The sensation of flutter is produced when mechanical vibrations in the range of 5-50Hz are applied to the skin. A flutter stimulus activates neurons in the primary somatosensory cortex (S1) that somatotopically map to the site of stimulation. A subset of these neurons-those with quickly adapting properties, associated with Meissner's corpuscles-are strongly entrained by periodic flutter vibrations, firing with a probability that oscillates at the input frequency. Hence, quickly adapting neurons provide a dynamic representation of such flutter stimuli. However, are these neurons directly involved in the perception of flutter? Here we investigate this in monkeys trained to discriminate the difference in frequency between two flutter stimuli delivered sequentially on the fingertips. Microelectrodes were inserted into area 3b of S1 and the second stimulus was substituted with a train of injected current pulses. Animals reliably indicated whether the frequency of the second (electrical) signal was higher or lower than that of the first (mechanical) signal, even though both frequencies changed from trial to trial. Almost identical results were obtained with periodic and aperiodic stimuli of equal average frequencies. Thus, the quickly adapting neurons in area 3b activate the circuit leading to the perception of flutter. Furthermore, as far as can be psychophysically quantified during discrimination, the neural code underlying the sensation of flutter can be finely manipulated, to the extent that the behavioural responses produced by natural and artificial stimuli are indistinguishable.

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 informationThe 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). Earlier studies using vibrotactile stimuli reported four basic observations: (1) that sensation in the flutter range is mediated by primary afferent fibers and S1 neurons with rapidly adapting properties associated with Meissner's mechanoreceptors (Mountcastle et al., 1967;Talbot et al., 1968); (2) that these afferents and cortical neurons are driven very effectively by periodic flutter stimuli, which evoke highly periodic spike trains (Mountcastle et al., 1969(Mountcastle et al., , 1990Recanzone et al., 1992); (3) that psychophysical performance in frequency discrimination, which is similar for humans and monkeys (LaMotte and Mountcastle, 1975), correlates closely with the discriminability of the evoked, periodic interspike intervals (Mountcastle et al., 1969, Recanzone et al., 1992; and (4) that in afferent and S1 units, the firing rate, computed over hundreds of milliseconds, changes little within the flutter range (Talbot et al., 1968;Mountcastle et al., 1969Mountcastle et al., , 1990Recanzon...

The ventral premotor cortex (VPC) is involved in the transformation of sensory information into action, although the exact neuronal operation is not known. We addressed this problem by recording from single neurons in VPC while trained monkeys report a decision based on the comparison of two mechanical vibrations applied sequentially to the fingertips. Here we report that the activity of VPC neurons reflects current and remembered sensory inputs, their comparison, and motor commands expressing the result; that is, the entire processing cascade linking the evaluation of sensory stimuli with a motor report. These findings provide a fairly complete panorama of the neural dynamics that underlies the transformation of sensory information into an action and emphasize the role of VPC in perceptual decisions.

Perceptual decisions arise from the activity of neurons distributed across brain circuits. But, decoding the mechanisms behind this cognitive operation across brain circuits has long posed a difficult problem. We recorded the neuronal activity of diverse cortical areas, while monkeys performed a vibrotactile discrimination task. We find that the encoding of the stimuli during the stimulus periods, working memory, and comparison periods is widely distributed across cortical areas. Notably, during the comparison and postponed decision report periods the activity of frontal brain circuits encode both the result of the sensory evaluation that corresponds to the monkey's possible choices and past information on which the decision is based. These results suggest that frontal lobe circuits are more engaged in the readout of sensory information from working memory, when it is required to be compared with other sensory inputs, than simply engaged in motor responses during this task.

The ability to discriminate between two sequential stimuli requires evaluation of current sensory information in reference to stored information. Where and how does this evaluation occur? We trained monkeys to compare two mechanical vibrations applied sequentially to the fingertips and to report which of the two had the higher frequency. We recorded single neurons in secondary somatosensory cortex (S2) while the monkeys performed the task. During the first stimulus period, the firing rate of S2 neurons encoded the stimulus frequency. During the second stimulus period, however, some S2 neurons did not merely encode the stimulus frequency. The responses of these neurons were a function of both the remembered (first) and current (second) stimulus. Moreover, a few hundred milliseconds after the presentation of the second stimulus, these responses were correlated with the monkey's decision. This suggests that some S2 neurons may combine past and present sensory information for decision-making.

The events linking sensory discrimination to motor action remain unclear. It is not known, for example, whether the motor areas of the frontal lobe receive the result of the discrimination process from other areas or whether they actively participate in it. To investigate this, we trained monkeys to discriminate between two mechanical vibrations applied sequentially to the fingertips; here subjects had to recall the first vibration, compare it to the second one, and indicate with a hand/arm movement which of the two vibrations had the higher frequency. We recorded the activity of single neurons in medial premotor cortex (MPC) and found that their responses correlate with the diverse stages of the discrimination process. Thus, activity in MPC reflects the temporal evolution of the decision-making process leading to action selection during this perceptual task.

Monkeys are able to discriminate the difference in frequency between two periodic mechanical vibrations applied sequentially to the fingertips. It has been proposed that this ability is mediated by the periodicity of the responses in the quickly adapting (QA) neurons of the primary somatosensory cortex (S1), instead of the average firing rates. We recorded from QA neurons of S1 while monkeys performed the vibrotactile discrimination task. We found that the periodic mechanical vibrations can be represented both in the periodicity and in the firing rate responses to varying degrees across the QA neuronal population. We then computed neurometric functions by using both the periodicity and the firing rate and sought to determine which of these two measures is associated with the psychophysical performance. We found that neurometric thresholds based on the firing rate are very similar to the animal's psychometric thresholds whereas neurometric thresholds based on periodicity are far lower than those thresholds. These results indicate that an observer could solve this task with a precision similar to that of the monkey, based only on the firing rate produced during the stimulus periods.A fundamental issue in neurobiology is understanding precisely which component of the neuronal activity evoked by a sensory stimulus is meaningful for perception (1-3). This has been investigated in monkeys trained to discriminate among vibrotactile stimuli (4-11), and it has been proposed that an observer could solve this task by measuring the periodic, neuronal spike intervals resulting from vibrotactile stimuli, instead of the firing rate in the quickly adapting (QA) neurons of the primary somatosensory cortex (S1) (5,7,8). However, this proposal is based on the study of a small number of QA neurons of S1 (7) or on responses to a narrow range of frequencies applied to anesthetized animals (8). Recently, we observed that monkeys can discriminate the mean frequencies of aperiodic stimuli, which lack any temporal regularity (10). Psychometric thresholds for periodic and aperiodic stimulus discrimination are very similar. Additionally, animals discriminate periodic and aperiodic stimuli whether these are delivered naturally, by a mechanical probe to the fingertips, or artificially, through microinjection of electrical current frequencies into the QA circuit of S1 (10, 11). These results challenge the proposal that periodicity is the neural signal used by monkeys to perform frequency discrimination (5, 7). To investigate this issue further, we recorded from a large number of single QA neurons in S1 while monkeys performed the vibrotactile discrimination task (6, 7-11), measuring both the periodic spike intervals and the firing rate during the stimulus periods. We found that the periodic mechanical vibrations can be represented both in the periodicity and in the firing rate responses to varying degrees across the QA neuronal population. We then computed neurometric functions by using both periodicity and firing rate and sought to determine...

During a sensory discrimination task, the responses of multiple sensory neurons must be combined to generate a choice. The optimal combination of responses is determined both by their dependence on the sensory stimulus and by their cofluctuations across trials-that is, the noise correlations. Positively correlated noise is considered deleterious, because it limits the coding accuracy of populations of similarly tuned neurons. However, positively correlated fluctuations between differently tuned neurons actually increase coding accuracy, because they allow the common noise to be subtracted without signal loss. This is demonstrated with data recorded from the secondary somatosensory cortex of monkeys performing a vibrotactile discrimination task. The results indicate that positive correlations are not always harmful and may be exploited by cortical networks to enhance the neural representation of features to be discriminated.