scholarly journals Local field potentials in a pre-motor region predict learned vocal sequences

2021 ◽  
Vol 17 (9) ◽  
pp. e1008100
Author(s):  
Daril E. Brown ◽  
Jairo I. Chavez ◽  
Derek H. Nguyen ◽  
Adam Kadwory ◽  
Bradley Voytek ◽  
...  
Keyword(s):  
Local Field ◽  
Local Field Potentials ◽  
Vocal Behavior ◽  
Field Potentials ◽  
Population Activity ◽  
Time Frequency ◽  
Complex Motor ◽  
History Of ◽  
Song Production ◽  
Motor Region

Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity detail patterns of sequential bursting in small, carefully identified subsets of neurons in the HVC population. The dynamics of HVC are well described by these characterizations, but have not been verified beyond this scale of measurement. There is a rich history of using local field potentials (LFP) to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time, and they have been used to study and decode human speech and other complex motor behaviors. Here we characterize LFP signals presumptively from the HVC of freely behaving male zebra finches during song production to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time-varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates the utility of LFP for studying vocal behavior in songbirds. Surprisingly, the time frequency structure of HVC LFP is qualitatively similar to well-established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight into common computational principles for learning and/or generating complex motor-vocal behaviors.

scholarly journals Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences

2020 ◽  
Author(s):  
Daril E. Brown ◽  
Jairo I. Chavez ◽  
Derek H. Nguyen ◽  
Adam Kadwory ◽  
Bradley Voytek ◽  
...  
Keyword(s):  
Motor Control ◽  
Local Field ◽  
Local Field Potentials ◽  
Brain Regions ◽  
Network Activity ◽  
Field Potentials ◽  
Population Activity ◽  
Time Frequency ◽  
Useful Signal ◽  
Complex Motor

AbstractNeuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity typically focuses on patterns of sequential bursting in small carefully identified subsets of single neurons in the HVC population. Much less is known about population dynamics beyond the scale of individual neurons. There is a rich history of using local field potentials (LFP), to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time and have been used to study and decode complex motor behaviors, such as human speech. Here we characterize LFP signals in the putative HVC of freely behaving male zebra finches during song production, to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates that LFP is a useful signal for studying motor control in songbirds. Surprisingly, the time frequency structure of putative HVC LFP is qualitatively similar to well established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight to common computational principles for learning and/or generating complex motor-vocal behaviors.Author SummaryVocalizations, such as speech and song, are a motor process that requires the coordination of several muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing and it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity in putative HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Given the similarities of the songbird and human motor-vocal systems these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

scholarly journals Evidence of Intermittency in the Local Field Potentials Recorded from Patients with Parkinson's Disease: A Wavelet-Based Approach

2007 ◽  
Vol 8 (3) ◽  
pp. 165-171 ◽  
Author(s):  
Asok K. Sen ◽  
Jonathan O. Dostrovsky
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Local Field ◽  
Local Field Potentials ◽  
Temporal Variations ◽  
Beta Activity ◽  
Field Potentials ◽  
Time Frequency ◽  
Intermittent Behavior ◽  
Complex Interactions

Using a continuous wavelet transform we have detected the presence of intermittency in the beta oscillations of the local field potentials (LFPs) that were recorded from the subthalamic nucleus (STN) of patients with Parkinson's disease. The intermittent behavior was identified by plotting the wavelet power spectrum of the LFP signal on a time–frequency plane. We also computed the temporal variations of scale-averaged wavelet power and wavelet entropy (WE). An intermittent pattern is characterized by large amounts of power over very short periods of time separated by almost quiescent periods. Time-localized changes in WE further support the evidence of intermittency. The cause and significance of the intermittent beta activity are presently unclear. It may be due to complex interactions of the cortico-basal-ganglia networks converging at the STN level.

scholarly journals Inferring entire spiking activity from local field potentials with deep learning

2020 ◽  
Author(s):  
Nur Ahmadi ◽  
Timothy G. Constandinou ◽  
Christos-Savvas Bouganis
Keyword(s):  
Deep Learning ◽  
Local Field ◽  
Signal To Noise Ratio ◽  
Local Field Potentials ◽  
Neuronal Population ◽  
Field Potentials ◽  
Population Activity ◽  
Neuronal Coding ◽  
Automated Technique ◽  
Spiking Activity

ABSTRACTExtracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Understanding the relationship between these two signals is essential for gaining deeper insight into neuronal coding and information processing in the brain and is also relevant to brain-machine interface (BMI) research. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. These spiking activities that are typically extracted via threshold-based technique may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another spiking activity in the form of a continuous signal, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by employing a deep learning method to infer ESA from LFPs intracortically recorded from the motor cortex area of two monkeys performing different tasks. Results from long-term recording sessions and across different tasks revealed that the inference accuracy of ESA yielded consistently and significantly higher accuracy than that of SUA and MUA. In addition, local motor potential (LMP) was found to be the most highly predictive feature compared to other LFP features. The overall results indicate that LFPs contain substantial information about the spikes, particularly ESA, which could be useful for the development of LFP-based BMIs. The results also suggest the potential use of ESA as an alternative neuronal population activity measure for analysing neural responses to stimuli or behavioural tasks.

Population registration of echo flow in the big brown bat’s auditory midbrain

2021 ◽  
Vol 126 (4) ◽  
pp. 1314-1325
Author(s):  
Michaela Warnecke ◽  
James A. Simmons ◽  
Andrea Megela Simmons
Keyword(s):  
Local Field ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Auditory Midbrain ◽  
Population Activity ◽  
Cluttered Environments ◽  
Auditory Scene

Echolocating bats navigate through cluttered environments that return cascades of echoes in response to the bat’s broadcasts. We show that local field potentials from the big brown bat’s auditory midbrain have consistent responses to a simulated echo cascade varying across echo delays and stimulus amplitudes, despite different underlying individual neuronal selectivities. These results suggest that population activity in the midbrain can build a cohesive percept of an auditory scene by aggregating activity over neuronal subpopulations.

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