Neural Processing of Communication Signals: The Extent of Sender-Receiver Matching Varies across Species of Apteronotus

Supervised learning algorithm for analysis of communication signals in the weakly electric fish Apteronotus leptorhynchus

Signal analysis plays a preeminent role in neuroethological research. Traditionally, signal identification has been based on pre-defined signal (sub-)types, thus being subject to the investigator's bias. To address this deficiency, we have developed a supervised learning algorithm for the detection of subtypes of chirps-frequency/amplitude modulations of the electric organ discharge that are generated predominantly during electric interactions of individuals of the weakly electric fish Apteronotus leptorhynchus. This machine learning paradigm can learn, from a 'ground truth' data set, a function that assigns proper outputs (here: time instances of chirps and associated chirp types) to inputs (here: time-series frequency and amplitude data). By employing this artificial intelligence approach, we have validated previous classifications of chirps into different types and shown that further differentiation into subtypes is possible. This demonstration of its superiority compared to traditional methods might serve as proof-of-principle of the suitability of the supervised machine learning paradigm for a broad range of signals to be analyzed in neuroethology.

Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods

The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods.

Spooky Interaction at a Distance in Cave and Surface Dwelling Electric Fishes

Glass knifefish (Eigenmannia) are a group of weakly electric fishes found throughout the Amazon basin. Their electric organ discharges (EODs) are energetically costly adaptations used in social communication and for localizing conspecifics and other objects including prey at night and in turbid water. Interestingly, a troglobitic population of blind cavefish Eigenmannia vicentespelea survives in complete darkness in a cave system in central Brazil. We examined the effects of troglobitic conditions, which includes a complete loss of visual cues and potentially reduced food sources, by comparing the behavior and movement of freely behaving cavefish to a nearby epigean (surface) population (Eigenmannia trilineata). We found that the strengths of electric discharges in cavefish were greater than in surface fish, which may result from increased reliance on electrosensory perception, larger size, and sufficient food resources. Surface fish were recorded while feeding at night and did not show evidence of territoriality, whereas cavefish appeared to maintain territories. Surprisingly, we routinely found both surface and cavefish with sustained differences in EOD frequencies that were below 10 Hz despite being within close proximity of about 50 cm. A half century of analysis of electrosocial interactions in laboratory tanks suggest that these small differences in EOD frequencies should have triggered the "jamming avoidance response," a behavior in which fish change their EOD frequencies to increase the difference between individuals. Pairs of fish also showed significant interactions between EOD frequencies and relative movements at large distances, over 1.5 m, and at high differences in frequencies, often >50 Hz. These interactions are likely "envelope" responses in which fish alter their EOD frequency in relation to higher order features, specifically changes in the depth of modulation, of electrosocial signals.

Neural Synchrony Gives Rise to Amplitude- and Duration-Invariant Encoding Consistent With Perception of Natural Communication Stimuli

When confronted with a highly variable environment, it remains poorly understood how neural populations encode and classify natural stimuli to give rise to appropriate and consistent behavioral responses. Here we investigated population coding of natural communication signals with different attributes (i.e., amplitude and duration) in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. Our results show that, while single peripheral neurons encode the detailed timecourse of different stimulus waveforms, measures of population synchrony are effectively unchanged because of coordinated increases and decreases in activity. A phenomenological mathematical model reproduced this invariance and shows that this can be explained by considering homogeneous populations whose responses are solely determined by single neuron firing properties. Moreover, recordings from downstream central neurons reveal that synchronous afferent activity is actually decoded and thus most likely transmitted to higher brain areas. Finally, we demonstrate that the associated behavioral responses at the organism level are invariant. Our results provide a mechanism by which amplitude- and duration-invariant coding of behaviorally relevant sensory input emerges across successive brain areas thereby presumably giving rise to invariant behavioral responses. Such mechanisms are likely to be found in other systems that share anatomical and functional features with the electrosensory system (e.g., auditory, visual, vestibular).

Encoding and Perception of Electro-communication Signals in Apteronotus leptorhynchus

Animal communication plays an essential role in triggering diverse behaviors. It is believed in this regard that signal production by a sender and its perception by a receiver is co-evolving in order to have beneficial effects such as to ensure that conspecifics remain sensitive to these signals. However, in order to give appropriate responses to a communication signal, the receiver has to first detect and interpret it in a meaningful way. The detection of communication signals can be limited under some circumstances, for example when the signal is masked by the background noise in which it occurs (e.g., the cocktail-party problem). Moreover, some signals are very alike despite having different meanings making it hard to discriminate between them. How the central nervous system copes with these tasks and problems is a central question in systems neuroscience. Gymnotiform weakly electric fish pose an interesting system to answer these questions for various reasons: (1) they use a variety of communication signals called “chirps” during different behavioral encounters; (2) the central physiology of the electrosensory system is well known; and (3) most importantly, these fish give reliable behavioral responses to artificial stimuli that resemble natural communication signals, making it possible to uncover the neural mechanisms that lead to the observed behaviors.