EthoLoop: automated closed-loop neuroethology in naturalistic environments

Nourizonoz, Ali; Zimmermann, Robert A.; Ho, Chun Lum Andy; Pellat, Sebastien; Ormen, Yannick; Prévost-Solié, Clément; Reymond, Gilles; Pifferi, Fabien; Aujard, Fabienne; Herrel, Anthony; Huber, Daniel

doi:10.1038/s41592-020-0961-2

Cited by 65 publications

(56 citation statements)

References 61 publications

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“…However, they are hard to combine with neuronal recordings. A smaller number of published complex behavioral environments include wireless recording of neural activity from single freely moving animals, although with limited behavioral control 29, 30 or with a simple behavioral loop that encouraged one specific behavior to obtain a reward 31 .…”

Section: Discussionmentioning

confidence: 99%

“…However, they are often hard to combine with neuronal recordings. Only a few behavioral environments include wireless recording of neural activity from single freely moving animals (Finkelstein et al, 2015;Nourizonoz et al, 2020;Yartsev & Ulanovsky, 2013). The novelty of the RIFF in this emerging field lies in its integrated approach allowing a rich repertoire of behavioral tasks combined with wireless electrophysiology.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, many environments for studying the behavior of freely moving animals in complex environments have been described but they often do not allow concomitant neuronal recordings [16][17][18][19][20][21][22][23][24][25][26][27][28] . A small number of publications describe complex environments that include wireless recording of neural activity in freely moving animals [29][30][31] , but allow only a limited behavioral range.…”

Section: Introductionmentioning

confidence: 99%

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The RIFF: an automated environment for studying the neural basis of auditory-guided complex behavior

Jankowski

Polterovich

Kazakov

et al. 2021

Preprint

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Behavior consists of the interaction between an organism and its environment, and is controlled by the brain. However, while brain activity varies at fast, sub-seconds time scales, behavioral measures tend to be temporally coarse, often limited just to the success or failure in a trial. The large gap between the temporal resolutions at which brain and behavior are observed likely impedes our understanding of the neural mechanisms underlying behavior. To overcome this problem, we developed the RIFF: an interactive arena for rats that has multiple feeding areas, multiple sound sources, and high-resolution tracking of behavior, with concomitant wireless electrophysiological recordings. The RIFF can be flexibly programmed to create arbitrarily complex tasks that the rats have to solve. It records unrestrained rat behavior together with neuronal data from chronically implanted electrodes. We present here a detailed description of the RIFF. We illustrate its power with results from two exemplary tasks. Rats learned within two days a complex task that required timed movement, and developed anticipatory behavior. Rats found solution strategies that differed between animals but were stable within each animal. We report auditory responses in and around primary auditory cortex as well as in the posterior insular cortex, but show that often the same neurons were also sensitive to non-auditory parameters such as rat location and body orientation. These parameters are crucial for state assessment and the selection of future actions. Our findings show that the complex, unrestrained behavior of rats can be studied in a controlled environment, enabling novel insights into the cognitive capabilities and learning mechanisms of rats. This combination of electrophysiology and detailed behavioral observation opens the way to a better understanding of how the brain controls behavior.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The RIFF: an automated environment for studying the neural basis of auditory-guided complex behavior

Jankowski

Polterovich

Kazakov

et al. 2021

Preprint

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“…An attractive feature of deep networks is the high-level of abstraction of the data-driven features that are exploited for classification. There are attempts to use this approach to study behavior in less restricted or controlled settings (Nourizonoz et al, 2020). In summary, with the progress in the field of deep learning, deep artificial neural networks is becoming popular in computational ethology.…”

Section: Social Behavior and Tracking Multiple Animals In The Homecage Using Deep Learningmentioning

confidence: 99%

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Bermudez-Contreras¹,

Whishaw²,

sutherland³

et al. 2021

Preprint

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The automation of monitoring and analysis of mouse behaviour in a homecage can be obtained from continuous video records with machine learning and computer vision. The approach of recreating a mouse’s “real world” behavior and laboratory test behavior in the “small world” of a laboratory cage can provide insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Algorithms identify behavioral acts (walk, rear), actions (sleep duration, distance travelled), organized patterns of movement (home base activity and excursions) over extended periods of time. In addition, performance on specific tests can be incorporated within a mouse’s living arrangement. Here we review approaches to engineering a small world and state of the art machine learning analyses for automated study of mouse homecage behavior. We highlight advantages and limitations of these approaches as a supplement to acute behavioral testing methodology.

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“…We used events to represent behaviour and event-processing and microservices to achieve 219 online capabilities(Larsen et al, 2021). Several studies have demonstrated the power of online 220 processing in closed loop assays in neuroscience(Grosenick et al, 2015;Nourizonoz et al, 2020), 221 to manipulate pitch in songbirds (Lohr et al, 2003; Brumm and Slabbekoorn, 2005; Riedner and 222 Adam, 2020) or to provide virtual reality (Ahrens et al, 2012; Reiser and Dickinson, 2008; Harvey 223 et al, 2009). Those studies take advantage of computationally attractive features, such as action 224…”

mentioning

confidence: 99%

Driving singing behaviour in songbirds using multi-modal, multi-agent virtual reality

Larsen

Adam

Berman

et al. 2021

Preprint

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Interactive biorobotics provides unique experimental potential to study the mechanisms underlying social communication but is limited by our ability to build expressive robots that exhibit the complex behaviours of birds and small mammals. An alternative to physical robots is to use virtual reality (VR). Here, we designed and built a modular, audio-visual virtual reality environment that allows online, multi-modal, multi-agent interaction for social communication. We tested this system in songbirds, which provide an exceptionally powerful and tractable model system to study social communication. We show that zebra finches (Taeniopygia guttata) communicating through the VR environment exhibit normal call timing behaviour, males sing female directed song and both males and females display high-intensity courtship behaviours to their mates. These results suggest that the VR system provides a sufficiently natural environment to elicit normal social communication behaviour. Furthermore, we developed a fully unsupervised online song motif detector and used it to manipulate the virtual social environment of male zebra finches based on the number of motifs sung. Our VR setup represents a first step in taking automatic behaviour annotation into the online domain and allows for animal-computer interaction using higher level behaviours such as song. Our unsupervised acoustic analysis eliminates the need for annotated training data thus reducing labour investment and experimenter bias.

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EthoLoop: automated closed-loop neuroethology in naturalistic environments

Cited by 65 publications

References 61 publications

The RIFF: an automated environment for studying the neural basis of auditory-guided complex behavior

The RIFF: an automated environment for studying the neural basis of auditory-guided complex behavior

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Driving singing behaviour in songbirds using multi-modal, multi-agent virtual reality

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