Naturalistic Neuroscience – Towards a Full Cycle from Lab to Field

71.9K
views
58
authors
14
articles
Editors
5
Impact
Loading...

Recent technological advances greatly improved the possibility to study freely behaving animals in natural conditions. However, many systems still rely on animal-mounted devices, which can already bias behavioral observations. Alternatively, animal behaviors can be detected and tracked in recordings of stationary sensors, e.g., video cameras. While these approaches circumvent the influence of animal-mounted devices, identification of individuals is much more challenging. We take advantage of the individual-specific electric fields electric fish generate by discharging their electric organ (EOD) to record and track their movement and communication behaviors without interfering with the animals themselves. EODs of complete groups of fish can be recorded with electrode arrays submerged in the water and then be tracked for individual fish. Here, we present an improved algorithm for tracking electric signals of wave-type electric fish. Our algorithm benefits from combining and refining previous approaches of tracking individual specific EOD frequencies and spatial electric field properties. In this process, the similarity of signal pairs in extended data windows determines their tracking order, making the algorithm more robust against detection losses and intersections. We quantify the performance of the algorithm and show its application for a data set recorded with an array of 64 electrodes distributed over a 12 m2 section of a stream in the Llanos, Colombia, where we managed, for the first time, to track Apteronotus leptorhynchus over many days. These technological advances make electric fish a unique model system for a detailed analysis of social and communication behaviors, with strong implications for our research on sensory coding.

4,214 views
5 citations
Images from video and recording of a single unit during hunting behavior. (A) Location of a tetrode is shown in the dorsal region of the FB on the mantis's right side. (B) Shows the activity of the cell (blue) along with the movement of the prey (red). (C) Sample frames from Supplementary Video S1 are shown with time of frame numbers indicated in (B). (B,C) Prey starts to move in first frame. Central complex (CX) unit activity increases and then stops when the prey stops (2nd frame). In frame 3, the prey runs as the mantis tracks it, and CX unit activity increases and is maintained through frame 6. Before the mantis tracks the prey, the single CX unit is correlated with mantis movement (r > 0.7, sliding Pearson's coefficient). After the mantis orients toward the prey, the same unit is correlated (r > 0.7) with prey movement. (D) Another prey is on its back and struggles (top) and then stops (middle) and struggles again just before the strike. Activity, shown here as spikes below each frame, is parallel to movement.
Original Research
13 June 2022

Complex tasks like hunting moving prey in an unpredictable environment require high levels of motor and sensory integration. An animal needs to detect and track suitable prey objects, measure their distance and orientation relative to its own position, and finally produce the correct motor output to approach and capture the prey. In the insect brain, the central complex (CX) is one target area where integration is likely to take place. In this study, we performed extracellular multi-unit recordings on the CX of freely hunting praying mantises (Tenodera sinensis). Initially, we recorded the neural activity of freely moving mantises as they hunted live prey. The recordings showed activity in cells that either reflected the mantis's own movements or the actions of a prey individual, which the mantises focused on. In the latter case, the activity increased as the prey moved and decreased when it stopped. Interestingly, cells ignored the movement of the other prey than the one to which the mantis attended. To obtain quantitative data, we generated simulated prey targets presented on an LCD screen positioned below the clear floor of the arena. The simulated target oscillated back and forth at various angles and distances. We identified populations of cells whose activity patterns were strongly linked to the appearance, movement, and relative position of the virtual prey. We refer to these as sensory responses. We also found cells whose activity preceded orientation movement toward the prey. We call these motor responses. Some cells showed both sensory and motor properties. Stimulation through tetrodes in some of the preparations could also generate similar movements. These results suggest the crucial importance of the CX to prey-capture behavior in predatory insects like the praying mantis and, hence, further emphasize its role in behaviorally and ecologically relevant contexts.

5,160 views
7 citations
Review
18 May 2022
Neural Processing of Naturalistic Echolocation Signals in Bats
M. Jerome Beetz
 and 
Julio C. Hechavarría
Overview on decoding spatial information from echoes. (A) To infer an object’s azimuth, bats can use inter-aural amplitude, time (left), and spectral cues (middle), but also monaural spectral cues (right). Ipsilateral echoes (blue waves) are higher in amplitude (Amp) and reach the ipsilateral ear earlier than the contralateral ear (orange waves). Contralateral echoes bypass the bat’s head which creates spectral notches due to multiple reflections. To understand how spectral notches are generated, we assume that the echo gets reflected off the bat’s pinna. This evokes a second echo that temporally overlaps with the first echo. By assuming a pinna-tragus distance of 3.43 mm and considering that the second echo travels back to the tragus, sound waves with a wavelength of 6.86 mm (or 50 kHz) are phase shifted by 180° between both echoes. This creates a spectral notch at 50 kHz (red arrow in black power spectrum). Based on characteristic spectral notches, contralateral echoes are discriminable from ipsilateral ones. Because of the frequency dependent directionality, echoes from off-axis objects mainly contain low frequency portions. Echoes from on-axis objects additionally contain high frequency portions (Surlykke et al., 2009a; Simmons, 2012, 2014; Wohlgemuth et al., 2016b). (B) Elevation decoding is based on elevation-dependent spectral-notches in the echoes. (C) Target distance is computed based on echo-delays. Both call and echo travel the distance between the bat and the object at sound velocity. This leads to distance-dependent echo delays to which combination-sensitive neurons in the auditory system of bats are tuned to. (D) To determine the object’s shape, the edges can be scanned by computing echo-delays. As soon as the calls get reflected off objects in the background, the echo delays abruptly increase. (E) Texture roughness is determined by spectral cues like spectral notches.

Echolocation behavior, a navigation strategy based on acoustic signals, allows scientists to explore neural processing of behaviorally relevant stimuli. For the purpose of orientation, bats broadcast echolocation calls and extract spatial information from the echoes. Because bats control call emission and thus the availability of spatial information, the behavioral relevance of these signals is undiscussable. While most neurophysiological studies, conducted in the past, used synthesized acoustic stimuli that mimic portions of the echolocation signals, recent progress has been made to understand how naturalistic echolocation signals are encoded in the bat brain. Here, we review how does stimulus history affect neural processing, how spatial information from multiple objects and how echolocation signals embedded in a naturalistic, noisy environment are processed in the bat brain. We end our review by discussing the huge potential that state-of-the-art recording techniques provide to gain a more complete picture on the neuroethology of echolocation behavior.

5,986 views
10 citations
Recommended Research Topics