TheSurveillanceStateofBehavioralAutomation.final x

Running head: BEHAVIORAL AUTOMATION 1

Running head: BEHAVIORAL AUTOMATION 1

The Surveillance State of Behavioral Automation

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Introduction

Technologies have a significant impact on behavioral science. Genomics has dramatically grown over the past few years. Much of its growth can be attributed to the automatization of DNA sequencing. The “automatization of DNA sequencing” has been contributed by molecular biologists, computer scientists, and engineers’ combined efforts. The expansion of the knowledge of genomes has made the collections of transgenic animal strains possible. Not surprisingly, the scientific discoveries made on genetics and genomics give no adequate, comparable expansion to mutant animal’s brain functionality (Schaefer & Claridge-Chang, 2012). The basic understanding of a brain is the behavior it produces; hence automation of behavioral tests is being conducted. Molecular biologists, scientists, and engineers still take part in tackling this issue. Therefore, they are working to automize and digitize animal behavior experiments. This research paper shall examine the implications of these automized and digitized animal behavior. Moreover, it will assess obtaining the type of data required from these experiments. The paper handles neurogenetic systems for mice and flies; however, worms are also mentioned.

Throughput increase as a result of automation

Experimental throughput and phenotyping process are increasing as a result of the efforts in automatization. The use of activity monitors for screening circadian mutants has benefitted from the automation process (Winter & Schaefers, 2011). For example, activity monitoring of zebrafish found hundreds of drugs that impacted the rest and wake state. Also, hundreds of flies’ data were captured and analyzed due to the availability of cheap webcams. Therefore, social behaviors are better examined through automatization since it demands “a time-consuming eye scoring from a video”. Lack of automatization contributes to low throughput. There are more than seven action types of boxing and lunging, and aggressive flies. Therefore, the actions need to be captured through the information collected on their limb positions. Software is necessary to make the behavior accessible in a high throughput screening.

Automation Allows the Possibility of New Physiology Experiments

Usually, physiology is combined with larger animals’ behavior; however, automation leads to using physiology in smaller neurogenetic animals. Drosophila has a tiny size, and it has been a challenge to physiologists. However, virtual reality screens facilitate the fly walking and flight behavior to be examined “through pathophysiology and electrophysiology”; This allowed an increase in the responsiveness of motion-sensitive visual neurons. It showed a profound impact on sensory dynamics; Which led to the conclusion that no part of the brain is immune to the activity’s effects.

Another scenario is the worms (Crawley, 2008). In the first system, a ratiometric fluorescent signal was used to record a freely moving animal. However, the second system, the optogenetic physiological control, used automated tracking fitted with a projector for neural activity. Neural activation and inactivation operated on projectors with several color channels. These two systems used integrated search and transgenic interventions and optical systems. The main goal is to achieve the specificity of a single neuron for a freely moving animal.

Automated Observation for a Rodent Home Cage

Animal behavior is split into psychologists and ethologists. The psychologists provide for explicit laboratory experimentation while the ethologists emphasize detailed observation. It was hard for the ethologists to use traditional methods to understand brain Behaviour, and therefore the automation brings detailed and rich descriptions of behavior. For example, the mice were “inspected utilizing a videotape.” The results proved score actions such as walking and climbing, strain difference, and disease phenotypes (Goulding et al., 2008). Scoring by eye tends to be difficult since it is time-intensive and subjective. As a result, systems such as EthoVision track these movement patterns; This has made replacing human scorers possible. The techniques such as HomeCageScan, sniffing methods can detect posture and movement, rest and awake, rearing, and so on. Consequently, the software of the analysis methodology in quality and availability.

Nonetheless, there are limitations to this video tracking method. For instance, it requires not obstructed images; This makes it impossible to explore the variety of enrichment the home cage offers. Therefore, the solution to this is to have alternate systems that detect floor movements, for example, LABORAS (Crabbe et al., 1999). The main advantage is that they have restrictions depending on the complex environment. Also, lactometers and photo beams can be used to detect eating and other general movements.

In summary, automation has widespread use in increasing throughput and has a diverse effect on behavioral neuroscience. Automation transforms physiology by making the conducted experiments accessible. Also, it integrates ethology-type observation with these psychology experiments. Automation provides clarity into two aspects; first, the socially housed animals in the experiments can be tracked and sorted; This allows for an efficient interpretation of the experiment’s clear conditions. The second aspect is that automation allows high-resolution motion capturing methods to give an automated classification. These two aspects are very for the behavioral patterns and increasing natural conditions.

References

Crabbe, J. C., Wahlsten, D., & Dudek, B. C. (1999). Genetics of mouse behavior: interactions with laboratory environment. Science, 284(5420), 1670–1672.

https://science.sciencemag.org/content/284/5420/1670

Crawley, J. N. (2008). Behavioral phenotyping strategies for mutant mice. Neuron, 57(6), 809–818.

https://linkinghub.elsevier.com/retrieve/pii/S0896627308002122

Goulding, E. H., Schenk, A. K., Juneja, P., MacKay, A. W., Wade, J. M., & Tecott, L. H. (2008). A robust automated system elucidates mouse home cage behavioral structure. Proceedings of the National Academy of Sciences of the United States of America, 105(52), 20575–20582.

https://www.pnas.org/content/105/52/20575

Schaefer, A. T., & Claridge-Chang, A. (2012). The surveillance state of behavioral automation. Current Opinion in Neurobiology, 22(1), 170–176.

https://www.sciencedirect.com/science/article/pii/S0959438811001942?via%3Dihub

Winter, Y., & Schaefers, A. T. U. (2011). A sorting system with automated gates permits individual operant experiments with mice from a social home cage. Journal of Neuroscience Methods, 196(2), 276–280.

https://www.sciencedirect.com/science/article/abs/pii/S0165027011000434?via%3Dihub