Pharmacologic therapeutic window of pramiracetam demonstrated in behavior, EEG, and single neuron firing rates

1985 ◽  
Vol 41 (9) ◽  
pp. 1153-1156 ◽  
Author(s):  
B. P. H. Poschel ◽  
P. M. Ho ◽  
F. W. Ninteman ◽  
M. J. Callahan
Keyword(s):  
Single Neuron ◽  
Therapeutic Window ◽  
Neuron Firing ◽  
Firing Rates

Cortical population activity predicts both spontaneous and evoked single neuron firing rates

1997 ◽  
Vol 237 ◽  
pp. S27
Author(s):  
T. Kenet ◽  
A. Arieli ◽  
A. Grinvald ◽  
M. Tsodyks
Keyword(s):  
Single Neuron ◽  
Population Activity ◽  
Neuron Firing ◽  
Firing Rates

scholarly journals Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex

2017 ◽  
Vol 117 (4) ◽  
pp. 1524-1543 ◽  
Author(s):  
Michael E. Rule ◽  
Carlos E. Vargas-Irwin ◽  
John P. Donoghue ◽  
Wilson Truccolo
Keyword(s):  
Steady State ◽  
Motor Cortex ◽  
Local Field ◽  
Single Neuron ◽  
Field Potential ◽  
Movement Preparation ◽  
Phase Coupling ◽  
Firing Rates ◽  
Sources Of Variation ◽  
Transient Events

Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation. NEW & NOTEWORTHY We show that whereas motor cortex spiking rates and beta (~20 Hz) spiking rhythmicity remain sustained during steady-state movement preparation periods, β-local field potential (β-LFP) oscillations emerge, in contrast, as transient events. Furthermore, the β-LFP phase at which neurons spike drifts: phase coupling is typically weak or absent. This dissociation points to two sources of variation in the level of motor cortex beta: one that impacts single-neuron spiking and another related to the generation of measured mesoscopic β-LFPs.

Information coding in the rodent prefrontal cortex. II. Ensemble activity in orbitofrontal cortex

1995 ◽  
Vol 74 (2) ◽  
pp. 751-762 ◽  
Author(s):  
G. Schoenbaum ◽  
H. Eichenbaum
Keyword(s):  
Orbitofrontal Cortex ◽  
Single Neuron ◽  
Activity Space ◽  
Behavioral Performance ◽  
Single Neurons ◽  
Reward Contingency ◽  
Neuron Firing ◽  
Firing Patterns ◽  
Neural Ensembles ◽  
Odor Discrimination Task

1. Neural activity was recorded from the orbitofrontal cortex (OF) of rats performing an eight-odor discrimination task that included predictable associations between particular odor pairs. A modified linear discriminant analysis was employed to characterize the population response in each trial of the task as a point in an N-dimensional activity space with the firing rate of each cell in the population represented on one of the N dimensions. The ability of the ensemble to discriminate among conditions of a variable was reflected in the tendency of population responses to cluster together in this activity space for repetitions of a given condition. We assessed coding of several variables describing the period of odor sampling, focusing on aspects of current, past, and future events reflected in single-neuron firing patterns, in ensembles composed of 22-138 cells active during the period when the rats sampled the discriminative stimulus in each trial. 2. OF ensembles performed well at discriminating variables with relevance to task demands represented in single-neuron firing patterns, specifically the physical attributes and assigned reward contingency of the current odor as well as the expectation of reward in the following trial that could be inferred from the predictable associations between particular pairs of odors. OF ensembles were able to correctly identify the identity and assigned reward contingency of the current odor in up to 52% (chance = 12.5%) and 99% (chance = 50%) of all trials, respectively, such that the observed behavioral performance required a population of 5,364 odor-responsive cells in the case of odor identity and only 40 cells in the case of valence. Expectations regarding upcoming rewards based on both assigned response contingency and associations between particular pairs of odors were correctly classified in up to 67% (chance = 20%) of all trials such that the observed level of behavioral performance required a population of 3,169 cells. 3. Other information represented in the single-neuron firing patterns, such as the identity and reward contingency of the preceding odor and specific odor-odor associations, was poorly encoded by OF ensembles. Thus neural ensembles in OF may represent only some of the information reflected in single-neuron activity. Stable coding of only the most useful and relevant information by the ensemble might emerge from the tuning properties of single neurons under the influence of the task at hand, producing in the well-trained animal the observed pattern of broad and diverse coding by single neurons and selective, task-relevant coding by neural ensembles in OF.

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.

Topography of intensity tuning in cat primary auditory cortex: single-neuron versus multiple-neuron recordings

1995 ◽  
Vol 73 (1) ◽  
pp. 190-204 ◽  
Author(s):  
M. L. Sutter ◽  
C. E. Schreiner
Keyword(s):  
Single Neuron ◽  
Primary Auditory Cortex ◽  
Single Units ◽  
Pool Data ◽  
Single Neurons ◽  
Firing Rates ◽  
A Cell ◽  
Multiple Unit ◽  
Pure Tones ◽  
Multiple Units

1. We studied the spatial distributions of amplitude tuning (monotonicity of rate-level functions) and response threshold of single neurons along the dorsoventral extent of cat primary auditory cortex (AI). To pool data across animals, we used the multiple-unit map of monotonicity as a frame of reference. Amplitude selectivity of multiple units is known to vary systematically along isofrequency contours, which run roughly in the dorsoventral direction. Clusters sharply tuned for intensity (i.e., "nonmonotonic" clusters) are located near the center of the contour. A second nonmonotonic region can be found several millimeters dorsal to the center. We used the locations of these two nonmonotonic regions as reference points to normalize data across animals. Additionally, to compare this study to sharpness of frequency tuning results, we also used multiple-unit bandwidth (BW) maps as references to pool data. 2. The multiple-unit amplitude-related topographies recorded in previous studies were confirmed. Pooled multiple-unit maps closely approximated the previously reported individual case maps when the multiple-unit monotonicity or the map of bandwidth (in octaves) of pure tones to which a cell responds 40 dB above minimum threshold were used as the pooling reference. When the map of bandwidth (in octaves) of pure tones to which a cell responds 10 dB above minimum threshold map was used as part of the measure, the pooled spatial pattern of multiple-unit activity was degraded. 3. Single neurons exhibited nonmonotonic rate-level functions more frequently than multiple units. Although common in single-neuron recordings (28%), strongly nonmonotonic recordings (firing rates reduced by > 50% at high intensities) were uncommon (8%) in multiple-unit recordings. Intermediately nonmonotonic neurons (firing rates reduced between 20% and 50% at high intensities) occurred with nearly equal probability in single-neuron (28%) and multiple-unit (26%) recordings. The remaining recordings for multiple units (66%) and single units (44%) were monotonic (firing rates within 20% of the maximum at the highest tested intensity). 4. In ventral AI (AIv), the topography of monotonicity for single units was qualitatively similar to multiple units, although single units were on average more intensity selective. In dorsal AI (AId) we consistently found a spatial gradient for sharpness of intensity tuning for multiple units; however, for pooled single units in Aid there was no clear topographic gradient. 5. Response (intensity) thresholds of single neurons were not uniformly distributed across the dorsoventral extent of AI.(ABSTRACT TRUNCATED AT 400 WORDS)

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