scholarly journals Stretching and squeezing of neuronal log firing rate distribution by psychedelic and intrinsic brain state transitions

2021 ◽  
Author(s):  
Bradley Dearnley ◽  
Martynas Dervinis ◽  
Melissa Shaw ◽  
Michael Okun
Keyword(s):  
Neuronal Population ◽  
Neuronal Firing ◽  
State Transitions ◽  
Brain State ◽  
Rate Distribution ◽  
Firing Rates ◽  
Neuronal Populations ◽  
Psychedelic Drugs ◽  
Large Populations ◽  
Intrinsic Fluctuations

AbstractHow psychedelic drugs change the activity of cortical neuronal populations and whether such changes are specific to transition into the psychedelic brain state or shared with other brain state transitions is not well understood. Here, we used Neuropixels probes to record from large populations of neurons in prefrontal cortex of mice given the psychedelic drug TCB-2. Drug ingestion significantly stretched the distribution of log firing rates of the population of recorded neurons. This phenomenon was previously observed across transitions between sleep and wakefulness, which suggested that stretching of the log-rate distribution can be triggered by different kinds of brain state transitions and prompted us to examine it in more detail. We found that modulation of the width of the log-rate distribution of a neuronal population occurred in multiple areas of the cortex and in the hippocampus even in awake drug-free mice, driven by intrinsic fluctuations in their arousal level. Arousal, however, did not explain the stretching of the log-rate distribution by TCB-2. In both psychedelic and naturally occurring brain state transitions, the stretching or squeezing of the log-rate distribution of an entire neuronal population reflected concomitant changes in two subpopulations, with one subpopulation undergoing a downregulation and often also stretching of its neurons’ log-rate distribution, while the other subpopulation undergoes upregulation and often also a squeeze of its log-rate distribution. In both subpopulations, the stretching and squeezing were a signature of a greater relative impact of the brain state transition on the rates of the slow-firing neurons. These findings reveal a generic pattern of reorganisation of neuronal firing rates by different kinds of brain state transitions.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

scholarly journals What, if anything, is the true neurophysiological significance of “rotational dynamics”?

2019 ◽  
Author(s):  
Mikhail A. Lebedev ◽  
Alexei Ossadtchi ◽  
Nil Adell Mill ◽  
Núria Armengol Urpí ◽  
Maria R. Cervera ◽  
...  
Keyword(s):  
Neuronal Population ◽  
Rotational Dynamics ◽  
Neuronal Responses ◽  
Linear Transformations ◽  
Experimental Conditions ◽  
Temporal Shift ◽  
Firing Rates ◽  
Motor Behaviors ◽  
Neuronal Populations ◽  
Neuronal Mechanisms

AbstractBack in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal shift in peak firing rates exhibited by individual neurons, and (2) the temporal sequence of peak rates remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s exaggerated assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses.” Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

scholarly journals In vivo Interictal Neuronal Signatures of Human Periventricular Nodular Heterotopia

2019 ◽  
Author(s):  
Valerio Frazzini ◽  
Stephen Whitmarsh ◽  
Katia Lehongre ◽  
Pierre Yger ◽  
Jean-Didier Lemarechal ◽  
...  
Keyword(s):  
Epileptic Activity ◽  
Neuronal Firing ◽  
Periventricular Nodular Heterotopia ◽  
Firing Rates ◽  
Neuronal Populations ◽  
Malformation Of Cortical Development ◽  
Nodular Heterotopia ◽  
Epileptic Spikes ◽  
Drug Resistant Epilepsy

AbstractPeriventricular nodular heterotopia (PNH) is a common type of malformation of cortical development and a cause of drug-resistant epilepsy. In contrast with other cortical malformations, no consistent interictal patterns have yet been identified in PNH, and it remains controversial whether epileptic activity originates within nodules. The current study addresses the heterogeneity in LFP signatures, as well as the question of epileptogenicity of nodules, by means of microelectrode recordings implanted within PNH tissues in two epileptic patients. Microelectrodes also allowed the first in vivo recording of heterotopic neurons in humans, permitting the investigation of neuronal firing rates during patterns of interictal activity. Highly consistent interictal patterns (IPs) were identified within PNH: 1) trains of periodic slow waves and 2) isolated slow deflections, both with superimposed fast activity, and 3) epileptic spikes. Neuron firing rates were significantly modulated during all IPs, suggesting that different IPs were generated by the same local neuronal populations. To conclude, this study presents the first in vivo microscopic description of local PNH microcircuits in humans and their organization into multiple epileptic neurophysiological patterns, providing a first pathognomonic signature of human PNH.

scholarly journals Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mikhail A. Lebedev ◽  
Alexei Ossadtchi ◽  
Nil Adell Mill ◽  
Núria Armengol Urpí ◽  
Maria R. Cervera ◽  
...  
Keyword(s):  
Neuronal Population ◽  
Neuronal Responses ◽  
Linear Transformations ◽  
Experimental Conditions ◽  
Firing Rates ◽  
Motor Behaviors ◽  
Neuronal Ensembles ◽  
Neuronal Populations ◽  
Activity Modeling ◽  
Neuronal Mechanisms

AbstractBack in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal sequence in peak firing rates exhibited by individual neurons, and (2) this sequence remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s bold assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses”. Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

scholarly journals The Effect of Correlated Variability on the Accuracy of a Population Code

1999 ◽  
Vol 11 (1) ◽  
pp. 91-101 ◽  
Author(s):  
L. F. Abbott ◽  
Peter Dayan
Keyword(s):  
Firing Rate ◽  
Neuronal Firing ◽  
Population Code ◽  
Firing Rates ◽  
Large Populations ◽  
Neuronal Firing Rate ◽  
General Limit ◽  
Widespread Belief ◽  
The Impact

We study the impact of correlated neuronal firing rate variability on the accuracy with which an encoded quantity can be extracted from a population of neurons. Contrary to widespread belief, correlations in the variabilities of neuronal firing rates do not, in general, limit the increase in coding accuracy provided by using large populations of encoding neurons. Furthermore, in some cases, but not all, correlations improve the accuracy of a population code.

scholarly journals Sleep regulation of the distribution of cortical firing rates

2016 ◽  
Author(s):  
Daniel Levenstein ◽  
Brendon O. Watson ◽  
John Rinzel ◽  
György Buzsáki
Keyword(s):  
Firing Rate ◽  
Neuronal Firing ◽  
Skewed Distribution ◽  
Slow Oscillation ◽  
Brain State ◽  
Neuronal System ◽  
Sleep Regulation ◽  
Firing Rates ◽  
Memory Traces ◽  
Intrinsic Firing

ABSTRACTSleep is thought to mediate mnemonic and homeostatic functions. However, the mechanism by which this brain state can implement both the “selective” plasticity needed to consolidate novel memory traces as well as the “general” plasticity necessary to maintain a well-functioning neuronal system is unclear. Recent findings show that both of these functions differentially affect neurons based on their intrinsic firing rate, a ubiquitous neuronal heterogeneity. Furthermore, they are both implemented by the NREM slow oscillation, which also distinguishes neurons based on firing rate during sequential activity at the DOWN->UP transition. These findings suggest a mechanism by which spiking activity during the slow oscillation acts to maintain network statistics that promote a skewed distribution of neuronal firing rates, and “perturbation” of that activity by hippocampal replay acts to integrate new memory traces into the existing cortical network.

Anesthesia-Induced State Transitions in Neuronal Populations

2009 ◽  
pp. 139-160
Author(s):  
Jamie Sleigh ◽  
Moira Steyn-Ross ◽  
Alistair Steyn-Ross ◽  
Logan Voss ◽  
Marcus Wilson
Keyword(s):  
State Transitions ◽  
Neuronal Populations

scholarly journals Gene expression analysis in the mouse brainstem identifies Cart and Nesfatin as neuropeptides coexpressed in the Calbindin-positive neurons of the Nucleus papilio

SLEEP ◽  
2020 ◽  
Vol 43 (11) ◽  
Author(s):  
Franck Girard ◽  
Michelle von Siebenthal ◽  
Fred P Davis ◽  
Marco R Celio
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Neuronal Population ◽  
Brain Atlas ◽  
Rem Sleep Deprivation ◽  
Neuronal Populations ◽  
Hybridization Data ◽  
Brainstem Nucleus ◽  
Genes Encoding

Abstract Study Objectives: The brainstem contains several neuronal populations, heterogeneous in terms of neurotransmitter/neuropeptide content, which are important for controlling various aspects of the rapid eye movement (REM) phase of sleep. Among these populations are the Calbindin (Calb)-immunoreactive NPCalb neurons, located in the Nucleus papilio, within the dorsal paragigantocellular nucleus (DPGi), and recently shown to control eye movement during the REM phase of sleep. Methods: We performed in-depth data mining of the in situ hybridization data collected at the Allen Brain Atlas, in order to identify potentially interesting genes expressed in this brainstem nucleus. Our attention focused on genes encoding neuropeptides, including Cart (Cocaine and Amphetamine Regulated Transcripts) and Nesfatin 1. Results: While nesfatin 1 appeared ubiquitously expressed in this Calb-positive neuronal population, Cart was coexpressed in only a subset of these glutamatergic NPCalb neurons. Furthermore, an REM sleep deprivation and rebound assay performed with mice revealed that the Cart-positive neuronal population within the DPGi was activated during REM sleep (as measured by c-fos immunoreactivity), suggesting a role of this neuropeptide in regulating some aspects of REM sleep. Conclusions: The assembled information could afford functional clues to investigators, conducive to further experimental pursuits.

scholarly journals Neuronal spike-rate adaptation supports working memory in language processing

2020 ◽  
Vol 117 (34) ◽  
pp. 20881-20889 ◽  
Author(s):  
Hartmut Fitz ◽  
Marvin Uhlmann ◽  
Dick van den Broek ◽  
Renato Duarte ◽  
Peter Hagoort ◽  
...  
Keyword(s):  
Working Memory ◽  
Language Processing ◽  
Neuronal Excitability ◽  
Memory Span ◽  
Temporal Integration ◽  
Serial Order ◽  
Neuronal Firing ◽  
Dynamic Memory ◽  
Memory Mechanism ◽  
Firing Rates

Language processing involves the ability to store and integrate pieces of information in working memory over short periods of time. According to the dominant view, information is maintained through sustained, elevated neural activity. Other work has argued that short-term synaptic facilitation can serve as a substrate of memory. Here we propose an account where memory is supported by intrinsic plasticity that downregulates neuronal firing rates. Single neuron responses are dependent on experience, and we show through simulations that these adaptive changes in excitability provide memory on timescales ranging from milliseconds to seconds. On this account, spiking activity writes information into coupled dynamic variables that control adaptation and move at slower timescales than the membrane potential. From these variables, information is continuously read back into the active membrane state for processing. This neuronal memory mechanism does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive conductances that reduce firing rates and can be accessed directly without cued retrieval. Memory span is systematically related to both the time constant of adaptation and baseline levels of neuronal excitability. Interference effects within memory arise when adaptation is long lasting. We demonstrate that this mechanism is sensitive to context and serial order which makes it suitable for temporal integration in sequence processing within the language domain. We also show that it enables the binding of linguistic features over time within dynamic memory registers. This work provides a step toward a computational neurobiology of language.

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