Timing and neural encoding of somatosensory parametric working memory in macaque prefrontal cortex

We trained monkeys to compare the frequencies of two mechanical vibrations applied sequentially to the tip of a finger and to report which of the two stimuli had the higher frequency. This task requires remembering the first frequency during the delay period between the two stimuli. Recordings were made from neurons in the inferior convexity of the prefrontal cortex (PFC) while the monkeys performed the task. We report neurons that fire persistently during the delay period, with a firing rate that is a monotonic function of the frequency of the first stimulus. Separately from, and in addition to, their correlation with the first stimulus, the delay period firing rates of these neurons were correlated with the behavior of the monkey, in a manner consistent with their interpretation as the neural substrate of working memory during the task. Most neurons had firing rates that varied systematically with time during the delay period. We suggest that this time-dependent activity may encode time itself and may be an intrinsic part of active memory maintenance mechanisms

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

The problem of neural coding in perceptual decision making revolves around two fundamental questions: (i) How are the neural representations of sensory stimuli related to perception, and (ii) what attributes of these neural responses are relevant for downstream networks, and how do they influence decision making? We studied these two questions by recording neurons in primary somatosensory (S1) and dorsal premotor (DPC) cortex while trained monkeys reported whether the temporal pattern structure of two sequential vibrotactile stimuli (of equal mean frequency) was the same or different. We found that S1 neurons coded the temporal patterns in a literal way and only during the stimulation periods and did not reflect the monkeys' decisions. In contrast, DPC neurons coded the stimulus patterns as broader categories and signaled them during the working memory, comparison, and decision periods. These results show that the initial sensory representation is transformed into an intermediate, more abstract categorical code that combines past and present information to ultimately generate a perceptually informed choiceThis work was supported in part by the Dirección de Asuntos del Personal Académico de la Universidad Nacional Autónoma de México and Consejo Nacional de Ciencia y Tecnología (R.R.) and Grant FIS2015-67876-P (to N.P.

Exploring the cortical evidence of a sensory-discrimination process.

Humans and monkeys have similar abilities to discriminate the difference in frequency between two consecutive mechanical vibrations applied to their fingertips. This task can be conceived as a chain of neural operations: encoding the two consecutive stimuli, maintaining the first stimulus in working memory, comparing the second stimulus with the memory trace left by the first stimulus and communicating the result of the comparison to the motor apparatus. We studied this chain of neural operations by recording and manipulating neurons from different areas of the cerebral cortex while monkeys performed the task. The results indicate that neurons of the primary somatosensory cortex (S1) generate a neural representation of vibrotactile stimuli which correlates closely with psychophysical performance. Discrimination based on microstimulation patterns injected into clusters of S1 neurons is indistinguishable from that produced by natural stimuli. Neurons from the secondary somatosensory cortex (S2), prefrontal cortex and medial premotor cortex (MPC) display at different times the trace of the first stimulus during the working-memory component of the task. Neurons from S2 and MPC appear to show the comparison between the two stimuli and correlate with the behavioural decisions. These neural operations may contribute to the sensory-discrimination process studied here

Procedure for recording the simultaneous activity of single neurons distributed across cortical areas during sensory discrimination

We report a procedure for recording the simultaneous activity of single neurons distributed across five cortical areas in behaving monkeys. The procedure consists of a commercially available microdrive adapted to a commercially available neural data collection system. The critical advantage of this procedure is that, in each cortical area, a configuration of seven microelectrodes spaced 250–500 μm can be inserted transdurally and each can be moved independently in the z axis. For each microelectrode, the data collection system can record the activity of up to five neurons together with the local field potential (LFP). With this procedure, we normally monitor the simultaneous activity of 70–100 neurons while trained monkeys discriminate the difference in frequency between two vibrotactile stimuli. Approximately 20–60 of these neurons have response properties previously reported in this task. The neuronal recordings show good signal-to-noise ratio, are remarkably stable along a 1-day session, and allow testing several protocols. Microelectrodes are removed from the brain after a 1-day recording session, but are reinserted again the next day by using the same or different x-y microelectrode array configurations. The fact that microelectrodes can be moved in the z axis during the recording session and that the x-y configuration can be changed from day to day maximizes the probability of studying simultaneous interactions, both local and across distant cortical areas, between neurons associated with the different components of this task

Neural correlates of a postponed decision report

Depending on environmental demands, a decision based on a sensory evaluation may be either immediately reported or postponed for later report. If postponed, the decision must be held in memory. But what exactly is stored by the underlying memory circuits, the final decision itself or the sensory information that led to it? Here, we report that, during a postponed decision report period, the activity of medial premotor cortex neurons encodes both the result of the sensory evaluation that corresponds to the monkey's possible choices and past sensory information on which the decision is based. These responses could switch back and forth with remarkable flexibility across the postponed decision report period. Moreover, these responses covaried with the animal's decision report. We propose that maintaining in working memory the original stimulus information on which the decision is based could serve to continuously update the postponed decision report in this task

Neuron 83, this issue

found in this control experiment: an offset in the motion coherence of the real stimulus biases the monkeys' choices and confidence ratings just like microstimulation did. In an elegant control experiment, Fetsch et al. (2014) sought to break the system apart. Instead of using low currents to stimulate a small patch of neurons with similar preferred orientations, the authors now injected a large amount of current that recruited a wider population of neurons including disparate preferred motion directions. This widespread activation resulted in a large increase in the number of sure bet choices, indicating that monkeys experienced noisy motion information and less confident decisions. The result illustrates at least two important issues. First, it demonstrates that monkeys are capable of reporting a large decrease in confidence and, second, it shows that the behavioral consequences of microstimulation are exquisitely dependent on the selectivity of the stimulated neurons. Large stimulation currents, instead of injecting additional information, indiscriminately recruit neuronal populations whose contributions can mask subtle sensory representations. The results reported by Fetsch et al. (2014) demonstrate that the mechanisms that read sensory evidence have access to the additional information added by microstimulation at the level of MT/MST. Future experiments should be aimed to identify the downstream neuronal circuits that read this evidence to decide whether to choose a safe bet or to risk for a larger reward. Importantly, these circuits must have learned, during behavioral training, the association between the amount of accumulated evidence and the likelihood that a given answer will be correct. What are the neuronal correlates of this learning? The answer will likely include the orchestrating functions of the frontal cortices, and also the modulatory effects of subcortical projection systems (de Lafuente and Romo, 2011; Schultz, 2013)