James J. Jun

Precision measurement of electric organ discharge timing from freely moving weakly electric fish

Physiological measurements from an unrestrained, untethered, and freely moving animal permit analyses of neural states correlated to naturalistic behaviors of interest. Precise and reliable remote measurements remain technically challenging due to animal movement, which perturbs the relative geometries between the animal and sensors. Pulse-type electric fish generate a train of discrete and stereotyped electric organ discharges (EOD) to sense their surroundings actively, and rapid modulation of the discharge rate occurs while free swimming in Gymnotus sp. The modulation of EOD rates is a useful indicator of the fishs central state such as resting, alertness, and learning associated with exploration. However, the EOD pulse waveforms remotely observed at a pair of dipole electrodes continuously vary as the fish swims relative to the electrodes, which biases the judgment of the actual pulse timing. To measure the EOD pulse timing more accurately, reliably, and noninvasively from a free-swimming fish, we propose a novel method based on the principles of waveform reshaping and spatial averaging. Our method is implemented using envelope extraction and multichannel summation, which is more precise and reliable compared with other widely used threshold- or peak-based methods according to the tests performed under various source-detector geometries. Using the same method, we constructed a real-time electronic pulse detector performing an additional online pulse discrimination routine to enhance further the detection reliability. Our stand-alone pulse detector performed with high temporal precision (<10 μs) and reliability (error <1 per 10(6) pulses) and permits longer recording duration by storing only event time stamps (4 bytes/pulse).

Enhanced sensory sampling precedes self-initiated locomotion in an electric fish

Cortical activity precedes self-initiated movements by several seconds in mammals; this observation has led into inquiries on the nature of volition. Preparatory neural activity is known to be associated with decision making and movement planning. Self-initiated locomotion has been linked to increased active sensory sampling; however, the precise temporal relationship between sensory acquisition and voluntary movement initiation has not been established. Based on long-term monitoring of sensory sampling activity that is readily observable in freely behaving pulse-type electric fish, we show that heightened sensory acquisition precedes spontaneous initiation of swimming. Gymnotus sp. revealed a bimodal distribution of electric organ discharge rate (EODR) demonstrating down- and up-states of sensory sampling and neural activity; movements only occurred during up-states and up-states were initiated before movement onset. EODR during voluntary swimming initiation exhibited greater trial-to-trial variability than the sound-evoked increases in EODR. The sampling variability declined after voluntary movement onset as previously observed for the neural variability associated with decision making in primates. Spontaneous movements occurred randomly without a characteristic timescale, and no significant temporal correlation was found between successive movement intervals. Using statistical analyses of spontaneous exploratory behaviours and associated preparatory sensory sampling increase, we conclude that electric fish exhibit key attributes of volitional movements, and that voluntary behaviours in vertebrates may generally be preceded by increased sensory sampling. Our results suggest that comparative studies of the neural basis of volition may therefore be possible in pulse-type electric fish, given the substantial homologies between the telencephali of teleost fish and mammals.

Real-Time Localization of Moving Dipole Sources for Tracking Multiple Free-Swimming Weakly Electric Fish

In order to survive, animals must quickly and accurately locate prey, predators, and conspecifics using the signals they generate. The signal source location can be estimated using multiple detectors and the inverse relationship between the received signal intensity (RSI) and the distance, but difficulty of the source localization increases if there is an additional dependence on the orientation of a signal source. In such cases, the signal source could be approximated as an ideal dipole for simplification. Based on a theoretical model, the RSI can be directly predicted from a known dipole location; but estimating a dipole location from RSIs has no direct analytical solution. Here, we propose an efficient solution to the dipole localization problem by using a lookup table (LUT) to store RSIs predicted by our theoretically derived dipole model at many possible dipole positions and orientations. For a given set of RSIs measured at multiple detectors, our algorithm found a dipole location having the closest matching normalized RSIs from the LUT, and further refined the location at higher resolution. Studying the natural behavior of weakly electric fish (WEF) requires efficiently computing their location and the temporal pattern of their electric signals over extended periods. Our dipole localization method was successfully applied to track single or multiple freely swimming WEF in shallow water in real-time, as each fish could be closely approximated by an ideal current dipole in two dimensions. Our optimized search algorithm found the animal’s positions, orientations, and tail-bending angles quickly and accurately under various conditions, without the need for calibrating individual-specific parameters. Our dipole localization method is directly applicable to studying the role of active sensing during spatial navigation, or social interactions between multiple WEF. Furthermore, our method could be extended to other application areas involving dipole source localization.

Enhanced attention precedes self-initiated locomotion in an electric fish

Volition is generally considered as a defining human faculty; but the outcome of a voluntary decision can be predicted by brain activity even before subject’s conscious awareness [1], and similar phenomena are observed in many species. Preparatory neural activities for voluntary movements involve movement planning and decision making [2], and active movements accompany heightened attention [3]. We show that enhanced attention precedes self-initiated movements in an animal model that exhibits a readily observable and quantifiable sensory acquisition rate. In addition to demonstrating preparatory increases in sensory sampling, our results reveal close similarities between the sensory sampling and the neural activity suggest associated with voluntary decision making [4]. Cortical activity precedes self-initiated movements by several seconds in mammals; this observation has led into inquiries on the nature of volition [1]. Preparatory neural activity is known to be associated with decision making and movement planning [2]. Self-initiated locomotion has been linked to active sensing indicative of enhanced attention [3]; however, the precise temporal relationship between sensory acquisition and voluntary movement initiation has not been established. Based on long-term monitoring of sensory sampling activity that is readily observable in freely behaving pulse-type electric fish, we show that heightened sensory acquisition precedes spontaneous initiation of swimming. Gymnotus sp. revealed a bimodal distribution of electric organ discharge rate (EODR) demonstrating Down- and Up-states of sensory sampling and neural activity; movements only occurred during Up-states and Up-states were initiated before movement-onset. EODR during voluntary swimming initiation exhibited greater trial-to-trial variability than the sound-evoked increases in EODR. The sensory sampling variability increased before, and declined after voluntary movement onset as previously observed for the neural variability associated with decision-making in primates [4]. In contrast, stimulus onset quenched the sampling variability similar to that previously reported in neural variability [5]. Spontaneous movements occurred randomly without a characteristic timescale, and no significant temporal correlation was found between successive movement intervals.

Nonstationary Stochastic Dynamics Underlie Spontaneous Transitions between Active and Inactive Behavioral States

Abstract The neural basis of spontaneous movement generation is a fascinating open question. Long-term monitoring of fish, swimming freely in a constant sensory environment, has revealed a sequence of behavioral states that alternate randomly and spontaneously between periods of activity and inactivity. We show that key dynamical features of this sequence are captured by a 1-D diffusion process evolving in a nonlinear double well energy landscape, in which a slow variable modulates the relative depth of the wells. This combination of stochasticity, nonlinearity, and nonstationary forcing correctly captures the vastly different timescales of fluctuations observed in the data (∼1 to ∼1000 s), and yields long-tailed residence time distributions (RTDs) also consistent with the data. In fact, our model provides a simple mechanism for the emergence of long-tailed distributions in spontaneous animal behavior. We interpret the stochastic variable of this dynamical model as a decision-like variable that, upon reaching a threshold, triggers the transition between states. Our main finding is thus the identification of a threshold crossing process as the mechanism governing spontaneous movement initiation and termination, and to infer the presence of underlying nonstationary agents. Another important outcome of our work is a dimensionality reduction scheme that allows similar segments of data to be grouped together. This is done by first extracting geometrical features in the dataset and then applying principal component analysis over the feature space. Our study is novel in its ability to model nonstationary behavioral data over a wide range of timescales.

A phenomenological model for self-initiated movement in electric fish

Observing behaviourally unconstrained animals can lead to simple characterization of complex behaviour. We apply this principle to infer the neural dynamics of self-initiated movement in pulse-type electric fish, Gymnotus sp. Recent long-term monitoring of fish (~200 hours from 22 recording sessions, over 4 animals) in freely swimming conditions, devoid of external stimuli, reveals non-trivial structures in their pattern of electric organ discharge (EOD). Simultaneous recording of EODs and fish movement show that the EOD rate (EODR) and the activity level of the fish are bi-modally distributed, as well as highly correlated. These features thus effectively define behavioural attractor states corresponding to high and low levels of neural activity (up- and down-states, respectively). Trajectories in the EODR-activity plane consist of diffusional motion around the attractor states, interrupted by sharp transitions between states. The duration of each state is uncorrelated with that of the next up- or down-state, and is log-normally distributed with no characteristic time-scale. Based on this data, our goal is to develop a modelling framework to better understand the neural pathway responsible for self-initiated movement. However, because the physiological parameters defining this pathway are experimentally unconstrained, it would be premature at this point to develop a detailed biophysical model of this system. There is thus a preliminary need to, instead, characterize the key features of the data from a phenomenological perspective. To address this research gap, we attempt to fit a stochastic process, with the simplest combination of dynamical components, that most closely reproduces the statistics of the data. As a first step, we hypothesize that the first principal component of the data (EODR and activity level) follows an overdamped Brownian motion in a double-well potential with additive noise. Based on the approach of [1], we fit a 4th order polynomial for the potential function by associating the stationary solution of the Fokker-Planck equation with the measured histogram. For most recording sessions, the fits appropriately reproduce the histograms, but consistently underestimates the width of the up-state potential well. Once the potential function is determined, we generate an estimate for the noise intensity by calculating the mean escape time from small regions at the bottom of either well, and comparing it with its theoretical expression. We find, however, that this estimate is dependent on the well that was used to generate it, with all recording sessions showing a larger noise estimate for the up-state than for the down-state. Moreover, in most cases, the ratio of up- to down-state duration is underestimated by the fitted process, indicative of either too short up-states, or too long down-states compared to the data. These findings are consistent with the above discrepancy for the width of the up-state potential well, as well as with visual inspection of the data, which shows greater variability when fish are in up-states. Taken together, these observations strongly suggest that state-dependent noise is involved in the process generating the data, either in the form of multiplicative noise, or Poisson shot noise.

Electrical localization of weakly electric fish using neural networks

Weakly Electric Fish (WEF) emit an Electric Organ Discharge (EOD), which travels through the surrounding water and enables WEF to locate nearby objects or to communicate between individuals. Previous tracking of WEF has been conducted using infrared (IR) cameras and subsequent image processing. The limitation of visual tracking is its relatively low frame-rate and lack of reliability when visually obstructed. Thus, there is a need for reliable monitoring of WEF location and behaviour. The objective of this study is to provide an alternative and non-invasive means of tracking WEF in real-time using neural networks (NN). This study was carried out in three stages. First stage was to recreate voltage distributions by simulating the WEF using EIDORS and finite element method (FEM) modelling. Second stage was to validate the model using phantom data acquired from an Electrical Impedance Tomography (EIT) based system, including a phantom fish and tank. In the third stage, the measurement data was acquired using a restrained WEF within a tank. We trained the NN based on the voltage distributions for different locations of the WEF. With networks trained on the acquired data, we tracked new locations of the WEF and observed the movement patterns. The results showed a strong correlation between expected and calculated values of WEF position in one dimension, yielding a high spatial resolution within 1 cm and 10 times higher temporal resolution than IR cameras. Thus, the developed approach could be used as a practical method to non-invasively monitor the WEF in real-time.