to Moca et al.s original finding that visibility order influenced participants recognition performance and visual exploration, we found that while orders allowing for stronger priors generally led participants to visually sample more informative locations, this was not the case of Random participants. Indeed, they appeared to behave naively, and their use of specific object-related priors seemed fully impaired, while they seemed to maintain the ability to use general task-related priors to guide their exploration. These findings have important implications for our understanding of perception, which appears markedly subjective even at the basic level of visual sampling and object perception.

neuroscience↗

bioRxiv2023

Fast updating feedback from piriform cortex to the olfactory bulb relays multimodal reward contingency signals during rule-reversal

While animals readily adjust their behavior to adapt to relevant changes in the environment, the neural pathways enabling these changes remain largely unknown. Here, using multiphoton imaging, we investigated whether feedback from the piriform cortex to the olfactory bulb supports such behavioral flexibility. To this end, we engaged head-fixed mice in a multimodal rule-reversal task guided by olfactory and auditory cues. Both odor and, surprisingly, the sound cues triggered cortical bulbar feedback responses which preceded the behavioral report. Responses to the same sensory cue were strongly modulated upon changes in stimulus-reward contingency (rule reversals). The re-shaping of individual bouton responses occurred within seconds of the rule-reversal events and was correlated with changes in the behavior. Optogenetic perturbation of cortical feedback within the bulb disrupted the behavioral performance. Our results indicate that the piriform-to-olfactory bulb feedback carries reward contingency signals and is rapidly re-formatted according to changes in the behavioral context.

neuroscience↗

bioRxiv2022

Gradient-k: Improving the performance of K-Means using the density gradient

We introduce Gradient-k, an upgrade of the k-means algorithm that improves clustering accuracy and reduces the number of iterations required for convergence. This is achieved by correcting the distance used in the k-means algorithm by a factor based on the angle between the density gradient and the direction to the cluster center. We show that this correction allows the algorithm to go beyond the traditional tessellation obtained by k-means, enabling the creation of nonlinear separation boundaries. The correction also provides the ability to distinguish clusters having non-normal shapes and of varying densities. Furthermore, Gradient-k has a lower computational complexity for datasets with many samples. We compare the performance of Gradient-k with that of k-means and of DBSCAN on several benchmark datasets, as well as on a more realistic dataset in the context of neural spike sorting.

bioinformatics↗

bioRxiv2019

Superlets: time-frequency super-resolution using wavelet sets

Time-frequency analysis is ubiquitous in many fields of science. Due to the Heisenberg-Gabor uncertainty principle, a single measurement cannot estimate precisely the location of a finite oscillation in both time and frequency. Classical spectral estimators, like the short-time Fourier transform (STFT) or the continuous-wavelet transform (CWT) optimize either temporal or frequency resolution, or find a tradeoff that is suboptimal in both dimensions. Following concepts from optical super-resolution, we introduce a new spectral estimator enabling time-frequency super-resolution. Sets of wavelets with increasing bandwidth are combined geometrically in a superlet to maintain the good temporal resolution of wavelets and gain frequency resolution in the upper bands. Superlets outperform the STFT, CWT, and other super-resolution methods on synthetic data and brain signals recorded in humans and rodents, resolving time-frequency details with unprecedented precision. Importantly, superlets can reveal transient oscillation events that are hidden in the averaged time-frequency spectrum by other methods.

neuroscience↗