Biology subjects

Toosi, R.

Publications and source records attributed to Toosi, R..

4 recordsLinked to original sources

bioRxiv2024

Microsaccade Selectivity as Discriminative Feature for Object Decoding

Microsaccades, a form of fixational eye movements, maintain visual stability during stationary observations. Previous studies have provided valuable insights into the relationship between microsaccade characteristics and external stimuli. However, the dynamic nature of microsaccades provides an opportunity to explore the mechanisms of information processing, particularly object decoding. This study examines the modulation of microsaccadic rates by different stimulus categories. Our experimental approach involves an analysis of microsaccade characteristics in monkeys and human subjects engaged in a passive viewing task. The stimulus categories comprised four primary categories: human, animal, natural, and man-made. We identified distinct microsaccade patterns across different stimulus categories, successfully decoding the stimulus category based on the microsaccade rate post-stimulus distribution. Our experiments demonstrate that stimulus categories can be classified with an average accuracy and recall of up to 85%. Our study found that microsaccade rates are independent of pupil size changes. Neural data showed that category classification in the inferior temporal (IT) cortex peaks earlier than microsaccade rates, suggesting a feedback mechanism from the IT cortex that influences eye movements after stimulus discrimination. These results exhibit potential for advancing neurobiological models, developing more effective human-machine interfaces, optimizing visual stimuli in experimental designs, and expanding our understanding of the capability of microsaccades as a feature for object decoding.

neuroscience↗

bioRxiv2023

The Spatial Frequency Representation Predicts Category Coding in the Inferior Temporal Cortex

Understanding the neural representation of spatial frequency (SF) in the primate cortex is vital for unraveling visual processing mechanisms in object recognition. While numerous studies concentrate on the representation of SF in the primary visual cortex, the characteristics of SF representation and its interaction with category representation remain inadequately understood. To explore SF representation in the inferior temporal (IT) cortex of macaque monkeys, we conducted extracellular recordings with complex stimuli systematically filtered by SF. Our findings disclose an explicit SF coding at single-neuron and population levels in the IT cortex. Moreover, the coding of SF content exhibits a coarse-to-fine pattern, declining as the SF increases. Temporal dynamics analysis of SF representation reveals that low SF (LSF) is decoded faster than high SF (HSF), and the SF preference dynamically shifts from LSF to HSF over time. Additionally, the SF representation for each neuron forms a profile that predicts category selectivity at the population level. IT neurons can be clustered into four groups based on SF preference, each exhibiting different category coding behaviors. Particularly, HSF-preferred neurons demonstrate the highest category decoding performance for face stimuli. Despite the existing connection between SF and category coding, we have identified uncorrelated representations of SF and category. In contrast to the category coding, SF is more sparse and places greater reliance on the representations of individual neurons. Comparing SF representation in the IT cortex to deep neural networks, we observed no relationship between SF representation and category coding. However, SF coding, as a category-orthogonal property, is evident across various ventral stream models. These results dissociate the separate representations of SF and object category, underscoring the pivotal role of SF in object recognition.

neuroscience↗

bioRxiv2021

The Effect of Spatial Frequency on the Visual Category Representation in the Macaque Inferior Temporal Cortex

To expand our knowledge about the object recognition, it is critical to understand the role of spatial frequency (SF) in an object representation that occurs in the inferior temporal (IT) cortex at the final stage of processing the visual information across the ventral visual pathway. Object categories are being recognized hierarchically in at least three levels of abstraction: superordinate (e.g., animal), mid-level (e.g., human face), and subordinate (e.g., face identity). Psychophysical studies have shown rapid access to mid-level category information and low SF (LSF) contents. Although the hierarchical representation of categories has been shown to exist inside the IT cortex, the impact of SF on the multi-level category processing is poorly understood. To gain a deeper understanding of the neural basis of the interaction between SF and category representations at multiple levels, we examined the neural responses within the IT cortex of macaque monkeys viewing several SF-filtered objects. Each stimulus could be either intact or bandpass filtered into either the LSF (coarse shape information) or high SF (HSF) (fine shape information) bands. We found that in both High- and Low-SF contents, the advantage of mid-level representation has not been violated. This evidence suggests that mid-level category boundary maps are strongly represented in the IT cortex and remain unaffected with respect to any changes in the frequency content of stimuli. Our observations indicate the necessity of the HSF content for the superordinate category representation inside the IT cortex. In addition, our findings reveal that the representation of global category information is more dependent on the HSF than the LSF content. Furthermore, the lack of subordinate representation in both LSF and HSF filtered stimuli compared to the intact stimuli provide strong evidence that all SF contents are necessary for fine category visual processing.

neuroscience↗

bioRxiv2020

An Adaptive Detection for Automatic Spike Sorting Based on Mixture of Skew-t distributions

Developing new techniques of simultaneous recoding using thousand electrodes, make the wide variety of spike waveforms across multiple channels. This problem causes spike loss and raise the crucial issue of spike sorting with unstable clusters. While there exist many automatic spike sorting methods, there has been a lack of studies developing robust and adaptive spike detection algorithm. Here, an adaptive procedure is introduced to improve the detection of spikes in different scenarios. This procedure includes a new algorithm which aligns the spike waveforms at the point of extremums. The other part is statistical filtering, which seeks to remove noises that mistakenly detected as true spike. To deal with non-symmetrical clusters, we proposed a new clustering algorithm based on the mixture of skew-t distributions. The proposed method could overcome the spike loss and skewed cells challenges by offering an improvement over automatic detection, alignment, and clustering of spikes. Investigating the sorted spikes, reveals that proposed adaptive algorithm improves the performance of the spike detection in both terms of precision and recall. The adaptive algorithm has been validated on different datasets and demonstrates a general solution to precise spike sorting, in vitro and in vivo.

neuroscience↗