Effect of neuroleptics of the phenothiazine series on firing rate and ultrastructure of rabbit cortical neurons

1975 ◽  
Vol 79 (4) ◽  
pp. 414-417
Author(s):  
A. V. Nemtsov ◽  
G. R. Dubinskaya
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Phenothiazine Series

scholarly journals Sparse bursts optimize information transmission in a multiplexed neural code

2018 ◽  
Vol 115 (27) ◽  
pp. E6329-E6338 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Spike Timing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

Possible Effects of Depolarizing GABAA Conductance on the Neuronal Input–Output Relationship: A Modeling Study

2005 ◽  
Vol 93 (6) ◽  
pp. 3504-3523 ◽  
Author(s):  
Kenji Morita ◽  
Kunichika Tsumoto ◽  
Kazuyuki Aihara
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Reversal Potential ◽  
Pyramidal Cells ◽  
Resting Potential ◽  
Input Output ◽  
Wide Range ◽  
The Relationship

Recent in vitro experiments revealed that the GABAA reversal potential is about 10 mV higher than the resting potential in mature mammalian neocortical pyramidal cells; thus GABAergic inputs could have facilitatory, rather than inhibitory, effects on action potential generation under certain conditions. However, how the relationship between excitatory input conductances and the output firing rate is modulated by such depolarizing GABAergic inputs under in vivo circumstances has not yet been understood. We examine herewith the input–output relationship in a simple conductance-based model of cortical neurons with the depolarized GABAA reversal potential, and show that a tonic depolarizing GABAergic conductance up to a certain amount does not change the relationship between a tonic glutamatergic driving conductance and the output firing rate, whereas a higher GABAergic conductance prevents spike generation. When the tonic glutamatergic and GABAergic conductances are replaced by in vivo–like highly fluctuating inputs, on the other hand, the effect of depolarizing GABAergic inputs on the input–output relationship critically depends on the degree of coincidence between glutamatergic input events and GABAergic ones. Although a wide range of depolarizing GABAergic inputs hardly changes the firing rate of a neuron driven by noncoincident glutamatergic inputs, a certain range of these inputs considerably decreases the firing rate if a large number of driving glutamatergic inputs are coincident with them. These results raise the possibility that the depolarized GABAA reversal potential is not a paradoxical mystery, but is instead a sophisticated device for discriminative firing rate modulation.

Neuronal responses in cat primary auditory cortex to electrical cochlear stimulation. II. Repetition rate coding

1996 ◽  
Vol 75 (3) ◽  
pp. 1283-1300 ◽  
Author(s):  
C. E. Schreiner ◽  
M. W. Raggio
Keyword(s):  
Electrical Stimulation ◽  
Firing Rate ◽  
Repetition Rate ◽  
Cortical Neurons ◽  
Primary Auditory Cortex ◽  
Acoustic Stimulation ◽  
Electrode Spacing ◽  
Bipolar Electrode ◽  
Cochlear Prosthesis ◽  
Cutoff Frequencies

1. Responses of neurons in primary auditory cortex (AI) of the barbiturate-anesthetized adult cat were studied using cochlear stimulation with electrical and acoustic stimuli. Neuronal responses to acoustic stimulation with brief biphasic clicks of the ear ipsilateral to the studied cortical hemisphere were compared with those evoked by electrical stimulation of the contralateral cochlea with brief biphasic electrical pulses delivered via a feline cochlear prosthesis. The contralateral ear was deafened immediately before implantation of the cochlear prosthesis. The feline cochlear prosthesis consisted of four bipolar electrode pairs and was placed in the scala tympani. Two bipolar electrode conditions were used for stimulation: one near radial pair with electrode spacing of 0.25-0.5 mm, and one longitudinal pair with electrode spacing of approximately 6 mm. 2. The firing rates obtained from single- and multiple-neuron recordings were measured as a function of stimulus repetition rate of electrical and acoustic pulses. From period histograms over a recording interval of 1,000 ms, the driven firing rate to repetition rates from 2 to 38 Hz was obtained and repetition rate transfer functions (RRTFs) were constructed. The RRTFs were characterized as low-pass or band-pass filters and several descriptors were obtained, such as the repetition rate producing the highest driven activity, high and low cutoff frequencies 6 dB below maximum firing rate, and maximum firing rate. 3. For a given neuron, the main characteristics of cortical RRTFs obtained with electrical and acoustic cochlear stimulation were quite similar. However, some small but statistically significant differences in the best repetition rate, cutoff frequencies, and maximum firing rate could be observed between the different stimulation modes. The proportion of band-pass RRTFs was larger for electrical stimulation (57%) than for acoustic stimulation (41%). The high cutoff frequencies for electrical stimulation were slightly but consistently higher than for acoustic RRTFs of the same neuron and the maximum firing rate for electrical stimulation was significantly higher than that evoked by ipsilateral acoustic stimulation. 4. The entrainment of cortical neurons to electrical and acoustic pulses was determined and entrainment profiles were constructed. For a given neuron, electrical entrainment profiles showed higher cutoff frequencies than with acoustic stimulation when judged with a fixed entrainment criterion of 0.25 spikes per event. The maximum entrainment seen for electrical stimulation was approximately 20% higher than seen for the same neuron with acoustic stimulation. 5. Correlation analysis of repetition coding and latency parameters revealed several relationships between these response aspects. Most prominent among them was a significant correlation between measures of the response latency and estimates of the ability to follow temporal repetitions for acoustic as well as electrical conditions. 6. Parametric and comparative evaluations of cortical responses to acoustic and electrical cochlear stimulation support the conclusion that the temporal resolution seen in cortical neurons is largely a consequence of central processing mechanisms based on cell and circuit properties and to a lesser degree a consequence of particular spatial and temporal peripheral excitation patterns. The slightly higher temporal resolution found for the electrical stimulation modes suggests that the temporally highly coherent electrical stimulation appears to engage, in a more effective manner, the excitatory/inhibitory mechanisms contributing to the response in AI than acoustic click stimulation with less temporal coherence. (ABSTRACT TRUNCATED)

scholarly journals Neuronal firing rates diverge during REM and homogenize during non-REM

2016 ◽  
Author(s):  
Hiroyuki Miyawaki ◽  
Brendon Watson ◽  
Kamran Diba
Keyword(s):  
Firing Rate ◽  
Frontal Cortex ◽  
Rem Sleep ◽  
Cortical Neurons ◽  
Nrem Sleep ◽  
Regression To The Mean ◽  
Firing Rates ◽  
The Mean ◽  
High Firing ◽  
Interneuron Activity

AbstractNeurons fire at highly variable innate rates and recent evidence suggests that low and high firing rate neurons display different plasticity and dynamics. Furthermore, recent publications imply possibly differing rate-dependent effects in hippocampus versus neocortex, but those analyses were carried out separately and with possibly important differences. To more effectively synthesize these questions, we analyzed the firing rate dynamics of populations of neurons in both hippocampal CA1 and frontal cortex under one framework that avoids pitfalls of previous analyses and accounts for regression-to-the-mean. We observed remarkably consistent effects across these regions. While rapid eye movement (REM) sleep was marked by decreased hippocampal firing and increased neocortical firing, in both regions firing rates distributions widened during REM due to differential changes in high-firing versus low-firing cells in parallel with increased interneuron activity. In contrast, upon non-REM (NREM) sleep, firing rate distributions narrowed while interneuron firing decreased. Interestingly, hippocampal interneuron activity closely followed the patterns observed in neocortical principal cells rather than the hippocampal principal cells, suggestive of long-range interactions. Following these undulations in variance, the net effect of sleep was a decrease in firing rates. These decreases were greater in lower-firing hippocampal neurons but higher-firing frontal cortical neurons, suggestive of greater plasticity in these cell groups. Our results across two different regions and with statistical corrections indicate that the hippocampus and neocortex show a mixture of differences and similarities as they cycle between sleep states with a unifying characteristic of homogenization of firing during NREM and diversification during REM.Significance StatementMiyawaki and colleagues analyze firing patterns across low-firing and high-firing neurons in the hippocampus and the frontal cortex throughout sleep in a framework that accounts for regression-to-the-mean. They find that in both regions REM sleep activity is relatively dominated by high-firing neurons and increased inhibition, resulting in a wider distribution of firing rates. On the other hand, NREM sleep produces lower inhibition, and results in a more homogenous distribution of firing rates. Integration of these changes across sleep results in net decrease of firing rates with largest drops in low-firing hippocampal pyramidal neurons and high-firing neocortical principal neurons. These findings provide insights into the effects and functions of different sleep stages on cortical neurons.

Responses of Primate Visual Cortical V4 Neurons to Simultaneously Presented Stimuli

2002 ◽  
Vol 88 (3) ◽  
pp. 1128-1135 ◽  
Author(s):  
Timothy J. Gawne ◽  
Julie M. Martin
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Firing Rate ◽  
Cortical Neurons ◽  
Clear Relationship ◽  
Substantial Fraction ◽  
Receptive Field Properties ◽  
Direct Support ◽  
V4 Neurons ◽  
Visual Cortical

We report here results from 45 primate V4 visual cortical neurons to the preattentive presentations of seven different patterns located in two separate areas of the same receptive field and to combinations of the patterns in the two locations. For many neurons, we could not determine any clear relationship for the responses to two simultaneous stimuli. However, for a substantial fraction of the neurons we found that the firing rate was well modeled as the maximum firing rate of each stimulus presented separately. It has previously been proposed that taking the maximum of the inputs (“MAX” operator) could be a useful operation for neurons in visual cortex, although there has until now been little direct physiological evidence for this hypothesis. Our results here provide direct support for the hypothesis that the MAX operator plays a significant (although certainly not exclusive) role in generating the receptive field properties of visual cortical neurons.

Dynamic Spatiotemporal Synaptic Integration in Cortical Neurons: Neuronal Gain, Revisited

2005 ◽  
Vol 94 (4) ◽  
pp. 2785-2796 ◽  
Author(s):  
Rony Azouz
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Temporal Integration ◽  
Synaptic Integration ◽  
Neuronal Membrane ◽  
Network Dynamic ◽  
Alternative Mechanism ◽  
Gain Modulation ◽  
Voltage Gated ◽  
The Relationship

Gain modulation is a ubiquitous phenomenon in cortical neurons, providing flexibility to operate under changing conditions. The prevailing view is that this modulation reflects a change in the relationship between mean input and output firing rate brought about by variation in neuronal membrane characteristics. An alternative mechanism is proposed for neuronal gain modulation that takes into account the capability of cortical neurons to process spatiotemporal synaptic correlations. Through the use of numerical simulations, it is shown that voltage-gated and leak conductances, membrane potential, noise, and input firing rate modify the sensitivity of cortical neurons to the degree of temporal correlation between their synaptic inputs. These changes are expressed in a change of the temporal window for synaptic integration and the range of input correlation over which response probability is graded. The study also demonstrates that temporal integration depends on the distance between the inputs and that this interplay of space and time is modulated by voltage-gated and leak conductances. Thus, gain modulation may reflect a change in the relationship between spatiotemporal synaptic correlations and output firing probability. It is further proposed that by acting synergistically with the network, dynamic spatiotemporal synaptic integration in cortical neurons may serve a functional role in the formation of dynamic cell assemblies.

Computational Studies of Gain Modification by Serotonin in Pyramidal Neurons of Prefrontal Coxtex

2006 ◽  
Author(s):  
Guoshi Li ◽  
Harvey C. Cline ◽  
Pierre Blier ◽  
Satish Nair
Keyword(s):  
Prefrontal Cortex ◽  
Firing Rate ◽  
Cortical Neurons ◽  
Pyramidal Neurons ◽  
Input Resistance ◽  
Square Root ◽  
Gain Modulation ◽  
Cellular Mechanisms ◽  
Brain Functions ◽  
A Current

Serotonin (5-HT) is widely implicated in brain functions and diseases, but the cellular mechanisms underlying 5-HT functions in the brain are not well understood (Zhang and Arsenault, 2005). Recent experiments (Zhang and Arsenault, 2005) have shown that 5-HT substantially increased the slope (gain) of the firing rate current (F-I) curve in layer 5 pyramidal neurons of the rat prefrontal cortex and this effect was limited to the range of firing rate (0-10 Hz) that is known to behaviourally relevant. Furthermore, it was found that 5-HT mediated gain increase was due to a reduction of the afterhyperpolarization (AHP) and an induction of the slow afterdepolarization (ADP), regardless of changes in the membrane potential, the input resistance or the properties of action potentials. To investigate this frequency-dependent gain modulation of 5-HT on the prefrontal cortex neurons, conductance-based Hodgkin-Huxley type models of the regular spiking (RS) cells in the prefrontal cortex are developed using a step by step approach. We first show that a model with an A current displays a square-root form F-I curve with higher slope at low frequency. However, for the same range of current injection steps used in experiment, the frequency range goes beyond 20 Hz, suggesting the presence of other hyperpolarizing currents in the model. As suggested by the experiment (Zhang and Arsenault, 2005), AHP currents (fast AHP, medium AHP and slow AHP) are included in the model to simulate 5-HT effect. Simulations show that AHP currents effectively linearize the F-I curve and decrease the slope of F-I curve in general, thus reducing the neuronal excitability. Since the slow AHP current is a target of 5-HT, the strength of this current is reduced gradually and the F-I curves are plotted together for comparison. The results indicate that with decreasing slow AHP strength, the current thresholds for repetitive spiking decreases and the slopes of the F-I curves increase in general. A square-root form F-I curve is not evident until the slow AHP current is blocked completely. This suggests that the medium AHP current also play a role in linearizing the F-I curve besides the slow AHP current. Based on current findings, a full model with both A current and AHP currents is being constructed to match the experimental data more closely so the mechanism of 5-HT on gain modulation of prefrontal cortical neurons can be better understood.

Estimating Network Parameters From Combined Dynamics of Firing Rate and Irregularity of Single Neurons

2011 ◽  
Vol 105 (1) ◽  
pp. 487-500 ◽  
Author(s):  
Kosuke Hamaguchi ◽  
Alexa Riehle ◽  
Nicolas Brunel
Keyword(s):  
Firing Rate ◽  
Network Structure ◽  
Cortical Neurons ◽  
Optimal Parameter ◽  
Inhibitory Neurons ◽  
Local Network ◽  
Excitatory Postsynaptic Potential ◽  
Dimensional Manifold ◽  
Firing Irregularity ◽  
Recurrent Connections

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV2) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV2 is widely distributed from quasi-regular to irregular (CV2 = 0.3–1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV2 neurons to move to the excitation-dominated region as well as to an increase of EPSP size.

scholarly journals Separating Spike Count Correlation from Firing Rate Correlation

2016 ◽  
Vol 28 (5) ◽  
pp. 849-881 ◽  
Author(s):  
Giuseppe Vinci ◽  
Valérie Ventura ◽  
Matthew A. Smith ◽  
Robert E. Kass
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Experimental Manipulation ◽  
Response Variability ◽  
Spike Count ◽  
Firing Rates ◽  
Area V4 ◽  
Rate Correlation ◽  
Over Time

Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4 in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC.

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