scholarly journals Fronto-striatal oscillations predict vocal output in bats

2019 ◽  
Author(s):  
Kristin Weineck ◽  
Francisco García-Rosales ◽  
Julio C. Hechavarría
Keyword(s):  
Large Scale ◽  
Phase Locking ◽  
Field Potential ◽  
Dorsal Striatum ◽  
Field Potentials ◽  
Action Prediction ◽  
Theta Band ◽  
Motor Action ◽  
Echolocation Calls ◽  
General Aspects

SummaryThe ability to vocalize is ubiquitous in vertebrates, but neural networks leading to vocalization production remain poorly understood. Here we performed simultaneous, large scale, neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus) during the production of echolocation and non-echolocation calls in bats. This approach allows to assess the general aspects underlying vocalization production in mammals and the unique evolutionary adaptations of bat echolocation. Our findings show that distinct intra-areal brain rhythms in the beta (12-30 Hz) and gamma (30-80 Hz) bands of the local field potential can be used to predict the bats’ vocal output and that phase locking between spikes and field potentials occurs prior vocalization production. Moreover, the fronto-striatal network is differentially coupled in the theta-band during the production of echolocation and non-echolocation calls. Overall, our results present evidence for fronto-striatal network oscillations in motor action prediction in mammals.

scholarly journals Oscillations of Local Field Potentials in the Rat Dorsal Striatum During Spontaneous and Instructed Behaviors

2007 ◽  
Vol 97 (5) ◽  
pp. 3800-3805 ◽  
Author(s):  
William E. DeCoteau ◽  
Catherine Thorn ◽  
Daniel J. Gibson ◽  
Richard Courtemanche ◽  
Partha Mitra ◽  
...  
Keyword(s):  
Local Field ◽  
Local Field Potentials ◽  
Dorsal Striatum ◽  
Oscillatory Activity ◽  
Field Potentials ◽  
Theta Band ◽  
Maze Task ◽  
Action Sequence ◽  
Frequency Coding ◽  
And Performance

Oscillatory activity is a candidate mechanism for providing frequency coding for the generation, storage and replay of sequential representations of events and episodes. We recorded local field potentials (LFPs) and spike activity in the striatum, a basal ganglia structure implicated in behavioral action-sequence learning and performance, as rats engaged in spontaneous and instructed behaviors in a T-maze task. We found that during voluntary behaviors, striatal LFPs exhibit prominent theta-band oscillations together with rhythms at higher and lower frequencies. Analysis of the theta-band activity demonstrated that these oscillations are strongly modulated during task performance and increase as the animals choose and execute their turning responses in the cue-instructed T-maze task. These theta rhythms are locally generated and are coherent across large parts of the striatum. We suggest that modulation of oscillatory activity in the striatum may be a key feature of neural processing related to the control of voluntary behavior.

scholarly journals Chronic, Multi-Site Recordings Supported by Two Low-Cost, Stationary Probe Designs Optimized to Capture Either Single Unit or Local Field Potential Activity in Behaving Rats

2021 ◽  
Vol 12 ◽  
Author(s):  
Miranda J. Francoeur ◽  
Tianzhi Tang ◽  
Leila Fakhraei ◽  
Xuanyu Wu ◽  
Sidharth Hulyalkar ◽  
...  
Keyword(s):  
Local Field ◽  
Single Unit ◽  
Large Scale ◽  
Brain Activity ◽  
Low Cost ◽  
Local Field Potentials ◽  
Field Potential ◽  
Brain Regions ◽  
Field Potentials ◽  
Brain Target

Rodent models of cognitive behavior have greatly contributed to our understanding of human neuropsychiatric disorders. However, to elucidate the neurobiological underpinnings of such disorders or impairments, animal models are more useful when paired with methods for measuring brain function in awake, behaving animals. Standard tools used for systems-neuroscience level investigations are not optimized for large-scale and high-throughput behavioral battery testing due to various factors including cost, time, poor longevity, and selective targeting limited to measuring only a few brain regions at a time. Here we describe two different “user-friendly” methods for building extracellular electrophysiological probes that can be used to measure either single units or local field potentials in rats performing cognitive tasks. Both probe designs leverage several readily available, yet affordable, commercial products to facilitate ease of production and offer maximum flexibility in terms of brain-target locations that can be scalable (32–64 channels) based on experimental needs. Our approach allows neural activity to be recorded simultaneously with behavior and compared between micro (single unit) and more macro (local field potentials) levels of brain activity in order to gain a better understanding of how local brain regions and their connected networks support cognitive functions in rats. We believe our novel probe designs make collecting electrophysiology data easier and will begin to fill the gap in knowledge between basic and clinical research.

scholarly journals Spatiotemporal patterns of rodent hippocampal field potentials uncover spatial representations

2019 ◽  
Author(s):  
Liang Cao ◽  
Viktor Varga ◽  
Zhe S. Chen
Keyword(s):  
Large Scale ◽  
Scale Up ◽  
Field Potential ◽  
Spatiotemporal Patterns ◽  
Spatial Representations ◽  
Learning Approaches ◽  
Field Potentials ◽  
Hippocampal Field ◽  
Low Dimensional ◽  
Array Recordings

AbstractSpatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze run, immobility and sleep. Here, we showed that multi-site hippocampal field potential amplitude at ultra-high frequency band (FPAuhf) provides not only a fast and reliable reconstruction of the rodent’s position in wake, but also a readout of replay content during sharp wave ripples. This FPAuhf feature may serve as robust real-time decoding strategy from large-scale (up to 100,000 electrodes) recordings in closed-loop experiments. Furthermore, we developed unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods, and to infer latent dynamical structures from lower-rank FPAuhf features. We also developed a novel optical flow-based method to identify propagating spatiotemporal LFP patterns from multi-site array recordings, which can be used for decoding application. Finally, we developed a prospective decoding strategy to predict animal’s future decision in goal-directed navigation.

scholarly journals Neuronal Correlates of Instrumental Learning in the Dorsal Striatum

2009 ◽  
Vol 102 (1) ◽  
pp. 475-489 ◽  
Author(s):  
Eyal Y. Kimchi ◽  
Mary M. Torregrossa ◽  
Jane R. Taylor ◽  
Mark Laubach
Keyword(s):  
Neuronal Activity ◽  
Lithium Chloride ◽  
Acoustic Stimulus ◽  
Instrumental Learning ◽  
Dorsal Striatum ◽  
Field Potentials ◽  
Theta Band ◽  
Sucrose Reward ◽  
Related Potentials ◽  
Operant Task

We recorded neuronal activity simultaneously in the medial and lateral regions of the dorsal striatum as rats learned an operant task. The task involved making head entries into a response port followed by movements to collect rewards at an adjacent reward port. The availability of sucrose reward was signaled by an acoustic stimulus. During training, animals showed increased rates of responding and came to move rapidly and selectively, following the stimulus, from the response port to the reward port. Behavioral “devaluation” studies, pairing sucrose with lithium chloride, established that entries into the response port were habitual (insensitive to devaluation of sucrose) from early in training and entries into the reward port remained goal-directed (sensitive to devaluation) throughout training. Learning-related changes in behavior were paralleled by changes in neuronal activity in the dorsal striatum, with an increasing number of neurons showing task-related firing over the training period. Throughout training, we observed more task-related neurons in the lateral striatum compared with those in the medial striatum. Many of these neurons fired at higher rates during initiation of movements in the presence of the stimulus, compared with similar movements in the absence of the stimulus. Learning was also accompanied by progressive increases in movement-related potentials and transiently increased theta-band oscillations (5–8 Hz) in simultaneously recorded field potentials. Together, these data suggest that representations of task-relevant stimuli and movements develop in the dorsal striatum during instrumental learning.

Dynamic coupling among neocortical neurons during evoked and spontaneous spike-wave seizure activity

1994 ◽  
Vol 72 (5) ◽  
pp. 2051-2069 ◽  
Author(s):  
M. Steriade ◽  
F. Amzica
Keyword(s):  
Cortical Neurons ◽  
Time Course ◽  
Early Stage ◽  
Field Potential ◽  
Time Lags ◽  
Temporal Relations ◽  
Intracellular Recordings ◽  
Field Potentials ◽  
Thalamic Nuclei ◽  
Spike Wave

1. We investigated the development from patterns of electroencephalogram (EEG) synchronization to paroxysms consisting of spike-wave (SW) complexes at 2–4 Hz or to seizures at higher frequencies (7–15 Hz). We used multisite, simultaneous EEG, extracellular, and intracellular recordings from various neocortical areas and thalamic nuclei of anesthetized cats. 2. The seizures were observed in 25% of experimental animals, all maintained under ketamine and xylazine anesthesia, and were either induced by thalamocortical volleys and photic stimulation or occurred spontaneously. Out of unit and field potential recordings within 370 cortical and 65 thalamic sites, paroxysmal events occurred in 70 cortical and 8 thalamic sites (approximately 18% and 12%, respectively), within which a total of 181 neurons (143 extracellular and 38 intracellular) were simultaneously recorded in various combinations of cell groups. 3. Stimulus-elicited and spontaneous SW seizures at 2–4 Hz lasted for 15–35 s and consisted of barrages of action potentials related to the spiky depth-negative (surface-positive) field potentials, followed by neuronal silence during the depth-positive wave component of SW complexes. The duration of inhibitory periods progressively increased during the seizure, at the expense of the phasic excitatory phases. 4. Intracellular recordings showed that, during such paroxysms, cortical neurons displayed a tonic depolarization (approximately 10–20 mV), sculptured by rhythmic hyperpolarizations. 5. In all cases, measures of synchrony demonstrated time lags between discharges of simultaneously recorded cortical neurons, from as short as 3–10 ms up to 50 ms or even longer intervals. Synchrony was assessed by cross-correlograms, by a method termed first-spike-analysis designed to detect dynamic temporal relations between neurons and relying on the detection of the first action potential in a spike train, and by a method termed sequential-field-correlation that analyzed the time course of field potentials simultaneously recorded from different cortical areas. 6. The degree of synchrony progressively increased from preseizure sleep patterns to the early stage of the SW seizure and, further, to its late stage. In some cases the time relation between neurons during the early stages of seizures was inversed during late stages. 7. These data show that, although the common definition of SW seizures, regarded as suddenly generalized and bilaterally synchronous activities, may be valid at the macroscopic EEG level, cortical neurons display time lags between their rhythmic spike trains, progressively increased synchrony, and changes in the temporal relations between their discharges during the paroxysms.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Functional Brain Connectivity Revealed by Sparse Coding of Large-Scale Local Field Potential Dynamics

2018 ◽  
Vol 32 (2) ◽  
pp. 255-270 ◽  
Author(s):  
Han Wang ◽  
Kun Xie ◽  
Li Xie ◽  
Xiang Li ◽  
Meng Li ◽  
...  
Keyword(s):  
Local Field ◽  
Sparse Coding ◽  
Local Field Potential ◽  
Large Scale ◽  
Brain Connectivity ◽  
Field Potential ◽  
Functional Brain

scholarly journals Dynamics of scalar potentials in theory of gravity

2019 ◽  
Vol 97 (8) ◽  
pp. 880-894
Author(s):  
M. Zubair ◽  
Farzana Kousar ◽  
Saira Waheed
Keyword(s):  
Scalar Field ◽  
Field Potential ◽  
Graphical Analysis ◽  
Chaplygin Gas ◽  
Field Potentials ◽  
De Sitter ◽  
Field Equations ◽  
Barotropic Fluid ◽  
First Case ◽  
De Sitter Universe

In this paper, we explore the nature of scalar field potential in [Formula: see text] gravity using a well-motivated reconstruction scheme for flat Friedmann–Robertson–Walker (FRW) geometry. The beauty of this scheme lies in the assumption that the Hubble parameter can be expressed in terms of scalar field and vice versa. Firstly, we develop field equations in this gravity and present some general explicit forms of scalar field potential via this technique. In the first case, we take the de Sitter universe model and construct some field potentials by taking different cases for the coupling function. In the second case, we derive some field potentials using the power law model in the presence of different matter sources like barotropic fluid, cosmological constant, and Chaplygin gas for some coupling functions. From graphical analysis, it is concluded that using some specific values of the involved parameters, the reconstructed scalar field potentials are cosmologically viable in both cases.

scholarly journals Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

2018 ◽  
Author(s):  
Zeinab Golgooni ◽  
Sara Mirsadeghi ◽  
Mahdieh Soleymani Baghshah ◽  
Pedram Ataee ◽  
Hossein Baharvand ◽  
...  
Keyword(s):  
Neural Network ◽  
Stem Cells ◽  
Deep Learning ◽  
Pluripotent Stem Cells ◽  
Field Potential ◽  
Input Sequence ◽  
Patient Specific ◽  
Field Potentials ◽  
Drug Induced

AbstractAimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.Methods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.ConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of “irregularity or abnormal waveforms” in an in vitro model of cardiotoxicity experiments.

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