Data analysis was carried out off-line. FS neurons were identified by their narrow APs and little steady-state firing-frequency accommodation.
We recorded the membrane potential V m responses to a serial of hyperpolarizing and depolarizing current steps ms in duration. In order to push AP firing to its maximal frequency, incremental depolarizing current steps increment, pA were repetitively applied until a severe decrease in AP amplitudes smaller than 30 mV, measured as the voltage difference between AP peak and the inter-spike valley was observed.
Since short somatic APs with severe sodium channel inactivation could propagate to distal axonal compartments Shu et al. Spikelets with amplitude smaller than 30 mV were not analyzed in this study.
The mean frequency of APs of each ms step was calculated by dividing the number of APs by the time period of ms, and the maximal mean frequency F mean is the highest one. The maximal instantaneous frequency F inst is the inverse value of the minimal ISI observed. Statistical significance of difference between samples was first tested using Kolmogorov-Smirnov K-S test. When a p -value smaller than 0. Box-whisker plots in figures are drawn as the Tukey box plot style, in which the bottom and top of the box are the first and third quartiles, and the band inside the box is the second quartile the median.
The lower end of the whisker is the lowest datum that still within 1. The distance between monkey's eye and the screen was 57 cm. To ensure the fixation, the monkey's eye position was monitored by an eye tracker Crist Instruments and compared to the location of fixation target by REX system NIH.
The distribution of spike width time from the trough to the peak of single units showed clear bimodal distribution. For each narrow-spiking unit, instantaneous frequencies were calculated as the reciprocal of ISIs. In vivo extracellular recording from the primary visual cortex of head-fixed awake mice was performed as previously described Chen et al. For the spike detection, the threshold was set at about 6 times of the noise level. Raw spikes were sorted as putative single units by using commercial software Offline Sorter, Plexon and then classified K-means method as broad- and narrow-spiking units based on their waveforms.
We calculated the spiking responses to the standard sinusoidal drifting gratings stimulation duration: 2 s, spatial frequency: 0. In human neocortical slices, we tested the AP firing pattern of every recorded neuron by step current injections.
These human FS neurons were categorized as three subtypes, in terms of their initial firing patterns in response to threshold current steps.
These three subtypes resemble the electrophysiological subtypes of cortical FS neurons of rodents Gupta et al. Besides the adaptation ratio, which can apparently differentiate those three subtypes c-FS 0.
The initial F-I slope of b-FS neurons 0. Subtypes of human neocortical FS neurons. Bottom traces are expanded view of the first solid line and last dashed line APs in a train and are in the same scale. D Comparison of adaptation ratio between human fast-spiking neuronal types. E F-I curves of all three types of human fast-spiking neurons.
F Comparison of initial F-I slope between human fast-spiking neuronal types. The circles in box-whisker plots are the maximal outliers. In response to ms current steps, the maximal mean AP frequency F mean without severe inactivation see Materials and Methods. Figures 2A—C of all human FS neurons was Figures 2D,E.
The maximal firing frequency of human cortical FS neurons. Dot plots are the corresponding instantaneous AP frequency. F—G The scatter plot of the maximal mean frequency F and the maximal instantaneous frequency G of human FS neurons vs. Dashed lines are the linear fit. Open and filled circles are epileptic and non-epileptic neurons, respectively.
The circles in box-whisker plots are the maximal or minimal outliers. Specifically, most of the tissue samples experienced repetitive epileptic seizures, which was always accompanied by changes in neuronal excitabilities.
Considering only epileptic samples, we found that the maximal firing frequency of FS neurons positively correlated with the age of patients Pearson's correlation coefficient 0. Figures 2F,G. This result suggests that the maximal firing frequency of FS neurons increased with age. It is supposed that higher firing frequency requires faster AP rising and falling, thus narrower AP. We found that the F mean and F inst were strongly correlated with AP waveform parameters, including AP rising rate and falling rate.
In other words, those FS neurons expressing higher responsiveness to current injection higher initial F-I slope tended to possess higher maximal firing frequencies. The correlation between maximal firing frequency and other intrinsic properties of human FS neurons. A The color coded correlation map of all intrinsic parameters examined in human FS neurons. B—C The linear fit of maximal instantaneous frequency vs.
Most of the human brain slices involved in this study were derived from association cortices. All of them were young adults see Materials and Methods. In response to ms current steps identical to those applied to human neurons, monkey FS neurons fired repetitive APs with a F mean of Figures 4D,E ; whereas the F mean Figures 4D,E.
These results demonstrate that the maximal firing frequency of FS neurons varies between similar cortices of different species and between cortices of the same species. Maximal firing frequency of monkey and mouse FS neurons. Gray traces are expanded view of the first APs. Dot plots indicate the corresponding instantaneous AP frequency of the V m responses composing the highest number of APs. D—E Comparison of the maximal firing frequency of monkey, human, and mouse FS neurons.
F Comparison of the AP width between different species. G Membrane time constants of human, monkey and mouse FS neurons. H The scatter plot of the maximal instantaneous frequency vs.
Solid lines are linear fits for each species. Comparison of the AP width of FS neurons revealed that they were wider in entorhinal cortex of mouse 0. Interestingly, there was no significant difference between the AP width of human and monkey FS neurons 0.
These results indicated that the difference in maximal firing frequency between human and monkey FS neurons was not accompanied by a discrepancy in the AP width. FS neurons are not only capable of generating high-frequency APs but also responsive to high-frequency synaptic inputs. Passive properties, such as a smaller membrane time constant, are required for reliable V m responses to high-frequency current inputs.
Even though we found no significant difference in the membrane time constant of FS neurons between species human This result suggests that, within species, those FS neurons possessing higher maximal firing frequency tended to be more responsive to high-frequency inputs. Furthermore, FS neurons in the mouse V1 expressed a much shorter membrane time constant 5. Our in vitro results were acquired under identical maintenance condition and using the same pattern of stimulation.
They revealed the differences in the intrinsic spiking ability of cortical FS neurons between species and between cortices.
In order to investigate to what extent the firing frequency of FS neurons could reach under physiological activation, we further examined the firing of FS neurons in the neocortex of behaving monkey and mouse. We plotted the cumulative distribution curves of ISIs for individual single units and found the curves of narrow-spiking units are more left-shifted i.
The maximal instantaneous frequency of those putative FS neurons was Considering that FS neurons do not always fire at their highest frequency during the fixation period, we also examined how often FS neurons fire at high frequencies.
Figures 5C,D. High-frequency firing of FS neurons in vivo. A Left, schematic drawing shows that single-unit recording is performed in the posterior parietal cortex of a monkey keeping fixation at a target point. Right, example averaged spike waveforms generated from 18 narrow-spiking units and 4 broad-spiking units recorded in monkey parietal cortex. B Example trace recorded in monkey neocortex during eye fixation period.
C Cumulative distribution of ISIs of all monkey units. Gray and light red lines are individual narrow- and broad-spiking units. Thick black and red lines are the cumulative distribution taking all 18 narrow units and all 4 broad units together, respectively. Red dashed line indicates the fifth percentile. X-axis values are in log scale. D The high-value extent of instantaneous frequency of cortical narrow-spiking units in monkey, along with in vitro F inst data for comparison.
E Left, schematic drawing shows that single-unit recording is performed in the primary visual cortex of a mouse receiving visual stimuli. Right, averaged spike waveforms generated from 18 narrow-spiking units black and 11 broad-spiking units red. F The spike density curves of narrow- black and broad- red spiking units in mouse V1 in response to visual stimuli.
Bin size, 1 ms. Data were collected from three cats, and two macaques. The cats were anaesthetized and the macaques were awake and free viewing. It is proposed that the low average rates were partly due to the effect of the anaesthetic which could be tested by systematically varying its level. The period from the initiation of the action potential to immediately after the peak is referred to as the absolute refractory period ARP see Figs.
This is the time during which another stimulus given to the neuron no matter how strong will not lead to a second action potential. The absolute refractory period takes about ms…. During the relative refractory period, a stronger than normal stimulus is needed to elicit neuronal excitation. However, during this time, the stimuli given must be stronger than was originally needed when the neuron was at rest.
The period during which a stronger than normal stimulus is needed in order to elicit an action potential is referred to as the relative refractory period RRP. We welcome suggestions for this page or anything on the site via our feedback box , though will not address all of them. An analysis of historical growth supports the possibility of radical increases in growth rate. Naive extrapolation of long-term trends would suggest massive increases in growth rate over the coming century, although growth over the last.
Several variants of this concept are worth distinguishing. In the course of our work, we have noticed a number of empirical questions which bear on our forecasts and might be relatively cheap to resolve. In the future we hope to address some of. AI Timelines. Clarifying concepts. Many scientists consider the best proxy measure of the speed or efficiency of thought processes to be reaction time — the time from the onset of a specific signal to the moment an action is initiated.
Indeed, researchers interested in assessing how fast information travels through the nervous system have used reaction time since the mids. This approach makes sense because thoughts are ultimately expressed through overt actions. Reaction time provides an index of how efficiently someone receives and interprets sensory information, decides what to do based on that information, and plans and initiates an action based on that decision.
The time it takes for all thoughts to occur is ultimately shaped by the characteristics of the neurons and the networks involved. Many things influence the speed at which information flows through the system, but three key factors are:. Distance — The farther signals need to travel, the longer the reaction time is going to be.
Reaction times for movements of the foot are longer than for movements of the hand, in large part because the signals traveling to and from the brain have a longer distance to cover. The key observation for the present purpose is that the same reflexes evoked in taller individuals tend to have longer response times than for shorter individuals. By way of analogy, if two couriers driving to New York leave at the same time and travel at exactly the same speed, a courier leaving from Washington, DC will always arrive before one leaving from Los Angeles.
Neuron characteristics — The width of the neuron is important. Signals are carried more quickly in neurons with larger diameters than those that are narrower — a courier will generally travel faster on wide multi-lane highways than on narrow country roads. How much myelination a neuron has is also important. Some nerve cells have myelin cells that wrap around the neuron to provide a type of insulation sheath.
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