Variability in somatosensory cortical neuron discharge: effects on capacity to signal different stimulus conditions using a mean rate code

1978 ◽  
Vol 41 (2) ◽  
pp. 338-349 ◽  
Author(s):  
R. C. Schreiner ◽  
G. K. Essick ◽  
B. L. Whitsel
Keyword(s):  
Firing Rate ◽  
Linear Equation ◽  
Cortical Neuron ◽  
Single Neuron ◽  
Rate Code ◽  
Firing Rates ◽  
Discrimination Criterion ◽  
The Mean ◽  
Change In Mean ◽  
The Relationship

1. The present study is based on the demonstration (8, 9) that the relationship between mean interval (MI) and standard deviation (SD) for stimulus-driven activity recorded from SI neurons is well fitted by the linear equation SD = a X MI + b and on the observations that the values of the slope (a) and y intercept (b) parameters of this relationship are independent of stimulus conditions and may vary widely from one neuron to the next (8). 2. A criterion for the discriminability of two different mean firing rates requiring that the mean intervals of their respective interspike interval (ISI) distributions be separated by a fixed interval (expressed in SD units) is developed and, on the basis of this criterion, a graphical display of the capacity of a neuron with a known SD-MI relationship to reflect a change in stimulus conditions with a change in mean firing rate is derived. Using this graphical approach, it is shown that the parameters of the SD-MI relationship for a single neuron determine a range of firing frequencies, within which that neuron exhibits the greatest capacity to signal differences in stimulus conditions using a frequency code. 3. The discrimination criterion is modified to incorporate the changes in the symmetry of the ISI distribution observed to accompany changes in mean firing rate. It is shown that, although the observed symmetry changes do influence the capacity of a cortical neuron to signal a change in stimulus conditions with a change in mean firing rate, they do not alter the range of firing rates (determined by the parameters of the SD-MI relationship) within which the capacity for discrimination is maximal. 4. The maximal number of firing levels that can be distinguished by a somatosensory cortical neuron (using the same discrimination criterion described above) discharging within a specified range of mean frequencies also is demonstrated to depend on the parameters of the linear equation which relates SD to MI. 5. Two approaches based on the t test for differences between two means are developed in an attempt to ascertain the minimum separation of the mean intervals of the ISI distributions necessary for two different mean firing rates to be discriminated with 80% certainty.

Activation of type-identified motor units during centrally evoked contractions in the cat medial gastrocnemius muscle. II. Motoneuron firing-rate modulation

1996 ◽  
Vol 75 (1) ◽  
pp. 38-50 ◽  
Author(s):  
K. E. Tansey ◽  
B. R. Botterman
Keyword(s):  
Firing Rate ◽  
Motor Unit ◽  
Muscle Force ◽  
Motor Units ◽  
Medial Gastrocnemius ◽  
Firing Rates ◽  
Rate Modulation ◽  
The Mean ◽  
Fast Twitch ◽  
The Relationship

1. The aim of this study was to examine the nature of motoneuron firing-rate modulation in type-identified motor units during smoothly graded contractions of the cat medial gastrocnemius (MG) muscle evoked by stimulation of the mesencephalic locomotor region (MLR). Motoneuron discharge patterns, firing rates, and the extent of firing-rate modulation in individual units were studied, as was the extent of concomitant changes in firing rates within pairs of simultaneously active units. 2. In 21 pairs of simultaneously active motor units, studied during 41 evoked contractions, the motoneurons' discharge rates and patterns were measured by processing the cells' recorded action potentials through windowing devices and storing their timing in computer memory. Once recruited, most motoneurons increased their firing rates over a limited range of increasing muscle tension and then maintained a fairly constant firing rate as muscle force continued to rise. Most motoneurons also decreased their firing rates over a slightly larger, but still limited, range of declining muscle force before they were derecruited. Although this was the most common discharge pattern recorded, several other interesting patterns were also seen. 3. The mean firing rate for slow twitch (type S) motor units (27.8 imp/s, 5,092 activations) was found to be significantly different from the mean firing rate for fast twitch (type F) motor units (48.4 imp/s, 11,272 activations; Student's t-test, P < 0.001). There was no significant difference between the mean firing rates of fast twitch, fatigue-resistant (type FR) and fast twitch, fatigable (type FF) motor units. When the relationship between motoneuron firing rate and whole-muscle force was analyzed, it was noted that, in general, smaller, lower threshold motor units began firing at lower rates and reached lower peak firing rates than did larger, higher threshold motor units. These results confirm both earlier experimental observations and predictions made by other investigators on the basis of computer simulations of the cat MG motor pool, but are in contrast to motor-unit discharge behavior recorded in some human motor-unit studies. 4. The extent of concomitant changes in firing rate within pairs of simultaneously active motor units was examined to estimate the extent of simultaneous motoneuron firing-rate modulation across the motoneuron pool. A smoothed (5 point sliding average) version of the two motoneurons' instantaneous firing rates was plotted against each other, and the slope and statistical significance of the relationship was determined. In 16 motor-unit pairs, the slope of the motoneurons' firing-rate relationship was significantly distinct from 0. Parallel firing-rate modulation (< 10-fold difference in firing rate change reflected by a slope of > 0.1) was noted only in pairs containing motor units of like physiological type and then only if they were of similar recruitment threshold. 5. Other investigators have demonstrated that changes in a motoneuron's "steady-state" firing rate predictably reflect changes in the amount of effective synaptic current that cell is receiving. The finding in the present study of limited parallel firing-rate modulation between simultaneously active motoneurons would suggest that changes in the synaptic drive to the various motoneurons of the pool is unevenly distributed. This finding, in addition to the findings of orderly motor-unit recruitment and the relationship between motor-unit recruitment threshold and motoneuron firing rate, cannot be adequately accommodated for by the existing models of the synaptic organization in motoneuron pools. Therefore a new model of the synaptic organization within the motoneuron pool has been proposed.

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.

scholarly journals An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co-firing rates of neural spike trains

2019 ◽  
Author(s):  
Ryan Grgurich ◽  
Hugh T. Blair
Keyword(s):  
Firing Rate ◽  
Uncertainty Principle ◽  
Neural Coding ◽  
Spike Trains ◽  
Grid Cells ◽  
Rate Code ◽  
Biologically Inspired ◽  
Firing Rates ◽  
Neural Populations ◽  
Similar Information

AbstractThe hippocampal system contains neural populations that encode an animal’s position and velocity as it navigates through space. Here, we show that such populations can embed two codes within their spike trains: a firing rate code (R) conveyed by within-cell spike intervals, and a co-firing rate code (Ṙ) conveyed by between-cell spike intervals. These two codes behave as conjugates of one another, obeying an analog of the uncertainty principle from physics: information conveyed in R comes at the expense of information in Ṙ, and vice versa. An exception to this trade-off occurs when spike trains encode a pair of conjugate variables, such as position and velocity, which do not compete for capacity across R and Ṙ. To illustrate this, we describe two biologically inspired methods for decoding R and Ṙ, referred to as sigma and sigma-chi decoding, respectively. Simulations of head direction (HD) and grid cells show that if firing rates are tuned for position (but not velocity), then position is recovered by sigma decoding, whereas velocity is recovered by sigma-chi decoding. Conversely, simulations of oscillatory interference among theta-modulated “speed cells” show that if co-firing rates are tuned for position (but not velocity), then position is recovered by sigma-chi decoding, whereas velocity is recovered by sigma decoding. Between these two extremes, information about both variables can be distributed across both channels, and partially recovered by both decoders. These results suggest that neurons with different spatial and temporal tuning properties—such as speed versus grid cells—might not encode different information, but rather, distribute similar information about position and velocity in different ways across R and Ṙ. Such conjugate coding of position and velocity may influence how hippocampal populations are interconnected to form functional circuits, and how biological neurons integrate their inputs to decode information from firing rates and spike correlations.

Transmembrane potentials and atrial fibrillation

1960 ◽  
Vol 199 (2) ◽  
pp. 346-348 ◽  
Author(s):  
Richard L. Klein ◽  
William C. Holland
Keyword(s):  
Atrial Fibrillation ◽  
Ion Transport ◽  
Firing Rate ◽  
Electrical Excitation ◽  
Firing Rates ◽  
Transmembrane Potentials ◽  
Atrial Cells ◽  
Microelectrode Technique ◽  
The Mean

The mean maximum following frequency of single atrial cells as determined by the microelectrode technique was found to be 820/min. with a range of 600–1050. The mean ‘firing’ rate of atrial cells during acetylcholine induced fibrillation was 900/min. with a range of 600–1250/min. The data are interpreted to mean that the observed changes in ion transport during fibrillation are not the result of higher firing rates of atrial cells as compared to those during rapid electrical excitation.

UNIT ACTIVITY IN THE PARAVENTRICULAR NUCLEUS OF FEMALE RATS AT DIFFERENT STAGES OF THE REPRODUCTIVE CYCLE AND AFTER OVARIECTOMY, WITH OR WITHOUT OESTROGEN OR PROGESTERONE TREATMENT

1973 ◽  
Vol 59 (3) ◽  
pp. 545-558 ◽  
Author(s):  
H. NEGORO ◽  
S. VISESSUWAN ◽  
R. C. HOLLAND
Keyword(s):  
Firing Rate ◽  
Paraventricular Nucleus ◽  
Reproductive Cycle ◽  
Refractory Period ◽  
Full Term ◽  
Ovariectomized Rats ◽  
Oestrogen Treatment ◽  
Absolute Refractory Period ◽  
Firing Rates ◽  
The Mean

SUMMARY Spontaneous firing rates were determined from extracellular recordings made from 878 antidromically identified units in the paraventricular nucleus (PVN) during the reproductive cycle of the female rat and in analytical experiments. In the latter, rats were ovariectomized and subsequently received either no treatment or oestrogen and/or progesterone. Among rats at metoestrus, dioestrus, mid-pregnancy and in ovariectomized progesterone-treated groups there was no significant difference in the firing rates. However, they were significantly lower than the rates recorded during pro-oestrus, oestrus, full-term pregnancy, the day of parturition, during lactation and in ovariectomized, oestrogen-treated rats. In spayed rats the mean firing rate was significantly lower than at pro-oestrus, oestrus, fullterm pregnancy, the 24 h period after parturition, during lactation and after oestrogen treatment. When progesterone was given subcutaneously to oestrogenized rats, the PVN activity, increased by oestrogen, was significantly depressed 4 h after administration. By 8 h the firing rate had completely recovered. The frequency distribution of the firing rates in pro-oestrus and oestrus showed an approximately normal distribution while those in metoestrus and dioestrus and mid-pregnancy had a Poisson distribution. At full term there were two peaks: one in the range of 3–5 spikes/s and the other less than one spike/s. The distribution was approximately normal on the day of parturition and subsequently the pattern became irregular. In ovariectomized rats and those treated with progesterone it was of a Poisson type while there was a distinct shift to higher frequencies after oestrogen treatment. The mean absolute refractory period, measured for each unit, varied and appears to be dependent on hormonal conditions. It was short in oestrus and long in dioestrus and mid-pregnancy. Oestrogen treatment significantly shortened the absolute refractory period of ovariectomized rats.

scholarly journals The Relationship between the Firing Rate of a Single Neuron and the Level of Activity in a Population of Neurons

1972 ◽  
Vol 59 (6) ◽  
pp. 767-778 ◽  
Author(s):  
Bruce W. Knight
Keyword(s):  
Theoretical Prediction ◽  
Firing Rate ◽  
Single Neuron ◽  
Quantitative Comparison ◽  
Resonant Response ◽  
Neuron Population ◽  
Instantaneous Rate ◽  
Level Of Activity ◽  
The Relationship

A quantitative comparison is made between experiment and the theoretically predicted dynamics of a neuron population. The experiment confirms the theoretical prediction that under appropriate conditions an enlarged resonant response should appear in the activity of the neuron population, near the frequency at which there is minimum modulation in the instantaneous rate of a single neuron. These findings bear on the relationship between the firing rate of a single neuron and the firing rate of a population of neurons.

41 DETERMINATION OF THE RELATIONSHIP BETWEEN VULVAR SKIN TEMPERATURES AND TIME OF OVULATION IN SWINE USING DIGITAL INFRARED THERMOGRAPHY

2010 ◽  
Vol 22 (1) ◽  
pp. 178 ◽  
Author(s):  
S. Scolari ◽  
R. Evans ◽  
R. Knox ◽  
M. Tamassia ◽  
S. Clark
Keyword(s):  
Infrared Thermography ◽  
Marked Change ◽  
Control Group ◽  
Thermal Imager ◽  
Promising Tool ◽  
Significant Difference ◽  
Estrus Detection ◽  
The Mean ◽  
Change In Mean ◽  
The Relationship

Accurate estrus detection is an essential component of a successful AI program in modern swine operations. It is necessary to establish efficacious means of estrus detection and optimize reproductive performance in the herd. Measurement of physiological traits such as body temperature, vaginal electrical resistance, and vulva reddening have been investigated as methods to aid in estrus detection in swine. The relationship between vulvar skin temperature (VST) and ovulation has not been previously investigated. Therefore, the objective of this study was to assess changes in VST that occur during the periovulatory period using digital infrared thermography (IRT). The experiment group consisted of a total of 25 gilts and 27 multiparous sows, and the control group consisted of 30 sows that were 60 days of gestation. All Yorkshire-Landrace females were housed individually in a temperature and humidity controlled environment. VST were measured twice daily at 8-h intervals using the infrared digital thermocamera (Fluke IR FlexCam® Thermal Imager, Fluke Corporation, Everett, WA) while the animals were standing and eating prior to estrus detection. Estrus detection was performed twice daily (at 8-h intervals) with the aid of an adult boar. Once standing estrus was observed, transrectal real-time ultrasound was performed twice daily at 8-h intervals to monitor follicle development and determine the time of ovulation. Ovaries were visualized using an Aloka 500 V ultrasonics machine (Aloka Inc., Tokyo, Japan) fitted with a transrectal 7.5-MHz linear transducer, which was fitted into a rigid, fixed-angle PVC adapter. Average VST and hours were reported in mean ± SEM and compared using an ANOVA and Student’s t-test using SAS software (SAS Institute Inc., Cary, NC, USA). Additionally, pairwise comparisons were performed to compare VST at different times during estrus. Significant differences were reported at P ≤ 0.05. Evidence of CL formation and ovulation was detected at 38 ± 9.3 h after onset of estrus in gilts, and 43 ± 12 h in sows. The mean VST of sows during estrus was significantly higher (P ≤ 0.05) than that of gilts. During estrus, the mean VST of gilts reached a peak of 35.6 ± 0.24°C and then decreased significantly to 33.9 ± 0.32°C 12 h prior to ovulation. This marked change in mean VST was detected between 36 and 12 h prior to ovulation. There was a similar trend in sows with a peak mean VST of 36.1 ± 0.25°C at 24 h prior to ovulation and then dropping to 34.6 ± 0.31°C 12 h prior to ovulation. There was no significant difference (P ≥ 0.05) between VST in gilts and sows at the time of ovulation. This study demonstrated that VST of sows and gilts measured by IRT change significantly during the periovulatory period. Additionally, there are distinct times that VST rises and then falls precipitously in sows compared with gilts. Digital IRT as a predictor for ovulation in swine appears to be a promising tool. Further studies involving predictor models and hormonal assays need to be performed.

How Optimal Stimuli for Sensory Neurons Are Constrained by Network Architecture

2008 ◽  
Vol 20 (3) ◽  
pp. 668-708 ◽  
Author(s):  
Christopher DiMattina ◽  
Kechen Zhang
Keyword(s):  
Firing Rate ◽  
Network Architecture ◽  
Single Neuron ◽  
Network Models ◽  
Synaptic Connections ◽  
Trial And Error ◽  
Neural Network Models ◽  
Stimulus Response ◽  
The Relationship ◽  
Topological Boundary

Identifying the optimal stimuli for a sensory neuron is often a difficult process involving trial and error. By analyzing the relationship between stimuli and responses in feedforward and stable recurrent neural network models, we find that the stimulus yielding the maximum firing rate response always lies on the topological boundary of the collection of all allowable stimuli, provided that individual neurons have increasing input-output relations or gain functions and that the synaptic connections are convergent between layers with nondegenerate weight matrices. This result suggests that in neurophysiological experiments under these conditions, only stimuli on the boundary need to be tested in order to maximize the response, thereby potentially reducing the number of trials needed for finding the most effective stimuli. Even when the gain functions allow firing rate cutoff or saturation, a peak still cannot exist in the stimulus-response relation in the sense that moving away from the optimum stimulus always reduces the response. We further demonstrate that the condition for nondegenerate synaptic connections also implies that proper stimuli can independently perturb the activities of all neurons in the same layer. One example of this type of manipulation is changing the activity of a single neuron in a given processing layer while keeping that of all others constant. Such stimulus perturbations might help experimentally isolate the interactions of selected neurons within a network.

Rostral ventrolateral medullary and caudal medullary raphe neurons with activity correlated to the 10-Hz rhythm in sympathetic nerve discharge

1992 ◽  
Vol 68 (5) ◽  
pp. 1535-1547 ◽  
Author(s):  
S. M. Barman ◽  
G. L. Gebber
Keyword(s):  
Slow Wave ◽  
Sympathetic Nerve ◽  
Frequency Domain Analysis ◽  
Firing Rates ◽  
Medullary Raphe ◽  
The Mean ◽  
The Relationship ◽  
Medullary Neurons ◽  
Raphe Neurons ◽  
Nerve Discharge

1. The current study is the first to identify medullary neurons whose naturally occurring discharges were correlated to the 10-Hz rhythm in sympathetic nerve discharge (SND). Spike-triggered averaging showed that 44 of 164 rostral ventrolateral medullary (RVLM) and 44 of 174 caudal medullary raphe neurons had activity correlated to the 10-Hz rhythm in inferior cardiac postganglionic SND of 23 baroreceptor-denervated, decerebrate cats. 2. When the frequency of the rhythm in SND was decreased by lowering body temperature, the discharges of the 10 neurons tested (6 RVLM and 4 raphe) remained locked to the peak of the next 10-Hz sympathetic nerve slow wave rather than to the peak of the preceding slow wave. This observation supports the contention that the 10-Hz rhythm in basal SND was generated in the brain stem rather than in the spinal cord. 3. Frequency-domain analysis was used to characterize further the relationship between the 10-Hz rhythm in SND and the discharges of 30 RVLM and 24 raphe neurons. The autospectra of the discharges of eight RVLM and four raphe neurons contained a sharp peak near 10 Hz, although the mean firing rates of these neurons were lower than the frequency of the rhythm in SND. Coherence values as high as 0.76 characterized the relationship between the discharges of these "rhythmically firing neurons" and the 10-Hz rhythm in SND. A coherence value of 1.0 indicates a perfect correlation. The autospectra of the discharges of the 22 RVLM and 20 raphe neurons did not contain a peak near 10 Hz. The mean firing rates and coherence values relating the discharges of these "nonrhythmically firing neurons" and the 10-Hz rhythm in SND were significantly lower than those for the rhythmically firing neurons. Because the frequency of the population rhythm recorded from the inferior cardiac nerve was higher than the firing rates of individual medullary neurons, the 10-Hz rhythm in SND appears to be an emergent property of a network of neurons whose discharges are probabilistically related to the population rhythm. 4. In addition to the peak near 10-Hz, the autospectrum of SND often contained considerable power at frequencies < 6 Hz. This component of SND is called the 2- to 6-Hz rhythm.(ABSTRACT TRUNCATED AT 400 WORDS)

Rate Limitations of Unitary Event Analysis

2000 ◽  
Vol 12 (9) ◽  
pp. 2063-2082 ◽  
Author(s):  
A. Roy ◽  
P. N. Steinmetz ◽  
E. Niebur
Keyword(s):  
Firing Rate ◽  
Random Variable ◽  
Event Analysis ◽  
Neural Firing ◽  
Time Intervals ◽  
Relative Variation ◽  
Firing Rates ◽  
Inhomogeneous Poisson Processes ◽  
The Mean ◽  
Unitary Events

Unitary event analysis is a new method for detecting episodes of synchronized neural activity (Riehle, Grüun, Diesmann, & Aertsen, 1997). It detects time intervals that contain coincident firing at higher rates than would be expected if the neurons fired as independent inhomogeneous Poisson processes; all coincidences in such intervals are called unitary events (UEs). Changes in the frequency of UEs that are correlated with behavioral states may indicate synchronization of neural firing that mediates or represents the behavioral state. We show that UE analysis is subject to severe limitations due to the underlying discrete statistics of the number of coincident events. These limitations are particularly stringent for low (0–10 spikes/s) firing rates. Under these conditions, the frequency of UEs is a random variable with a large variation relative to its mean. The relative variation decreases with increasing firing rate, and we compute the lowest firing rate, at which the 95% confidence interval around the mean frequency of UEs excludes zero. This random variation in UE frequency makes interpretation of changes in UEs problematic for neurons with low firing rates. As a typical example, when analyzing 150 trials of an experiment using an averaging window 100 ms wide and a 5ms coincidence window, firing rates should be greater than 7 spikes per second.

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