Neuronal Dynamics Underlying Communication Signals in a Weakly Electric Fish: Implications for Connectivity in a Pacemaker Network

Lucas, Kathleen M.; Warrington, Julie; Lewis, Timothy J.; Lewis, John E.

doi:10.1016/j.neuroscience.2019.01.004

Cited by 9 publications

(24 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of particular interest in this study is the fact that our model was fit to data from two 268 related species (A. leptorhynchus and A. albifrons). While there are known differences 269 in cell counts, and frequencies [18,[37][38][39], little is known about the differences in 270 pacemaker network dynamics between these species [14]. We show preliminary data to 271 suggest that both species have similar action potential waveforms (figure 1B) despite 272 wide variations in baseline potential, peak-peak amplitude, and frequency.…”

mentioning

confidence: 76%

“…In contrast, the neural oscillators 10 underlying the electric organ discharge (EOD) of the weakly electric fish Apteronotus 11 have a CV as low as ∼10 -4 (corresponding to a raw standard deviation of ∼100ns), 12 making it the most precise biological oscillator known [10,13]. The high precision of the 13 EOD of Apteronotus makes it a particularly attractive model for the study of neural 14 circuit timing [10,13,14]. 15 Apteronotus generates an oscillating electric field (EOD) to sense their environment 16 in the dark [15].…”

Section: Introductionmentioning

confidence: 99%

“…The timing of the oscillations 18 underlying the EOD are set by the medullary pacemaker network (PN) [13,[16][17][18][19]. This 19 nucleus is a collection of two principle cell types: pacemaker cells which are intrinsic to 20 the PN, and relay cells which project down the spinal cord to drive the EOD [14,16,20]. 21 Additionally, there are parvalbumin positive cells (parvocells) whose function is 22 currently unknown, but are not thought to contribute to the oscillatory function of the 23 PN [21].…”

Section: Introductionmentioning

confidence: 99%

“…21 Additionally, there are parvalbumin positive cells (parvocells) whose function is 22 currently unknown, but are not thought to contribute to the oscillatory function of the 23 PN [21]. 24 The pacemaker cells in the PN are highly synchronized, with relative phases across 25 cells close to 2% of the oscillator period [10,14]. In general, networks are thought to 26 achieve high-precision and high-synchrony through the population-averaged activity of a 27 large number of strongly-connected cells [14,16,22].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamics of a neuronal pacemaker in the weakly electric fish Apteronotus

Shifman

Sun

Benoit

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

AbstractThe precise timing of neuronal activity is critical for normal brain function. In weakly electric fish, the medullary pacemaker network (PN) sets the timing for an oscillating electric organ discharge (EOD) used for electric sensing. This network is the most precise biological oscillator known, with sub-microsecond variation in oscillator period. The PN consists of two principle sets of neurons, pacemaker and relay cells, that are connected by gap junctions and normally fire in synchrony, one-to-one with each EOD cycle. However, the degree of gap junctional connectivity between these cells appears insufficient to provide the population averaging required for the observed temporal precision of the EOD. This has led to the hypothesis that individual cells themselves fire with high precision, but little is known about the oscillatory dynamics of these pacemaker cells. To this end, we have developed a biophysical model of a pacemaker neuron action potential based on experimental recordings. We validated the model by comparing the changes in oscillatory dynamics produced by different experimental manipulations. Our results suggest that a relatively simple model captures the complex dynamics exhibited by pacemaker cells, and that these dynamics may enhance network synchrony and precision.Author summaryMany neural networks in the brain exhibit activity patterns which oscillate regularly in time. These oscillations, like a clock, can provide a precise sense of time, enabling drummers to maintain complex beat patterns and pets to anticipate “feeding time”. The exact mechanisms by which brain networks give rise to these biological clocks are not clear. The pacemaker network of weakly electric fish has the highest precision of all known biological clocks. In this study, we develop a detailed biophysical model of neurons in the pacemaker network. We then validate the model against experiments using a nonlinear dynamics approach. Our results show that pacemaker precision is due, at least in part, to how individual pacemaker cells generate their activity. This supports the idea that temporal precision in this network is not solely an emergent property of the network but also relies on the dynamics of individual neurons.

show abstract

mentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamics of a neuronal pacemaker in the weakly electric fish Apteronotus

Shifman

Sun

Benoit

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Computational models has been previously used to answer different questions in the 265 study of electroreception and electrogenesis. Regarding electrogenesis, authors 266 in[23,26,30] used an anatomically detailed model of the pacemaker of Apteronotus 267 leptorhynchus to study the electric organ signal and its spatiotemporal features in 268 wave-type fish. In[29] a model for the Eigenmannia was employed to address the 269 jamming avoidance response in these fish.…”

mentioning

confidence: 99%

Modeling the variability of the electromotor command system of pulse electric fish

Fernández

Varona

Rodríguez

2020

Preprint

View full text Add to dashboard Cite

The electromotor neural system in weakly electric fish is a network responsible for active electroreception and electrolocation. This system controls the timing of pulse generation in the electrical signals used by these fish for extracting information from the environment and communicating with other specimens. Ethological studies related to fish mating, exploratory, submissive or aggressive behaviors have described distinct sequences of pulse intervals (SPIs). Accelerations, scallops, rasps, and cessations are four patterns of SPIs reported in pulse mormyrids, each showing characteristic temporal structures and large variability both in timing and duration. This paper presents a biologically plausible computational model of the electromotor command circuit that reproduces these four SPI patterns as a function of the input to the model while keeping the same internal parameter configuration. The topology of the model is based on a simplified representation of the network as described by morphological and electrophysiological studies. An initial ad hoc tuned configuration (S-T) was build to reproduce all four SPI patterns. Then, starting from S-T, a genetic algorithm (GA) was developed to automatically find the parameters of the model connectivity. Two different configurations obtained from the GA are presented here: one optimized to a set of synthetic examples of SPI patterns based on experimental observations in mormyrids (S-GA), and another configuration adjusted to patterns recorded from freely-behaving Gnathonemus Petersii specimens (R-GA). A robustness analysis to input variability of these model configurations was performed to discard overfitting and assess validity. Results showed that the four SPI patterns are consistently reproduced, both with synthetic (S-GA) data and with signals recorded from behaving animals (R-GA). This new model can be used as a tool to analyze the electromotor command chain during electrogeneration and assess the role of temporal structure in electroreception. Author summaryWeakly electric fish are a convenient system to study information processing in the nervous system. These fish have a remarkable sense of active electroreception, which allows them to generate and detect electrical fields for locating objects and communicating with other specimens in their surroundings. The electrical signal generated by these fish can be easily monitored noninvasively in freely-behaving animals. Activity patterns in this signal have been associated to different fish behaviors, like aggression or mating, for some species of the mormyridae family. In this work we use discharge patterns recorded from specimens of the Gnathonemus Petersii species along 1/20 with synthetic data to develop a model of the electromotor command network. The model network is based on morphological and physiological studies in this type of weakly electric fish. The parameters of this model were tuned using a genetic algorithm to fit both synthetic and recorded activity patterns. This computational model allows to simula...

show abstract