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Jan Frans Willem Slaets

Publications and source records attributed to Jan Frans Willem Slaets.

3 recordsLinked to original sources

Automated pulse discrimination of two freely-swimming weakly electric fish and analysis of their electrical behavior during a dominance contest

Electric fishes modulate their electric organ discharges with a remarkable variability. Some patterns can be easily identified, such as pulse rate changes, offs and chirps, which are often associated with important behavioral contexts, including aggression, hiding and mating. However, these behaviors are only observed when at least two fish are freely interacting. Although their electrical pulses can be easily recorded by non-invasive techniques, discriminating the emitter of each pulse is challenging when physically similar fish are allowed to freely move and interact. Here we optimized a custom-made software recently designed to identify the emitter of pulses by using automated chirp detection, adaptive threshold for pulse detection and slightly changing how the recorded signals are integrated. With these optimizations, we performed a quantitative analysis of the statistical changes throughout the dominance contest with respect to Inter Pulse Intervals, Chirps and Offs dyads of freely moving Gymnotus carapo. In all dyads, chirps were signatures of subsequent submission, even when they occurred early in the contest. Although offs were observed in both dominant and submissive fish, they were substantially more frequent in submissive individuals, in agreement with the idea from previous studies that offs are electric cues of submission. In general, after the dominance is established the submissive fish significantly changes its average pulse rate, while the pulse rate of the dominant remained unchanged. Additionally, no chirps or offs were observed when two fish were manually kept in direct physical contact, suggesting that these electric behaviors are not automatic responses to physical contact.

q-bio.QM↗

Modular Acquisition and Stimulation System for Timestamp-Driven Neuroscience Experiments

Dedicated systems are fundamental for neuroscience experimental protocols that require timing determinism and synchronous stimuli generation. We developed a data acquisition and stimuli generator system for neuroscience research, optimized for recording timestamps from up to 6 spiking neurons and entirely specified in a high-level Hardware Description Language (HDL). Despite the logic complexity penalty of synthesizing from such a language, it was possible to implement our design in a low-cost small reconfigurable device. Under a modular framework, we explored two different memory arbitration schemes for our system, evaluating both their logic element usage and resilience to input activity bursts. One of them was designed with a decoupled and latency insensitive approach, allowing for easier code reuse, while the other adopted a centralized scheme, constructed specifically for our application. The usage of a high-level HDL allowed straightforward and stepwise code modifications to transform one architecture into the other. The achieved modularity is very useful for rapidly prototyping novel electronic instrumentation systems tailored to scientific research.

q-bio.QM↗

Individual discrimination of freely swimming pulse-type electric fish from electrode array recordings

Pulse-type weakly electric fishes communicate through electrical discharges with a stereotyped waveform, varying solely the interval between pulses according to the information being transmitted. This simple codification mechanism is similar to the one found in various known neuronal circuits, which renders these animals as good models for the study of natural communication systems, allowing experiments involving behavioral and neuroethological aspects. Performing analysis of data collected from more than one freely swimming fish is a challenge since the detected electric organ discharge (EOD) patterns are dependent on each animal's position and orientation relative to the electrodes. However, since each fish emits a characteristic EOD waveform, computational tools can be employed to match each EOD to the respective fish. In this paper we describe a computational method able to recognize fish EODs from dyads using normalized feature vectors obtained by applying Fourier and dual-tree complex wavelet packet transforms. We employ support vector machines as classifiers, and a continuity constraint algorithm allows us to solve issues caused by overlapping EODs and signal saturation. Extensive validation procedures with Gymnotus sp. showed that EODs can be assigned correctly to each fish with only two errors per million discharges.

q-bio.QM↗