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Previous Article | Next Article
The Journal of Neuroscience, July 15, 2000, 20(14):5503-5515
Periodicity and Firing Rate As Candidate Neural Codes for the Frequency of Vibrotactile Stimuli
Emilio Salinas, Adrián Hernández, Antonio Zainos, and Ranulfo Romo Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, 04510, México D.F., México
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
The flutter sensation is felt when mechanical vibrations between 5 and 50 Hz are applied to the skin. Neurons with rapidly adapting properties in the somatosensory system of primates are driven very effectively by periodic flutter stimuli; their evoked spike trains typically have a periodic structure with highly regular time differences between spikes. A long-standing conjecture is that, such periodic structure may underlie a subject's capacity to discriminate the frequencies of periodic vibrotactile stimuli and that, in primary somatosensory areas, stimulus frequency is encoded by the regular time intervals between evoked spikes, not by the mean rate at which these are fired. We examined this hypothesis by analyzing extracellular recordings from primary (S1) and secondary (S2) somatosensory cortices of awake monkeys performing a frequency discrimination task. We quantified stimulus-driven modulations in firing rate and in spike train periodicity, seeking to determine their relevance for frequency discrimination. We found that periodicity was extremely high in S1 but almost absent in S2. We also found that periodicity was enhanced when the stimuli were relevant for behavior. However, periodicity did not covary with psychophysical performance in single trials. On the other hand, rate modulations were similar in both areas, and with periodic and aperiodic stimuli, they were enhanced when stimuli were important for behavior, and were significantly correlated with psychophysical performance in single trials. Thus, the exquisitely timed, stimulus-driven spikes of primary somatosensory neurons may or may not contribute to the neural code for flutter frequency, but firing rate seems to be an important component of it.
Key words: awake monkeys; primary somatosensory cortex; secondary somatosensory cortex; neural coding; flutter; discrimination; periodicity; mutual information
INTRODUCTION
The sensation of flutter is produced when mechanical vibrations between 5 and 50 Hz are applied to the skin (Mountcastle et al., 1967
; Talbot et al., 1968
Nevertheless, the fourth and crucial observation was based on a small number of neurons (Mountcastle et al., 1990
MATERIALS AND METHODS
Neurophysiology and behavior. The behavioral task is schematized in Figure 1a [see also Romo et al. (1998)
Sinusoidal stimuli were used initially; 137 S1 neurons were studied in this way. Later we switched to trains of short mechanical pulses like those illustrated in Figure 1a. Each of these pulses consisted of a single-cycle sinusoid lasting 20 msec. For stimulation at 20 Hz, 11 successive pulses were applied, separated by 50 msec. This interval was measured between the beginnings of successive pulses. The data obtained with sinusoidal stimuli were not used in Figure 2 or in comparisons with S2 responses, but they were included in the comparisons between active and passive conditions and between neuronal and psychophysical responses.
Experiments with aperiodic stimuli were also conducted (Romo et al., 1998
Passive stimulation tests were also performed in blocks and were also interleaved between blocks of active discrimination. During passive stimulation, the hand used to press the push-buttons was restrained, so there were no behavioral reactions, and no reward was provided. Otherwise, single trials proceeded as with regular discrimination. The same combinations of base-comparison frequencies were used in experiments with periodic and aperiodic vibrations and in passive and active conditions. However, not all neurons could be recorded long enough to complete all the types of tests, so the numbers of recorded neurons varied across conditions.
Recordings were obtained with an array of seven independent microelectrodes of 2-3 M
(Mountcastle et al., 1990
Response measures. The firing rate in each trial was equal to the number of spikes emitted during the 500 msec stimulation period (base or comparison) divided by 0.5 sec. The mean firing rate r was obtained by averaging over trials of equal stimulus frequency; therefore, r and its SD
were functions of frequency (r and
2 minimization (Press et al., 1992
where s is the stimulus frequency. In the text, angle brackets
(1) indicate averaging over all stimulus frequencies. Thus,
r
where rmax and rmin are the maximum and minimum values, over all stimulus frequencies, of the mean firing rate.
(2) To relate periodicity to performance, we used several measures based on Fourier decompositions of the time signals formed by the evoked trains of spikes. For each trial, the power spectrum of the spike train evoked during the stimulation period (base or comparison) was computed and normalized, having had the DC component removed, so that the total power summed over all positive frequency bins was 100% (Press et al., 1992
Four quantities were extracted from each power spectrum, that is, at each trial. The first two were the power (or amplitude) at the stimulus frequency (PS) and the power at twice the stimulus frequency. These two numbers should increase for evoked spikes that are more tightly phase-locked to the stimulation pulses. The third quantity was the maximum power (maximum y coordinate) between 4 and 42 Hz, and its corresponding frequency (x coordinate) was the fourth quantity, which we denominated the power spectrum frequency at peak (PSFP). The three amplitudes measure how periodic a spike train is, whereas the PSFP is an actual estimate of stimulus frequency that depends on the periodicity of the evoked spike trains; this distinction is crucial (notice that the stimulus frequency needs to be known a priori to compute PS and the amplitude at twice the stimulus frequency). Note that the resolution of the PSFP depends on the width of the frequency bins of the power spectrum. Consider an example that was relatively common in S1. Suppose a neuron is strongly phaselocked to the stimulus and fires spikes somewhat like a clock, one or two spikes per stimulation pulse, in an approximately periodic fashion. In its spectra, the maximum power would be at the stimulus frequency. Thus, the PSFP amplitude would be equal to PS, the PSFP would be the center of the frequency bin nearest to the stimulus frequency, and all three amplitudes (at the PSFP, at the stimulus frequency, and at twice the stimulus frequency) would be much larger than the average power across all bins. Statistical tests applied to any of these four quantities were always performed also with the other three, but sometimes only the results for the most sensitive one are mentioned.
In each trial, we also computed the average interburst interval (AIBI), which measures how often a burst of spikes is produced. A burst was defined as a group of spikes in which all intervals between consecutive spikes were less than
msec. Thus, the number of spikes per burst was variable, with a minimum of 1, and all interspike intervals within a given burst had to be smaller than
Standardized responses. To compare the above responses in correct discrimination trials (hits) versus incorrect discrimination trials (errors), these responses had to be pooled across different base-comparison frequency pairs, because errors were rather infrequent. To illustrate the pooling method, we first consider the firing rate as the response. The standardized rate in a given trial was obtained by taking the original firing rate at that trial, subtracting the mean rate from all trials belonging to the same condition (base-comparison frequency pair), and dividing by the SD from the same subset of trials. The same was done for all trials in the data collection run. This eliminated any differences in response level attributable to preference for one frequency or combination of base and comparison stimulus frequencies
differences across conditions
Trial-to-trial covariations in the firing rates of simultaneously recorded neurons were measured using Pearson's linear correlation coefficient
(Press et al., 1992
where N is the total number of trials in the run, including all stimulus frequencies.
(3) Information estimates and other statistics. Having computed the firing rate, the PSFP, and the AIBI in each trial, we quantified how they varied as functions of stimulus frequency for any given cell. For this we used Shannon's mutual information (Cover and Thomas, 1991
The information that a response r provides about a stimulus s is computed from the probability distributions relating these two variables. In our case, s is the frequency of the applied flutter stimulus. The function P(r|s) represents the conditional probability of observing a response r given that the stimulus had a value s. The expression P(r) describes the probability of observing a response r regardless of the value of the stimulus, and P(s) is the probability that the stimulus takes a value s. When all stimuli are presented the same number of times, P(s) is simply a constant. Using these quantities, the information that the response provides about the stimulus can be computed as:
Here the sums are over all possible values of the stimulus and the response. Information is measured in bits. If the stimulus s can take N different values, the maximum amount of information that can be provided by any signal is log2(N) bits. A response carrying these many bits of information lets us determine exactly which of the N stimulus values is presented in any trial; stimulus and response are then maximally correlated. Most information results shown below correspond to experiments in which seven or eight frequencies were applied and in which, therefore, the maximum amount of mutual information was log2 (8) = 3 bits. An exception to this was made in comparisons between active and passive conditions, because here what mattered was the difference in information values (found with identical numbers of frequencies) across conditions; several sets with 9-11 frequencies were allowed in these cases.
(4) The information that the firing rate provided about the stimulus, IRATE, was computed from the set of firing rates from all trials assuming that the response probability distributions P(r|s) were Gaussian (Abbott et al., 1996
The significance of all information values was computed through Monte Carlo resampling schemes (Efron, 1982
When information is computed from relatively small numbers of data samples, it is typically biased upward with respect to its true value, especially when binned distributions are used (Treves and Panzeri, 1995
Overall, the average correction for IRATE and IAIBI was approximately
0.11 bits, and it was similar for significant and nonsignificant values. The mean correction was larger for IPSFP, because it was computed from binned distributions: on average it was approximately
Unless specified otherwise, other statistical comparisons were based on permutation tests (Siegel and Castellan, 1988
RESULTS
Firing rate and periodicity modulations in S1
Three monkeys (Macaca mulatta) were trained in the discrimination task. In each trial, the frequencies of two mechanical vibrations delivered successively had to be compared (Romo et al., 1998
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Figure 1. Behavioral paradigm and stimulus sets used. a, Schematic diagram of the task. In each trial, the mechanical probe was lowered so that it touched one of the fingertips of the restrained hand (PD, probe down); the monkey reacted, placing its free hand on a lever within 1 sec after indentation (KD, key down); after a delay period (1.5-3 sec) the probe oscillated vertically, delivering a series of pulses at a base frequency; after an interstimulus interval (1-3 sec), a second set of pulses was delivered at a comparison frequency; after the end of the comparison stimulus, the monkey had to release the lever within 600 msec (KU, key up) and press one of two push-buttons (PB). One button indicated that the comparison frequency was higher than the base, and the other indicated that the comparison was lower than the base. b, c, Two stimulus sets frequently used in the experiments. The numbers inside the grid indicate the percentage of correct responses for each base-comparison combination. Set A had constant differences of 8 Hz between base and comparison. Percentages are based on the performance of three monkeys throughout 350 runs with this set. Set B was designed to vary the difficulty of the task in a more systematic manner. The percentages shown correspond to 42 runs from two monkeys.
For each neuron, two quantities were computed in each trial: the mean firing rate and the PSFP, which is an estimate of stimulus frequency based on the periodicity of evoked action potentials (see Materials and Methods). We used the PSFP because, just like firing rate, it is a scalar quantity from which stimulus frequency can be estimated on a trial-by-trial basis; however, unlike with firing rate, the accuracy of this estimation depends on the periodicity of the spike trains. Figure 2, a and c, shows examples of S1 spike trains evoked during the base stimulus in individual trials, and Figure 2, b and d, shows the corresponding power spectra. The PSFP is simply the center (x coordinate) of the frequency bin with the most power. As illustrated in these Figures, the PSFP in S1 tends to be the same across trials. This is because the evoked spikes are phase-locked to the individual stimulation pulses. This can be seen more clearly in Figure 2h, which shows average S1 responses triggered at the time of individual pulses, the onset of which occurs at a time lag equal to 0 msec. The evoked activity reflects the periodicity of the sensory input. Curves for mean PSFP versus frequency were also obtained; this was done by averaging the PSFP over trials with equal stimulus frequency. These curves are shown in Figure 2f. Here the points fall close to the x = y line, confirming that the PSFP typically falls near the stimulus frequency. Curves for mean firing rate versus frequency were also obtained; examples are shown in Figure 2e. Notice that these neurons tend to fire more action potentials at higher stimulus frequencies. This was also true for the population: when straight lines were fit (Press et al., 1992
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Figure 2. Neuronal responses in S1. Left and middle columns show data, in the same format, from two neurons from areas 3b and 1, respectively. The right column shows population data from 68 neurons in area 3b and 61 neurons in area 1. All plots are based on neuronal activity evoked by the base stimulus. a, c, Raster plots from five trials in which stimulus frequency was 12 Hz (a) and 28 Hz (c). Small vertical ticks indicate spikes; each row corresponds to one trial. The long vertical line indicates stimulation onset. b, d, Power spectra of the five spike trains shown immediately above. Power is expressed as percentage of total power across all bins, but only frequencies within the flutter range are shown. e, Mean firing rate (±1 SD) as a function of stimulus frequency. Continuous line indicates best linear fit; dashed line indicates baseline firing rate, computed in the 800 msec preceding stimulation onset. For the neurons on the left and middle columns, IRATE = 0.50 ± 0.10 and 0.58 ± 0.09 bits, respectively (both significant). f, Mean PSFP (±1 SD) as a function of stimulus frequency. The diagonal dotted line indicates equality between x and y axes. For the neurons on the left and middle columns, IPSFP = 2.71 ± 0.03 and 2.08 ± 0.04 bits, respectively (both significant). Because PSFP values were discrete and often distributed bimodally, SDs here suggest more overlap between response distributions than was actually measured. g, Distribution of slopes from linear fits to the rate-versus-frequency curves (as in e). White and black bars correspond to area 3b and area 1 neurons, respectively. h, Average S1 responses triggered on individual stimulation pulses. The three histograms correspond to stimulation at 12, 20, and 28 Hz and were constructed from the responses of 89-102 S1 neurons tested at these frequencies. The y axis indicates the firing rate (in 1 msec time bins), averaged over neurons and trials, x milliseconds before or after the onset of an individual stimulation pulse, where x is called the time lag. Phase-locking is readily apparent at all frequencies. i, Cumulative distributions for IRATE and IPSFP. The value on the y axis represents the fraction of neurons with information smaller or equal to the amount indicated on the x axis. Thin lines indicate separate distributions for areas 3b and 1; thick lines correspond to pooled data sets. Note the different scales on the x axes.
Curves like those of Figure 2, e and f, give a rough idea of the strength of association between the stimulus and the evoked variations in firing rate and in PSFP, but comparing them against each other is difficult. Instead, a quantitative measure of association was computed: Shannon's mutual information (Cover and Thomas, 1991
In S1, the information that the PSFP
In contrast to these numbers, the information about stimulus frequency provided by the firing rate, IRATE, was approximately sixfold lower, but certainly not negligible (0.28 ± 0.23 bits; 74 of 129 values were significant). The distribution of values is shown in Figure 2i (left). What order of magnitude for IRATE should we have expected based on rate curves like those in Figure 2e? To get a better idea of the correspondence between the rate-versus-frequency curves and IRATE, consider the following idealized but representative example. Suppose the applied stimulus frequency s can take one of eight values, 8, 12, 16, 20, 24, 28, 32 or 36 Hz, and consider a neuron whose mean firing rate increases linearly with s with a slope of 0.7 spikes, typical of S1 (Fig. 2g), such that the evoked mean firing rate can be described by:
Here
(5) represents random Gaussian noise with zero mean and unit variance, so
Taken together, these results confirm that S1 spikes are precisely time-locked to the stimulation pulses (Mountcastle et al., 1969
Differences between S1 and S2
Figure 3 shows results, displayed in the same format as those in Figure 2, for a population of S2 neurons. The mean strength of rate modulations in S2 was comparable to that measured in S1: the average IRATE in S2 was lower (0.14 ± 0.18 bits), but the maximum values were still around 1 bit, as shown in Figure 2i, and ~20% of all values (139 of 689) were significant (a calculation similar to the one around Equation 5 in this case gave a typical IRATE of ~0.2 bits, assuming Poisson statistics). Thus, considerable rate modulation was also present in S2. However, comparison between S1 and S2 responses revealed four major differences.
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Figure 3. Neuronal responses in S2. Left and middle columns show data from two neurons: the firing rate of one increases with increasing stimulus frequency (positive slope), and the firing rate of the other decreases with increasing stimulus frequency (negative slope). Slopes were extracted from the linear fits shown in e. Same format is used as in Figure 2, except in d, middle column, frequency was 27 Hz; in e, IRATE = 0.89 ± 0.09 and 0.75 ± 0.13 bits for left and middle columns, respectively (both significant); in h, IPSFP = 0.26 ± 0.18 and IPSFP = 0 ± 0.30 bits, for left and middle columns, respectively (both not significant). In h, histograms are averages of 250-287 neurons (note same scale as in Fig. 2). Population data in g and i are based on 689 S2 neurons. All data are based on neuronal activity evoked by the base stimulus.
First, a lack of periodicity in S2 was revealed. This can be seen in the spike rasters of Figure 3, a and c, and in the pulse-triggered responses of Figure 3h. For the latter, the responses of neurons with positive and negative slopes were averaged, which is why the mean firing rates are so similar at the three frequencies shown. Note that phase-locking is hardly noticeable, especially at higher frequencies. Consistent with this, the mean PSFP in this case is practically independent of stimulus frequency, as illustrated in the examples of Figure 3f. In quantitative terms, the mean IPSFP in S2, computed for the spikes evoked during the base stimulus, was an order of magnitude smaller than in S1 (0.17 ± 0.34 bits), and only 52 of the 689 neurons had values that were significantly different from zero.
Second, S2 contained a larger proportion of neurons that fired most strongly at low stimulation frequencies. The middle column of Figure 3 illustrates such a unit, and e shows that its rate decreases as a function of frequency. As mentioned above, in S1 stronger activity typically occurred at higher frequencies, as shown in Figure 2e. When firing rates were fitted (Press et al., 1992
The third difference was that "flat" neurons were more abundant in S2 (62%, 428/689) than in S1 (31%, 40/129). Flat neurons had firing rates that did increase or decrease significantly during stimulation, compared with the baseline activity preceding the base stimulus, but were not affected by stimulus frequency, i.e., IRATE was not significant (here we used p > 0.05 as a criterion). This was also reflected in the distribution of slopes: a larger fraction of S2 neurons had slopes that were close to zero, as can be seen by comparing Figures 2g and 3g. This difference in the proportion of flat neurons could be caused partly by suboptimal stimulation of S2; we have observed that S2 receptive fields have essentially no preference for one or another fingertip (Fitzgerald et al., 1999
Fourth, many neurons in S2 either sustained their frequency-specific responses beyond the base stimulus or displayed them only after stimulus offset, during the interstimulus interval. Figure 4a illustrates this for a neuron with positive slope that maintained significant rate modulation even 1 sec after stimulus offset. Figure 4c shows the activity of another, more typical neuron that had a negative slope and prolonged its response for a few hundred milliseconds. The histograms in Figure 4, b and d, indicate the amount and significance of IRATE and IPSFP for these neurons as a function of time, and the plot in Figure 4e presents the numbers of S2 neurons with significant information (IRATE and IPSFP) also as a function of time. Notice that ~13% (89/689) of the neurons displayed significant rate modulations in the 250 msec window after stimulus offset. Figure 4e also shows that during this same period, 10 neurons also had significant IPSFP; however, the number expected just by chance was seven. This means that the sustained activity lacked a significant oscillatory component. Significant rate modulations after the stimulus were not observed in S1: four neurons had significant IRATE during the interstimulus interval, but three were expected just by chance. Therefore, sustained activity was absent in the primary sensory area [compare with Zhou and Fuster (1996
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Figure 4. Sustained neuronal responses in S2. The base stimulus turned on at time zero, lasting 500 msec; stimulus onset and offset are indicated by dotted vertical lines. Interstimulus interval duration was 1-3 sec. a, Spike density histograms of a neuron that fired most strongly at high frequencies (positive slope). For the shown traces, stimulus frequencies were 8, 20, and 28 Hz, as indicated. b, Information (+1 SD) carried by the neuron illustrated in a as a function of time. IRATE (black bars) and IPSFP (white bars) were computed every 250 msec using the spikes contained in a 250 msec time window centered at the midpoint (x coordinate) between bars. Large and small dots indicate significance levels of p < 0.01 and p < 0.05, respectively. c, Spike density histograms of a neuron that fired most strongly at low frequencies (negative slope); same stimulus frequencies as in a. d, Information carried by the neuron illustrated in c as a function of time. e, Number of neurons with significant (p < 0.01) information about stimulus frequency as a function of time. Black bars correspond to IRATE and white bars to IPSFP, as in b and d. All spike densities were obtained by convolving the spike trains with a Gaussian kernel of SD equal to 30 msec and averaging over trials of equal frequency.
A simple compromise between firing rate and timing
The above results show that, on the basis of single-cell comparisons, firing rate modulations in S2 were somewhat weaker than those in S1 in terms of information content; on average, IRATE differed by a factor of 2. However, the difference in terms of periodicity was a factor of 10. Although in S2 the actual average values of IRATE and IPSFP were similar, two points should be stressed: first, that the fraction of neurons with significant IRATE was twice as high as the fraction of neurons with significant IPSFP, and second, that the IPSFP values represent upper bounds.
We also checked whether distinctions between frequencies could be made based on the AIBI in each trial. A burst is simply a group of spikes close together in time, like those shown in Figure 2a (left). We defined a burst through a time window
For each neuron, the AIBI was obtained in each trial, and the information that the AIBI provided about stimulus frequency, IAIBI, was computed (see Materials and Methods). Parameter
According to these results, neurons immediately downstream from S1 may read out stimulus frequency in at least two ways: either from S1 firing rate modulations or from the periodic structure of S1 spike trains. In contrast, for neurons downstream from S2, the second possibility may not be available. Hence, two areas involved in somatosensory processing could potentially use fundamentally different codes to represent the same quantity. S1 is extremely important for somatosensory processing: lesions in this area cause severe impairments in discrimination and categorization tasks (LaMotte and Mountcastle, 1979
Context-dependent modulations of activity
In general, the attentional state of a subject performing a task may have a strong influence on the neurons involved in it; neuronal responses are often enhanced when attention is focused on a sensory feature that the neurons react to (Hsiao et al., 1993
Figure 5 compares S1 activity evoked during the comparison stimulus in active and passive conditions. Figure 5a shows that the mean IRATE was significantly higher in the active condition (0.42 ± 0.35 bits in active, 0.27 ± 0.23 bits in passive; n = 50 neurons with significant information in at least one of the conditions; p < 0.0004); indeed, most points fall above the equality line. Other measures of neuronal activity also showed significant variations across conditions. Figure 5c shows the average variability in firing rate across trials,
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Figure 5. Behavioral context modulates neuronal activity in S1. In all plots, responses during active discrimination (y axes) are compared with responses during passive stimulation (x axes). Diagonal lines indicate equality between x and y axes. All comparisons are based on the responses of 77 S1 neurons tested in both situations. All quantities were computed from the responses to the comparison stimulus. a, The mean IRATE was significantly higher during active discrimination (p < 0.0004); note that higher values tend to fall above the x = y line. Circles correspond to 50 neurons with IRATE significantly different from zero in at least one of the two conditions, and dots indicate nonsignificant neurons. Crosses indicate the mean uncertainty in the information values; they correspond to ±1 average SD in each direction. b, Circles correspond to 63 neurons with IPSFP significantly different from zero in at least one of the two conditions, and dots indicate nonsignificant neurons. The mean IPSFP was higher during active discrimination, and the effect was close to but below the significance threshold (p > 0.025). Crosses correspond to ±1 average SD in each direction. c, Trial-to-trial variability in the firing rate, quantified by
We were concerned about this result, however, because we had not taken into account the correlations among neurons, i.e., the stimulus-independent co-fluctuations in numbers of spikes fired. For certain changes in the correlations, the information about stimulus frequency transmitted jointly by the rates of multiple neurons might have actually decreased, despite an increase in the information conveyed by individual neurons (Shadlen and Newsome, 1998
Very similar differences between rate modulation in active and passive conditions were obtained in S2. Figure 6a and c, illustrates this for IRATE and
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Figure 6. Behavioral context modulates neuronal activity in S2. In all plots, responses during active discrimination (y axes) are compared with responses during passive stimulation (x axes). Diagonal lines indicate equality between x and y axes. All comparisons are based on the responses of 108 S2 neurons tested in both situations. Format is the same as in Figure 5, except that all quantities were computed from the responses to the base stimulus. a, The mean IRATE was significantly higher during active discrimination (p < 0.0002); note that higher values tend to fall above the x = y line. Circles correspond to 43 neurons with IRATE significantly different from zero in at least one of the two conditions, and dots indicate nonsignificant neurons. Crosses indicate the mean uncertainty in the information values; they correspond to ±1 average SD in each direction. b, Circles correspond to 19 neurons with IPSFP significantly different from zero in at least one of the two conditions, and dots indicate nonsignificant neurons. The mean IPSFP was not significantly different in the two conditions (p > 0.11). Crosses correspond to ±1 average SD in each direction. c, Trial-to-trial variability in the firing rate, quantified by
The periodicity of evoked spikes in S1 was also different in active and passive tests, although the changes seemed more subtle than for rate. This is shown in Figure 5b. Here a disproportionate number of data points seem to fall above the equality line, in agreement with the finding that the mean IPSFP was larger in the active condition (1.62 ± 0.90 in active, 1.45 ± 0.85 in passive; n = 63 S1 neurons with significant IPSFP in at least one condition), but the effect did not reach the significance criterion of 0.01 (p > 0.025). However, we also compared the mean power at stimulus frequency (mean PS) across conditions. This quantity is just the percentage of power at the frequency bin that includes the stimulus frequency, averaged over all trials (see Materials and Methods). The data are shown in Figure 5d. The mean PS was also larger during active discrimination (0.72 ± 0.53% in active, 0.62 ± 0.38% in passive; n = 77 S1 neurons), and in this case the effect was significant (p < 0.005). Thus, the timing of evoked S1 spikes relative to the stimulation pulses was more regular during active discrimination; tighter phase-locking occurred in this condition.
Figure 6, b and d, shows that in contrast to S1, no changes in periodicity were detected in S2 in terms of IPSFP and mean PS (Fig. 6, see legend). The same happened for the mean power at the PSFP and for the mean power at twice the stimulus frequency.
Runs of passive tests were applied in blocks, typically after a block of active discrimination trials. Thus, we considered the possibility that the results of this section might have been corrupted by some sort of systematic drift in the recordings, such that later tests tended to be, for instance, more noisy. However, each neuron was typically tested in more than two conditions, so we were able to run the same battery of statistical comparisons on experiments of the same type, active or passive, testing for differences between early and late experimental runs. For example, if three runs (complete blocks of trials) were collected with the sequence active, passive, active, statistical tests were performed between the passive run and the first active run, and the same tests were repeated for the second and first active runs, as a control. Across the neural population, no significant effects were obtained in any of the control comparisons, showing that the described differences between active and passive conditions were not caused by drift artifacts.
In summary, these experiments showed an attentional or a contextual enhancement of neural activity. In both S1 and S2, the firing rate encoded stimulus frequency better when the stimulus guided the animal's behavior, in the sense that rate provided more information about the relevant stimulus feature. The periodicity of the evoked spikes did not change with behavioral context in S2, but it did so in S1. This was surprising and indicates that spike timing may be influenced by attention or behavioral context (Steinmetz et al., 2000
Responses to aperiodic stimuli
Two of the monkeys also discriminated the average frequencies of aperiodic stimuli (Romo et al., 1998
The monkeys' performance in this task only decreased slightly compared with discrimination of periodic stimuli: overall, 88 versus 80% correct (Romo et al., 1998
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Figure 7. Neuronal responses to periodic and aperiodic stimuli. The four raster plots at the top show spike trains from an S1 neuron that was tested with periodic (a) and aperiodic (b) stimuli at frequencies of 12 and 35 Hz, as indicated. Each set of responses includes 10 trials collected during active discrimination. For a given stimulus frequency, the train of stimulation pulses was identical for all periodic trials but was different for all aperiodic trials. However, at a given mean frequency, the total number of pulses delivered was the same in both conditions. Long vertical lines indicate stimulus onset. In the periodic condition the neuron had IRATE = 0.67 ± 0.10 and IAIBI = 1.44 ± 0.10 bits; with aperiodic stimulation IRATE = 0.70 ± 0.10 and IAIBI = 0.65 ± 0.11 bits. IRATE (c) and IAIBI (d) were computed for 41 S1 and 30 S2 neurons tested with periodic and aperiodic stimuli. In both panels, circles and triangles correspond to S1 and S2 neurons, respectively, with significant information in at least one of the two conditions (periodic or aperiodic), and small dots indicate S1 and S2 neurons that had nonsignificant values in the two conditions. Diagonal lines indicate equal values in the two axes. Crosses on the bottom right corners indicate ±1 average SD in each direction. IRATE did not change across conditions, in either area; data points in c are distributed symmetrically around the diagonal line. IAIBI was significantly larger with periodic stimulation; data points in d tend to fall below the diagonal. With aperiodic pulses, IAIBI was similar to IRATE; the y-axis values in c and d are similar (see Results).
Not surprisingly, in these experiments IPSFP practically vanished: of 41 S1/S2 neurons with significant IPSFP in the periodic condition, only one had a significant value in the aperiodic condition. The same thing happened with the mean power at the PSFP, at the mean stimulus frequency and at twice the mean stimulus frequency. This was expected and simply showed that no consistent modulations in periodicity are seen with aperiodic stimulation; the Fourier spectrum shows no regularity from one trial to the next.
What about bursts of spikes; could they provide a reliable measure of mean stimulus frequency for aperiodic stimuli? In the periodic condition, the AIBI of S1 neurons carried more information than the rate, as has been described. The AIBI of S1 neurons also provided significant information in the aperiodic condition but, as shown in Figure 7d, IAIBI in this case was significantly smaller than with periodic stimulation (0.71 ± 0.47 in periodic, 0.32 ± 0.18 in aperiodic; n = 31 S1 neurons tested in both conditions and with significant IAIBI in at least one of them; p < 0.0002); most data points fall below the equality line. The key observation here is that with aperiodic stimuli, IAIBI was, on average, slightly smaller than IRATE, and this was the case whether all neurons or only those with significant information were compared. This can be appreciated by comparing the y-axis values in Figure 7, c and d. Comparisons using bursts of other sizes were also made
Discrimination of periodic and aperiodic stimuli corresponds to slightly different tasks; in fact, the two types of stimuli can be distinguished easily by human subjects. This means that at least some information about the temporal structure of the stimuli is readily accessible perceptually. Therefore, these results cannot exclude the possibility that temporal information is used to construct the neural representation of stimulus frequency, even in the aperiodic condition. However, they provide two conclusions: first, that constructing a neural representation of stimulus frequency based on the temporal patterns of spikes evoked during aperiodic stimulation would require read-out mechanisms more powerful than simply identifying bursts of spikes in single cells, and second, that firing rate could provide a neural code for frequency common to the two tasks and the two areas.
Covariations between neuronal and behavioral responses
As mentioned in the introductory remarks, earlier studies pointed out a close match between the discriminability of flutter frequencies and the observed variance in the phase at which spikes are evoked by periodic stimuli (Mountcastle et al., 1969
The main idea was to compare neuronal responses during correct discriminations (hits) with responses during error trials. Because errors were much less frequent than hits, responses obtained for different conditions, i.e., for different combinations of base and comparison stimulus frequencies, had to be standardized and pooled, as described in Materials and Methods. In all data collection runs, two types of trials were considered separately. In trials of type 1, the frequency of the comparison stimulus was lower than the frequency of the base, and in trials of type 2, the frequency of the comparison was higher than the frequency of the base. Thus, for each run, two comparisons were made, one for each set of trials of the same type. In both cases the mean standardized response in error trials was compared with the mean standardized response in hit trials, and the significance of the difference was determined. Figure 8, a and c, illustrates this procedure for a single S1 neuron, and Figure 9, a and c, illustrates it for a single S2 neuron. Here H and E indicate hit and error categories, respectively, and the subscript indicates the type of trial. Each dot corresponds to a single trial, and the horizontal bars indicate the means for hits and errors in the corresponding categories. In both Figures, the difference between panels a and c lies in the quantity considered as the response.
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Figure 8. Single-trial covariations between behavioral responses and neuronal responses in S1. This analysis was based on the neuronal activity evoked by the comparison stimulus. a, Standardized firing rates (dots) for a single S1 neuron are shown subdivided into four categories: hits (H1) and errors (E1) when the frequency of the base stimulus was higher than the frequency of the comparison stimulus (type 1 trials), and hits (H2) and errors (E2) when the frequency of the comparison stimulus was higher than the frequency of the base (type 2 trials). Thick horizontal lines represent mean values for each category. For this neuron, the mean values in error trials were significantly different (p < 0.002) from the mean values in hit trials of the same type. This neuron had a positive slope of 1.17 ± 0.12 spikes. b, The standardized firing rate reveals a negative correlation between error types in S1. Each point represents one of 191 S1 neurons with at least five errors of each type. For each neuron, the mean difference in standardized rate between errors and hits for type 2 trials (E2
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Figure 9. Single-trial covariations between behavioral responses and neuronal responses in S2, based on the neuronal activity evoked by the comparison stimulus. Same format as in Figure 8. a, Standardized firing rates (dots) for a single S2 neuron are shown subdivided into four categories: hits (H1) and errors (E1) when the frequency of the base stimulus was higher than the frequency of the comparison stimulus (type 1 trials), and hits (H2) and errors (E2) when the frequency of the comparison stimulus was higher than the frequency of the base (type 2 trials). Thick horizontal lines represent mean values for each category. For this neuron, the mean values in error trials were significantly different (p < 0.0002) from the mean values in hit trials of the same type. b, The standardized firing rate reveals a negative correlation between error types in S2. Each point represents one of 128 S2 neurons with at least five errors of each type. For each neuron, the mean difference in standardized rate between errors and hits for type 2 trials (E2
To detect systematic variations in periodic spike timing, we computed standardized versions of the PSFP amplitude, of PS, and of the amplitude of the power spectrum at twice the stimulus frequency. These three quantities tend to increase the closer a spike train is to a perfectly periodic arrangement, so high values should correspond to better likelihood for correct discrimination, if periodicity is related to performance. In most S1 and S2 cells, these quantities were the same in hit and error trials, as illustrated in Figures 8c and 9c. We did find four S1 neurons for which the mean standardized PS was significantly different for hit and error trials, but this number of neurons was not significantly different (p > 0.21) from that expected by chance under the null hypothesis that hit and error responses come from the same distribution (among 238 S1 neurons, 2.38 significant tests at the 0.01 level were expected by chance). In other words, the result was not significant. The same was true for the three measures of periodicity, in both S1 and S2, and for type 1 and type 2 trials. The periodicity of spikes in single neurons showed no detectable covariations with behavior.
This negative result, however, was obtained by testing the neurons one at a time, but if higher periodicity tends to produce better performance, then across the population, standardized responses might show a tendency to be larger in hit trials than in error trials. Nevertheless, no such trend was observed. This is illustrated in Figures 8d and 9d. In these plots, each point corresponds to one neuron. Each x coordinate is the difference between the mean of all type 1 error trials and the mean of all type 1 hit trials, using the standardized PS as a response, and the y coordinate is the same quantity but for type 2 trials. Observe that the clouds of points are symmetric and centered at 0 in both directions. This is because, on average, differences between responses in hits and errors were not significantly different from zero and were not correlated across types of trials either: for the S1 population in Figure 8d, the correlation coefficient was practically zero (0.008, p < 0.9), and for the S2 population in Figure 9d, the correlation coefficient was
In contrast, the numbers of evoked spikes did show significant covariations with behavioral performance. Figure 8a shows data from an S1 neuron with large differences between the means of the H and E categories. This neuron had a positive slope of 1.17 ± 0.12 spikes. The significant difference between H1 and E1 means that at any given comparison frequency lower than the base, on average, the chances of observing an error were higher when the neuron fired more spikes than usual for the given comparison frequency. This association was not a rare event. Among 231 runs that had at least five type 2 errors, we found 11 S1 neurons whose average standardized rates were significantly different in hit and error trials. This number may appear small, but with 231 samples the chances of finding at least 11 significant values when no real difference exists between two conditions is <3 in 105 (binomial distribution with p = 0.01). Among the 219 runs with sufficient type 1 errors, only four neurons with significant differences were found, which was within the range expected by chance (p > 0.18), but there was additional evidence for the firing rate being related to behavior. In this case, a significant effect was observed across the population: the differences between standardized responses for hits and errors were significantly anticorrelated across trial types. This is shown in Figure 8b. In this plot the cloud of points is not symmetric; its correlation coefficient is
In S2 the relationship between rate and behavior was even more evident. In type 1 trials, 28 of 321 neurons had significant differences between hits and errors, and in type 2 trials the numbers were 16 out of 206. The probability of finding these many by chance was, in both cases, <10
9. As can be seen in Figure 9b, differences across trial types were negatively correlated in this area too. These anticorrelation patterns would be expected if a comparison between the average rates of two neuronal populations underlaid the comparison between frequencies required by the discrimination process (Salinas and Romo, 1998
DISCUSSION
In these experiments, we examined the responses of S1 and S2 neurons to mechanical vibrations applied to the fingertips. Our aim was to determine, in our laboratory task where the only relevant stimulus feature is temporal frequency, what attributes of the evoked neuronal activity are important for behavior. In our simplified task this amounts to finding out what is the neural code for stimulus frequency. We specifically examined the hypothesis that such a code is constructed by some neural mechanism that reads out the periodic interspike intervals of the spike trains evoked in S1. As discovered in earlier work, the periodicity of spikes evoked by flutter vibrations was extremely prominent and reliable in this area. Unfortunately, however, we could not determine with certainty whether their precise timing plays a significant functional role in frequency discrimination. Previous studies contemplated a code for flutter frequency based exclusively on periodicity (Mountcastle et al., 1967
The evidence is as follows. First, rate modulations were widespread. We found that, contrary to earlier reports (Mountcastle et al., 1990
Although not conclusive in terms of the specific question we pursued, the experiments with periodic stimuli revealed a number of interesting facts about the timing of evoked spikes in the somatosensory cortices. First, as found earlier, periodicity was extremely high in S1 (Fig. 2f,h,i), and presumably it is even higher in primary afferents (Talbot et al., 1968
Coding mechanisms based exclusively on periodicity appear to be too rigid, as illustrated by the experiments with aperiodic stimulation: periodicity in S1 activity could not encode mean frequency during stimulation with aperiodic patterns, in the sense that mean frequency could not be read out from a Fourier decomposition of the evoked S1 responses, but temporal integration in a wider sense cannot be ruled out by these results. One key question in the discrimination task performed by our monkeys is how neurons postsynaptic to S1 integrate their incoming inputs: are they are able to read out some of the temporal features of the evoked activity? Neurons downstream from S1 must respond to certain features in the temporal structure of S1 activity, and they account for the ability of human subjects to distinguish without difficulty between periodic and aperiodic stimuli. From the comparisons between IRATE and IAIBI it appears that such features need to be more sophisticated than plain bursts of spikes from single neurons, but other schemes are possible. We also found that the precise timing of S1 spikes, in relation to the periodic stimulation pulses, was not appreciably correlated with psychophysical performance, but there might be particular time scales or temporally sensitive processes for which variations in S1 spike timing do have an impact in postsynaptic activity and in the code for flutter frequency, and the present experimental design or the analytic tools that we used may have been insensitive to those time scales. In the present task, the monkeys were not required to extract any features from the temporal structure of the stimuli other than the mean frequency, but the results
Evidently, single cell recordings also have limitations; possible neural codes based on coordinated spike timing across multiple neurons have been reported (O'Keefe and Recce, 1993
FOOTNOTES Received March 13, 2000; revised April 26, 2000; accepted May 1, 2000.
This research was supported by an International Scholars Award from the Howard Hughes Medical Institute and grants from DGAPA-UNAM and CONACYT to R.R. We thank Bill Newsome and Carlos Brody for invaluable suggestions and discussions, and David Egelman for helpful comments. We appreciate the technical assistance of Sergio Méndez and Federico Jandete. E.S. also thanks Terry Sejnowski and the Howard Hughes Medical Institute for their support during final stages of this work. R.R. designed and performed the experiments with the assistance of A.H. and A.Z.; E.S. designed and performed the data analysis; E.S. and R.R. co-wrote this manuscript.
Correspondence should be addressed to Ranulfo Romo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, 04510, México D.F., México. E-mail: rromo{at}ifisiol.unam.mx.
Dr. Salinas's present address: Computational Neurobiology Laboratory, The Salk Institute, 10010 North Torrey Pines Road, La Jolla, CA 92037.
REFERENCES
- Abbott LF, Dayan P (1999) The effect of correlated activity on the accuracy of a population code. Neural Comput 11:91-101[Web of Science][Medline].
- Abbott LF, Rolls ET, Tovee MJ (1996) Representational capacity of face coding in monkeys. Cereb Cortex 6:498-505
[Abstract/Free Full Text] .- Ahissar E (1998) Temporal-code to rate-code conversion by neuronal phase-locked loops. Neural Comput 10:597-650[Web of Science][Medline].
- Ahissar E, Vaadia E (1990) Oscillatory activity of single units in a somatosensory cortex of an awake monkey and their possible role in texture analysis. Proc Natl Acad Sci USA 87:8935-8939
[Abstract/Free Full Text] .- Cariani PA, Delgutte B (1996) Neural correlates of the pitch of complex tones. I. Pitch and pitch salience. J Neurophysiol 76:1698-1716
[Abstract/Free Full Text] .- Chubbuck JK (1966) Small motion biological stimulator. Appl Digest 5:1319-1341.
- Cover TM, Thomas JA (1991) In: Elements of information theory. New York: Wiley.
- Dan Y, Alonso JM, Usrey WM, Reid RC (1998) Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus. Nat Neurosci 1:501-507[Web of Science][Medline].
- DeCharms RC, Merzenich MM (1995) Primary cortical representation of sounds by the coordination of action potential timing. Nature 381:610-613.
- Efron B (1982) In: The jacknife, the bootstrap and other resampling plans. Philadelphia: Society for Industrial and Applied Mathematics.
- Fitzgerald PJ, Lane JW, Yoshioka T, Nakama T, Hsiao SS (1999) Multi-digit receptive field structures and orientation tuning properties of neurons in SII cortex of the awake monkey. Soc Neurosci Abstr 25:1684.
- Gardner EP, Palmer CI, Hamalainen HA, Warren S (1992) Simulation of motion on the skin. V. Effect of stimulus temporal frequency on the representation of moving bar patterns in primary somatosensory cortex of monkeys. J Neurophysiol 67:37-63
[Abstract/Free Full Text] .- Gawne TJ, Richmond BJ (1993) How independent are the messages carried by adjacent inferior temporal neurons? J Neurosci 13:2758-2771[Abstract].
- Gochin PM, Colombo M, Dorfman GA, Gerstein GL, Gross CG (1994) Neural ensemble coding in inferior temporal cortex. J Neurophysiol 71:2325-2337
[Abstract/Free Full Text] .- Hernández A, Salinas E, García R, Romo R (1997) Discrimination in the sense of flutter: new psychophysical measurements in monkeys. J Neurosci 17:6391-6400
[Abstract/Free Full Text] .- Hsiao SS, Johnson KO, O'Shaughnessy DM (1993) Effects of selective attention of spatial form processing in monkey primary and secondary somatosensory cortex. J Neurophysiol 70:444-447
[Abstract/Free Full Text] .- Jones EG (1975) Lamination and differential distribution of the thalamic afferents within the sensory-motor cortex of the squirrel monkeys. J Comp Neurol 160:167-204[Web of Science][Medline].
- Jones EG (1983) Lack of collateral thalamocortical projection to fields of the first somatic sensory cortex in monkeys. Exp Brain Res 52:375-384[Web of Science][Medline].
- LaMotte RH, Mountcastle VB (1975) The capacities of humans and monkeys to discriminate between vibratory stimuli of different frequency and amplitude: a correlation between neural events and psychophysical measurements. J Neurophysiol 38:539-559
[Abstract/Free Full Text] .- LaMotte RH, Mountcastle VB (1979) Disorders in somesthesis following lesions of parietal lobe. J Neurophysiol 42:400-419
[Abstract/Free Full Text] .- Leopold DA, Logothetis NK (1996) Activity changes in early visual cortex reflect monkeys' percepts during binocular rivalry. Nature 379:549-553[Medline].
- McAdams CJ, Maunsell JHR (1999) Effects of attention on orientation tuning functions of single neurons in macaque cortical area V4. J Neurosci 19:431-441
[Abstract/Free Full Text] .- McLurkin JW, Optican LM, Richmond BJ, Gawne TJ (1991) Concurrent processing and complexity of temporally encoded neuronal messages in visual perception. Science 253:675-677
[Abstract/Free Full Text] .- Mountcastle VB, Talbot WH, Darian-Smith I, Kornhuber HH (1967) Neural basis of the sense of flutter-vibration. Science 155:597-600
[Abstract/Free Full Text] .- Mountcastle VB, Talbot WH, Sakata H, Hyvärinen J (1969) Cortical neuronal mechanisms in flutter-vibration studied in unanesthetized monkeys. Neuronal periodicity and frequency discrimination. J Neurophysiol 32:453-484.
- Mountcastle VB, Steinmetz MA, Romo R (1990) Frequency discrimination in the sense of flutter: psychophysical measurements correlated with postcentral events in behaving monkeys. J Neurosci 10:3032-3044[Abstract].
- Mountcastle VB, Reitboek HJ, Poggio GF, Steinmetz MA (1991) Adaptation of the Reitboek method of multiple microelectrode recording to the neocortex of the waking monkey. J Neurosci Methods 36:77-84[Web of Science][Medline].
- Ochoa J, Torebjörk E (1983) Sensations evoked by intraneural microstimulation of single mechanoreceptor units innervating the human hand. J Physiol (Lond) 342:633-654
[Abstract/Free Full Text] .- O'Keefe J, Recce ML (1993) Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3:317-330[Web of Science][Medline].
- Pons TP, Garraghty PE, Friedman DP, Mishkin M (1987) Physiological evidence for serial processing in somatosensory cortex. Science 237:417-420
[Abstract/Free Full Text] .- Pons TP, Garraghty PE, Mishkin M (1992) Serial and parallel processing of tactal information in somatosensory cortex of rhesus monkeys. J Neurophysiol 68:518-527
[Abstract/Free Full Text] .- Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) In: Numerical recipes in C. Cambridge: Cambridge UP.
- Recanzone GH, Merzenich MM, Schreiner CE (1992) Changes in the distributed temporal response properties of S1 cortical neurons reflect improvements in performance on a temporally based tactile discrimination task. J Neurophysiol 67:1071-1091
[Abstract/Free Full Text] .- Richmond BJ, Optican L (1990) Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. II. Information transmission. J Neurophysiol 64:370-380
[Abstract/Free Full Text] .- Riehle A, Grün S, Diesemann M, Aertsen A (1997) Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278:1950-1953
[Abstract/Free Full Text] .- Romo R, Merchant H, Zainos A, Hernández A (1996) Categorization of somaesthetic stimuli: sensorimotor performance and neuronal activity in primary somatic sensory cortex of awake monkeys. NeuroReport 7:1273-1279[Web of Science][Medline].
- Romo R, Hernández A, Zainos A, Salinas E (1998) Somatosensory discrimination based on cortical microstimulation. Nature 392:387-390[Medline].
- Romo R, Brody CD, Hernández A, Lemus L (1999) Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399:470-473[Medline].
- Salinas E, Romo R (1998) Conversion of sensory signals into motor commands in primary motor cortex. J Neurosci 18:499-511
[Abstract/Free Full Text] .- Shadlen MN, Newsome WT (1994) Noise, neural codes and cortical organization. Curr Opin Neurobiol 4:569-579[Medline].
- Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity, computation and information coding. J Neurosci 18:3870-3896
[Abstract/Free Full Text] .- Siegel S, Castellan NJ (1988) In: Nonparametric statistics for the behavioral sciences. New York: McGraw-Hill.
- Sinclair RJ, Burton H (1991) Neuronal activity in the primary somatosensory cortex in monkeys (Macaca mulatta) during active touch of textured surface gratings: responses to groove width, applied force, and velocity of motion. J Neurophysiol 66:153-169
[Abstract/Free Full Text] .- Sinclair RJ, Burton H (1993) Neuronal activity in the second somatosensory cortex of monkeys (Macaca mulatta) during active touch of gratings. J Neurophysiol 70:331-350
[Abstract/Free Full Text] .- Singer W, Gray CM (1995) Visual feature integration and the temporal correlation hypothesis. Annu Rev Neurosci 18:555-586[Web of Science][Medline].
- Softky WR, Koch C (1993) The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J Neurosci 13:334-350[Abstract].
- Steinmetz PN, Roy A, Fitzgerald PJ, Hsiao SS, Johnson KO, Niebur E (2000) Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature 404:187-190[Medline].
- Talbot WH, Darian-Smith I, Kornhuber HH, Mountcastle VB (1968) The sense of flutter vibration: comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand. J Neurophysiol 31:301-334
[Free Full Text] .- Tovee MJ, Rolls ET, Treves A, Bellis RP (1995) Information encoding and the responses of single neurons in the primate temporal visual cortex. J Neurophysiol 70:640-654
[Abstract/Free Full Text] .- Treue S, Martínez-Trujillo JC (1999) Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399:575-579[Medline].
- Treves A, Panzeri S (1995) The upward bias in measures of information derived from limited data samples. Neural Comput 7:399-407[Web of Science].
- Vallbo AB (1995) Single-afferent neurons and somatic sensation in humans. In: The cognitive neurosciences (Gazzaniga MS, ed), pp 237-252. Cambridge, MA: MIT.
- Wickersham I, Groh JM (1998) Electrically evoking sensory experience. Curr Biol 8:R412-R414[Web of Science][Medline].
- Zainos A, Merchant H, Hernández A, Salinas E, Romo R (1997) Role of primary somatic sensory cortex in the categorization of tactile stimuli: effects of lesions. Exp Brain Res 115:357-360[Web of Science][Medline].
- Zhou YD, Fuster JM (1996) Mnemonic neuronal activity in somatosensory cortex. Proc Natl Acad Sci USA 93:10533-10537
[Abstract/Free Full Text] .- Zhou YD, Fuster JM (1997) Neuronal activity of somatosensory cortex in a cross-modal (visuo-haptic) memory task. Exp Brain Res 116:551-555[Web of Science][Medline].
- Zohary E, Shadlen MN, Newsome WT (1994) Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370:140-143[Medline].
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Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models
J. Neurosci., August 15, 2000; 20(16): 6193 - 6209.
[Abstract] [Full Text] [PDF]
J. J. Hopfield and C. D. Brody
What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration
PNAS, January 30, 2001; 98(3): 1282 - 1287.
[Abstract] [Full Text] [PDF]
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