Identification and characterization of neuronal cell classes in motor circuits are essential for understanding the neural basis of behavior. It is a challenging task, especially in a non-genetic model organism, to identify cell-specific expression of functional macromolecules. Here, we performed constellation pharmacology, calcium imaging of dissociated neurons to pharmacologically identify functional receptors expressed by vocal neurons in adult male and female African clawed frogs, Xenopus laevis. Previously we identified a population of vocal neurons called fast trill neurons (FTNs) in the amphibian parabrachial nucleus (PB) that express NMDA receptors and GABA and/or glycine receptors. Using constellation pharmacology, we identified four cell classes of putative fast trill neurons (pFTNs, responsive to both NMDA and GABA/glycine applications). We discovered that some pFTNs responded to the application of substance P (SP), acetylcholine (ACh), or both. Electrophysiological recordings obtained from FTNs using an ex vivo preparation verified that SP and/or ACh depolarize FTNs. Bilateral injection of ACh, SP, or their antagonists into PBs showed that ACh receptors are not sufficient but necessary for vocal production, and SP receptors play a role in shaping the morphology of vocalizations. Additionally, we discovered that the PB of adult female X. laevis also contains all the subclasses ofmore »
Ex vivo brain preparation to Analyze vocal pathways of Xenopus frogs.
Understanding the neural basis of behavior is a challenging task for technical reasons. Most methods of recording neural activity require animals to be immobilized, but neural activity associated with most behavior cannot be recorded from an anesthetized, immobilized animal. Using amphibians, however, there has been some success in developing in vitro brain preparations that can be used for electrophysiological and anatomical studies. Here, we describe an ex vivo frog brain preparation from which fictive vocalizations (the neural activity that would have produced vocalizations had the brain been attached to the muscle) can be elicited repeatedly. When serotonin is applied to the isolated brains of male and female African clawed frogs, Xenopus laevis, laryngeal nerve activity that is a facsimile of those that underlie sex-specific vocalizations in vivo can be readily recorded. Recently, this preparation was successfully used in other species within the genus including Xenopus tropicalis and Xenopus victorianus. This preparation allows a variety of techniques to be applied including extracellular and intracellular electrophysiological recordings and calcium imaging during vocal production, surgical and pharmacological manipulation of neurons to evaluate their impact on motor output, and tract tracing of the neural circuitry. Thus, the preparation is a powerful tool with which more »
- Award ID(s):
- 1934386
- Publication Date:
- NSF-PAR ID:
- 10315802
- Journal Name:
- Cold Spring Harbor protocols
- ISSN:
- 1940-3402
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein’s function based on its sequence, we still do not understand how to accurately predict an organism’s behavior based on neural activity. Here, we introduce the unsupervised Bayesian Ising Approximation (uBIA) for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. In data recorded during singing from neurons in a vocal control region, our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependenciesmore »
-
In many vertebrates, courtship occurs through the performance of elaborate behavioral displays that are as spectacular as they are complex. The question of how sexual selection acts upon these animals’ neuromuscular systems to transform a repertoire of pre-existing movements into such remarkable (if not unusual) display routines has received relatively little research attention. This is a surprising gap in knowledge, given that unraveling this extraordinary process is central to understanding the evolution of behavioral diversity and its neural control. In many vertebrates, courtship displays often push the limits of neuromuscular performance, and often in a ritualized manner. These displays can range from songs that require rapid switching between two independently controlled ‘voice boxes’ to precisely choreographed acrobatics. Here, we propose a framework for thinking about how the brain might not only control these displays, but also shape their evolution. Our framework focuses specifically on a major midbrain area, which we view as a likely important node in the orchestration of the complex neural control of behavior used in the courtship process. This area is the periaqueductal grey (PAG), as studies suggest that it is both necessary and sufficient for the production of many instinctive survival behaviors, including courtship vocalizations. Thus,more »
-
Continuing advances in neural interfaces have enabled simultaneous monitoring of spiking activity from hundreds to thousands of neurons. To interpret these large-scale data, several methods have been proposed to infer latent dynamic structure from high-dimensional datasets. One recent line of work uses recurrent neural networks in a sequential autoencoder (SAE) framework to uncover dynamics. SAEs are an appealing option for modeling nonlinear dynamical systems, and enable a precise link between neural activity and behavior on a single-trial basis. However, the very large parameter count and complexity of SAEs relative to other models has caused concern that SAEs may only perform well on very large training sets. We hypothesized that with a method to systematically optimize hyperparameters (HPs), SAEs might perform well even in cases of limited training data. Such a breakthrough would greatly extend their applicability. However, we find that SAEs applied to spiking neural data are prone to a particular form of overfitting that cannot be detected using standard validation metrics, which prevents standard HP searches. We develop and test two potential solutions: an alternate validation method (“sample validation”) and a novel regularization method (“coordinated dropout”). These innovations prevent overfitting quite effectively, and allow us to test whether SAEsmore »
-
Manto, Mario (Ed.)Background Cerebellar electrical stimulation has shown promise in improving motor recovery post-stroke in both rodent and human studies. Past studies have used motor evoked potentials (MEPs) to evaluate how cerebellar stimulation modulates ongoing activity in the cortex, but the underlying mechanisms are incompletely understood. Here we used invasive electrophysiological recordings from the intact and stroke-injured rodent primary motor cortex (M1) to assess how epidural cerebellar stimulation modulates neural dynamics at the level of single neurons as well as at the level of mesoscale dynamics. Methods We recorded single unit spiking and local field potentials (LFPs) in both the intact and acutely stroke-injured M1 contralateral to the stimulated cerebellum in adult Long-Evans rats under anesthesia. We analyzed changes in the firing rates of single units, the extent of synchronous spiking and power spectral density (PSD) changes in LFPs during and post-stimulation. Results Our results show that post-stimulation, the firing rates of a majority of M1 neurons changed significantly with respect to their baseline rates. These firing rate changes were diverse in character, as the firing rate of some neurons increased while others decreased. Additionally, these changes started to set in during stimulation. Furthermore, cross-correlation analysis showed a significant increase in coincidentmore »
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1101/pdb.prot106872
