A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets

Abstract

How neural networks evolve to recognize species-specific communication signals is unknown. One hypothesis is that novel recognition phenotypes are produced by parameter variation in a computationally flexible “mother network”. We test this hypothesis in crickets, where males produce and females recognize mating songs with a species-specific pulse pattern. Whether the song recognition network in crickets is computationally flexible to recognize the diversity of pulse patterns and what network properties support and constrain this flexibility is unknown. Using electrophysiological recordings from the cricket Gryllus bimaculatus, we built a model of the song recognition network that reproduces the network dynamics as well as the neuronal and behavioral tuning for that species. An analysis of the model’s parameter space reveals that the network can produce all recognition phenotypes known in crickets and even other insects. Biases in phenotypic diversity produced by the model are consistent with the existing behavioral diversity in crickets, and arise from computations that likely evolved to increase energy efficiency and robustness of song recognition. The model’s parameter to phenotype mapping is degenerate – different network parameters can create similar changes in the phenotype – which is thought to support evolutionary plasticity. Our study suggest that a computationally flexible mother network could underlie the diversity of song recognition phenotypes in crickets and we reveal network properties that constrain and support behavioral diversity.

Neural network organization for courtship-song feature detection in Drosophila

“…[5][6][7] Although prior work has examined where selectivity for conspecific syllables emerges across a variety of systems, including songbirds, 8 primates, 9 and mice, 10 the precise circuit mechanisms shaping selectivity are unknown. For example, although a hypothesized circuit for recognizing stereotyped songs has been described in crickets, 11,12 connectivity among these neurons has not yet been determined, and cricket songs comprise only a single mode. Understanding how auditory systems establish selectivity for conspecific sounds requires linking responses to different song types with neuronal connectivity, a significant challenge in larger brains.…”

Neural Network Organization for Courtship Song Feature Detection inDrosophila

“…While prior work has examined where selectivity for conspecific sounds emerges across a variety of systems including songbirds (Moore and Woolley, 2019), primates (Romanski and Averbeck, 2009), and mice (Roberts and Portfors, 2015), how this selectivity arises remains unknown. While a hypothesized circuit for recognizing stereotyped conspecific songs has been described in crickets (Clemens et al, 2020; Schöneich et al, 2015), synaptic connectivity between neurons in the circuit has not yet been determined, and cricket songs comprise only a single mode. Ultimately, understanding how auditory systems establish selectivity for conspecific sounds requires linking information on responses to different song types with neuronal connectivity, a significant challenge in larger brains.…”

Response properties of spiking and non-spiking brain neurons mirror pulse interval selectivity

“…A computational modeling study demonstrates that LN5 response properties and its connection to LN3 rank very high in shaping the models response properties and can shape the tuning of the pattern recognition network ( Clemens et al, 2020 ). We cannot yet explain the LN5 response differences in our I1/I2 experiments and tentatively point toward different LN5-like neurons.…”

“…A computational modeling study demonstrates that LN5 response properties and its connection to LN3 rank very high in shaping the models response properties and can shape the tuning of the pattern recognition network (Clemens et al, 2020).…”