scholarly journals Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

2015 ◽  
Author(s):  
Ajith Sahasranamam ◽  
Ioannis Vlachos ◽  
Ad Aertsen ◽  
Arvind Kumar
Keyword(s):  
Single Neuron ◽  
Network Activity ◽  
Population Activity ◽  
Firing Patterns ◽  
Bursting Neuron ◽  
State Dependent ◽  
Network Sensitivity ◽  
Activity State ◽  
Operating Points ◽  
Spike Patterns

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

scholarly journals Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

2021 ◽  
Author(s):  
Shreya Saxena ◽  
Abigail A. Russo ◽  
John P. Cunningham ◽  
Mark M. Churchland
Keyword(s):  
Motor Cortex ◽  
Muscle Activity ◽  
Single Neuron ◽  
Activity Patterns ◽  
Network Activity ◽  
Movement Speed ◽  
Neural Population ◽  
Recurrent Networks ◽  
Neural Signals ◽  
Population Activity

AbstractLearned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling, and yielded quantitative and qualitative predictions. To evaluate predictions, we recorded motor cortex population activity during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

scholarly journals Refractoriness accounts for variable spike burst responses in somatosensory cortex

2017 ◽  
Author(s):  
Bartosz Teleńczuk ◽  
Richard Kempter ◽  
Gabriel Curio ◽  
Alain Destexhe
Keyword(s):  
Somatosensory Cortex ◽  
Cortical Neurons ◽  
Single Neuron ◽  
Primary Somatosensory Cortex ◽  
General Mechanism ◽  
Population Activity ◽  
Synaptic Inputs ◽  
Neuron Spike ◽  
Biophysical Mechanism ◽  
Spike Patterns

AbstractNeurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronised bursts of spikes, which lock to the macroscopic 600 Hz EEG waves. The mechanism of burst generation and synchronisation in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronised bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with non-trivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).Significance statementNeurons in the hand area of the primary somatosensory cortex respond to repeated presentation of the same stimulus with variable sequences of spikes, which can be grouped into distinct temporal spike patterns. In a simplified model, we show that such spike patterns are product of synaptic inputs and intrinsic neural properties. This model can reproduce both single-neuron and population responses only when a private variability in each neuron is combined with a multiplicative gain shared over whole population, which fluctuates over trials and might represent the dynamical state of the early stages of sensory processing. This phenomenon exemplifies a general mechanism of transforming the ensemble cortical states into precise temporal spike patterns at the level of single neurons.

scholarly journals Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hamidreza Abbaspourazad ◽  
Mahdi Choudhury ◽  
Yan T. Wong ◽  
Bijan Pesaran ◽  
Maryam M. Shanechi
Keyword(s):  
Neural Control ◽  
Multiple Scales ◽  
Network Activity ◽  
Neural Dynamics ◽  
Population Activity ◽  
Reach And Grasp ◽  
Spatiotemporal Scales ◽  
Motor Cortical ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

AbstractMotor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode’s decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

Level of Arousal During the Small Irregular Activity State in the Rat Hippocampal EEG

2004 ◽  
Vol 91 (6) ◽  
pp. 2649-2657 ◽  
Author(s):  
Beata Jarosiewicz ◽  
William E. Skaggs
Keyword(s):  
Slow Wave Sleep ◽  
Power Spectra ◽  
Pyramidal Cells ◽  
Auditory Stimuli ◽  
Sleep State ◽  
Population Activity ◽  
Ca1 Pyramidal Cells ◽  
Emg Amplitude ◽  
Current Location ◽  
Activity State

The sleeping rat cycles between two well-characterized hippocampal physiological states, large irregular activity (LIA) during slow-wave sleep (SWS) and theta activity during rapid-eye-movement sleep (REM). A third, less well-characterized electroencephalographic (EEG) state, termed “small irregular activity” (SIA), has been reported to occur when an animal is startled out of sleep without moving and during active waking when it abruptly freezes. We recently found that the hippocampal population activity of a spontaneous sleep state whose EEG resembles SIA reflects the rat's current location in space, suggesting that it is also a state of heightened arousal. To test whether this spontaneous SIA state corresponds to the SIA state reported in the literature and to compare the level of arousal during SIA to the other well-characterized physiological states, we recorded unit activity from ensembles of hippocampal CA1 pyramidal cells, EEG from the hippocampus and the neocortex, and electromyography (EMG) from the dorsal neck musculature in rats presented with auditory stimuli while foraging for randomly scattered food pellets and while sleeping. Auditory stimuli presented during sleep reliably induced SIA episodes very similar to spontaneous SIA in hippocampal and neocortical EEG amplitudes and power spectra, EMG amplitude, and CA1 population activity. Both spontaneous and elicited SIA exhibited neocortical desynchronization, and both had EMG amplitude comparable to that of waking LIA. We conclude based on this and other evidence that spontaneous SIA and elicited SIA correspond to a single state and that the level of arousal in SIA is higher than in the well-characterized sleep states but lower than the active theta state.

scholarly journals Differential Effects of Open- and Closed-Loop Intracortical Microstimulation on Firing Patterns of Neurons in Distant Cortical Areas

2019 ◽  
Vol 30 (5) ◽  
pp. 2879-2896 ◽  
Author(s):  
Alberto Averna ◽  
Valentina Pasquale ◽  
Maxwell D Murphy ◽  
Maria Piera Rogantin ◽  
Gustaf M Van Acker ◽  
...  
Keyword(s):  
Closed Loop ◽  
Activity Patterns ◽  
Brain Lesion ◽  
Progressive Increase ◽  
Intracortical Microstimulation ◽  
Behavioral Recovery ◽  
Firing Patterns ◽  
Electrical Microstimulation ◽  
Spike Patterns ◽  
Activity Dependent

Abstract Intracortical microstimulation can be used successfully to modulate neuronal activity. Activity-dependent stimulation (ADS), in which action potentials recorded extracellularly from a single neuron are used to trigger stimulation at another cortical location (closed-loop), is an effective treatment for behavioral recovery after brain lesion, but the related neurophysiological changes are still not clear. Here, we investigated the ability of ADS and random stimulation (RS) to alter firing patterns of distant cortical locations. We recorded 591 neuronal units from 23 Long-Evan healthy anesthetized rats. Stimulation was delivered to either forelimb or barrel field somatosensory cortex, using either RS or ADS triggered from spikes recorded in the rostral forelimb area (RFA). Both RS and ADS stimulation protocols rapidly altered spike firing within RFA compared with no stimulation. We observed increase in firing rates and change of spike patterns. ADS was more effective than RS in increasing evoked spikes during the stimulation periods, by producing a reliable, progressive increase in stimulus-related activity over time and an increased coupling of the trigger channel with the network. These results are critical for understanding the efficacy of closed-loop electrical microstimulation protocols in altering activity patterns in interconnected brain networks, thus modulating cortical state and functional connectivity.

scholarly journals Kiss1 Neurons Drastically Change Their Firing Activity in Accordance With the Reproductive State: Insights From a Seasonal Breeder

Endocrinology ◽  
2014 ◽  
Vol 155 (12) ◽  
pp. 4868-4880 ◽  
Author(s):  
Masaharu Hasebe ◽  
Shinji Kanda ◽  
Hiroyuki Shimada ◽  
Yasuhisa Akazome ◽  
Hideki Abe ◽  
...  
Keyword(s):  
Fluorescent Protein ◽  
Resting Membrane Potential ◽  
Reproductive State ◽  
Firing Patterns ◽  
State Dependent ◽  
Channel Expression ◽  
Physiological Analysis ◽  
Seasonal Breeder ◽  
Green Fluorescent

Kisspeptin (Kiss) neurons show drastic changes in kisspeptin expression in response to the serum sex steroid concentration in various vertebrate species. Thus, according to the reproductive states, kisspeptin neurons are suggested to modulate various neuronal activities, including the regulation of GnRH neurons in mammals. However, despite their reproductive state-dependent regulation, there is no physiological analysis of kisspeptin neurons in seasonal breeders. Here we generated the first kiss1-enhanced green fluorescent protein transgenic line of a seasonal breeder, medaka, for histological and electrophysiological analyses using a whole-brain in vitro preparation in which most synaptic connections are intact. We found histologically that Kiss1 neurons in the nucleus ventralis tuberis (NVT) projected to the preoptic area, hypothalamus, pituitary, and ventral telencephalon. Therefore, NVT Kiss1 neurons may regulate various homeostatic functions and innate behaviors. Electrophysiological analyses revealed that they show various firing patterns, including bursting. Furthermore, we found that their firings are regulated by the resting membrane potential. However, bursting was not induced from the other firing patterns with a current injection, suggesting that it requires some chronic modulations of intrinsic properties such as channel expression. Finally, we found that NVT Kiss1 neurons drastically change their neuronal activities according to the reproductive state and the estradiol levels. Taken together with the previous reports, we here conclude that the breeding condition drastically alters the Kiss1 neuron activities in both gene expression and firing activities, the latter of which is strongly related to Kiss1 release, and the Kiss1 peptides regulate the activities of various neural circuits through their axonal projections.

scholarly journals Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons

2016 ◽  
Vol 115 (1) ◽  
pp. 457-469 ◽  
Author(s):  
Mahmood S. Hoseini ◽  
Ralf Wessel
Keyword(s):  
Single Neuron ◽  
Experimental Studies ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Peak Frequency ◽  
Spiking Neurons ◽  
Network Activity ◽  
Cortical Circuits ◽  
Model Network ◽  
Low Rate

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits.

scholarly journals Inference of a Mesoscopic Population Model from Population Spike Trains

2020 ◽  
Vol 32 (8) ◽  
pp. 1448-1498 ◽  
Author(s):  
Alexandre René ◽  
André Longtin ◽  
Jakob H. Macke
Keyword(s):  
Population Model ◽  
Single Neuron ◽  
Simulated Data ◽  
Population Data ◽  
Model Parameters ◽  
Population Activity ◽  
Mcmc Sampling ◽  
Wide Range ◽  
Low Dimensional ◽  
Aggregate Population

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

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