scholarly journals Deconstructing hunting behavior reveals a tightly coupled stimulus-response loop

2019 ◽  
Author(s):  
Duncan S. Mearns ◽  
Julia L. Semmelhack ◽  
Joseph C. Donovan ◽  
Herwig Baier

AbstractAnimals build behavioral sequences out of simple stereotyped actions. A comprehensive characterization of these actions and the rules underlying their temporal organization is necessary to understand sensorimotor transformations performed by the brain. Here, we use unsupervised methods to study behavioral sequences in zebrafish larvae. Generating a map of swim bouts, we reveal that fish modulate their tail movements along a continuum. We cluster bouts that share common kinematic features and contribute to similar behavioral sequences into seven modules. Behavioral sequences comprising a subset of modules bring prey into the anterior dorsal visual field of the larvae. Fish then release a capture maneuver comprising a stereotyped jaw movement and fine-tuned stereotyped tail movements to capture prey at various distances. We demonstrate that changes to chaining dynamics, but not module production, underlie prey capture deficits in two visually impaired mutants. Our analysis thus reveals the temporal organization of a vertebrate hunting behavior, with the implication that different neural architectures underlie prey pursuit and capture.

2020 ◽  
Vol 30 (1) ◽  
pp. 54-69.e9 ◽  
Author(s):  
Duncan S. Mearns ◽  
Joseph C. Donovan ◽  
António M. Fernandes ◽  
Julia L. Semmelhack ◽  
Herwig Baier

Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.


Author(s):  
Stefano Vassanelli

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.


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