Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of intrinsic fluctuations in spiking activity across areas, and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. Based on our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the taskdependent features of the population response in a single figure.

Extensive work in humans using magneto-and electroencephalography strongly suggests that decreased oscillatory α-activity (8-14 Hz) facilitates processing in a given region, whereas increased α-activity serves to actively suppress irrelevant or interfering processing. However, little work has been done to understand how α-activity is linked to neuronal firing. Here, we simultaneously recorded local field potentials and spikes from somatosensory, premotor, and motor regions while a trained monkey performed a vibrotactile discrimination task. In the local field potentials we observed strong activity in the α-band, which decreased in the sensorimotor regions during the discrimination task. This α-power decrease predicted better discrimination performance. Furthermore, the α-oscillations demonstrated a rhythmic relation with the spiking, such that firing was highest at the trough of the α-cycle. Firing rates increased with a decrease in α-power. These findings suggest that α-oscillations exercise a strong inhibitory influence on both spike timing and firing rate. Thus, the pulsed inhibition by α-oscillations plays an important functional role in the extended sensorimotor system.T he prominent posterior α-rhythm (8-14 Hz) was first described by Hans Berger (1) and long considered to reflect cortical idling (2, 3). To a large extent, the α-rhythm has been ignored by animal neurophysiologists (but see ref. 4) and considered to be of little functional relevance. Thus, it remains largely unknown how ongoing α-oscillations relate to neuronal firing.In contrast to the idling hypothesis, converging electrophysiological evidence in humans suggests that α-oscillations play an important functional role in cognitive processing (5-7). In particular, α-activity might serve to shape the state of sensory brain regions to direct the flow of information and optimize performance (8). In support of this idea, several studies on visual perception have shown that anticipatory α-activity reflects the orienting of attention (9-14) and influences detection performance (15-17). Recently, it was shown that the functionality of α-oscillations can be generalized to the somatosensory system (18-21). Furthermore, α-activity has been implicated in visual (22-25), auditory (26), and somatosensory working-memory performance (27).These studies strongly suggest that decreased α-activity facilitates processing in task-relevant brain regions, whereas increased α-activity functions to suppress distracting input in task-irrelevant regions. However, given the strong oscillatory nature of the α-activity, it is less clear how it influences processing in a phasic manner. It has been suggested that α-oscillations serve to depress processing every ∼100 ms by a mechanism of pulsed inhibition (5,(28)(29)(30). In support of this notion, it has recently been demonstrated that perception is modulated by the prestimulus phase of the α-rhythm (31, 32). Likewise, it was recently shown that the magnitude of the blood-oxygen level-dependent signal in response to a visual...

Humans and monkeys have similar abilities to discriminate the difference in frequency between two mechanical vibrations applied sequentially to the fingertips. A key component of this sensory task is that the second stimulus is compared with the trace left by the first (base) stimulus, which must involve working memory. Where and how is this trace held in the brain? This question was investigated by recording from single neurons in the prefrontal cortex of monkeys while they performed the somatosensory discrimination task. Here we describe neurons in the inferior convexity of the prefrontal cortex whose discharge rates varied, during the delay period between the two stimuli, as a monotonic function of the base stimulus frequency. We describe this as 'monotonic stimulus encoding', and we suggest that the result may generalize: monotonic stimulus encoding may be the basic representation of one-dimensional sensory stimulus quantities in working memory. Thus we predict that other behavioural tasks that require ordinal comparisons between scalar analogue stimuli would give rise to monotonic responses similar to those reported here.

Networks adapt to environmental demands by switching between distinct dynamical behaviors. The activity of frontal-lobe neurons during two-interval discrimination tasks is an example of these adaptable dynamics. Subjects first perceive a stimulus, then hold it in working memory, and finally make a decision by comparing it with a second stimulus. We present a simple mutual-inhibition network model that captures all three task phases within a single framework. The model integrates both working memory and decision making because its dynamical properties are easily controlled without changing its connectivity. Mutual inhibition between nonlinear units is a useful design motif for networks that must display multiple behaviors.

Phantom perception refers to the conscious awareness of a percept in the absence of an external stimulus. On the basis of basic neuroscience on perception and clinical research in phantom pain and phantom sound, we propose a working model for their origin. Sensory deafferentation results in high-frequency, gamma band, synchronized neuronal activity in the sensory cortex. This activity becomes a conscious percept only if it is connected to larger coactivated "(self-)awareness" and "salience" brain networks. Through the involvement of learning mechanisms, the phantom percept becomes associated to distress, which in turn is reflected by a simultaneously coactivated nonspecific distress network consisting of the anterior cingulate cortex, anterior insula, and amygdala. Memory mechanisms play a role in the persistence of the awareness of the phantom percept, as well as in the reinforcement of the associated distress. Thus, different dynamic overlapping brain networks should be considered as targets for the treatment of this disorder.A fundamental concept in psychology and philosophy of the mind is the notion of perception: The act of interpreting and organizing a sensory stimulus to produce a meaningful experience of the world and of oneself. A stimulus produces an effect on the different sensory receptors, inducing sensation. Further processing of this sensory stimulation generates an internal representation of the outer and inner world called a percept. Since the first days of psychology, two challenging questions have existed: How is sensory information encoded and, in particular, how is this represented information transformed into the individual awareness of a conscious percept (1)? Our understanding of sensory encoding, perception, and consciousness is challenged with a further degree of complexity in the case of phantom perception, the conscious awareness of a percept in the absence of an external stimulus. Deciphering the underlying neural correlates of phantom perception is a scientific endeavor that will aid in understanding the active processes of selecting, organizing, and interpreting information, which ultimately lead to the formation of a conscious percept within the brain.Although some cases of phantom percepts have been described for the visual, olfactory, and gustatory systems, the vast majority of sensory phantoms are those present in the somatosensory (phantom limb perception/phantom limb pain and neuropathic pain) (2) and auditory (tinnitus) (3) modalities. Upfront we are challenged with the following questions: In the absence of an external sensory stimulus, where and how in the brain is the conscious percept generated? In addition, are the neural substrates underlying the generation of a conscious phantom percept similar for the auditory and somatosensory modalities? If so, can we advance in our understanding and treatment of tinnitus on the basis of what is known for phantom limb and phantom pain perception and vice versa? Here, we address these questions and propose a working model of how pha...

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.working memory | prefrontal cortex | population coding T he neuronal basis of working memory (WM) in prefrontal cortex (PFC) has been studied for decades through singleneuron recordings from monkeys performing tasks in which a transient sensory stimulus must be held in WM across a secondslong delay to guide a future response. These studies discovered that a key neural correlate of WM in PFC is stimulus-selective persistent activity, i.e., stable elevated firing rates in a subset of neurons, that spans the delay (1). These neurophysiological findings have grounded a leading hypothesis that WM is supported by stable persistent activity patterns in PFC that bridge the gap between stimulus and response epochs. Because the timescales of WM maintenance (several seconds) are longer than typical timescales of neuronal and synaptic integration (∼10-100 ms), mechanisms at the level of neural circuits may be critical for generating WM activity in PFC (2). A leading theoretical framework proposes that PFC circuits subserve WM maintenance through dynamical attractors, i.e., stable fixed points in network activity, generated by strong recurrent connectivity (3, 4).Recent neurophysiologi...

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.