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.
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 task-dependent 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.
Midbrain dopamine (DA) neurons respond to sensory stimuli associated with future rewards. When reward is delivered probabilistically, DA neurons reflect this uncertainty by increasing their firing ...rates in a period between the sensory cue and reward delivery time. Probability of reward, however, has been externally conveyed by visual cues, and it is not known whether DA neurons would signal uncertainty arising internally. Here we show that DA neurons code the uncertainty associated with a perceptual judgment about the presence or absence of a vibrotactile stimulus. We observed that uncertainty modulates the activity elicited by a go cue instructing monkey subjects to communicate their decisions. That is, the same go cue generates different DA responses depending on the uncertainty level of a judgment made a few seconds before the go instruction. Easily detected suprathreshold stimuli elicit small DA responses, indicating that future reward will not be a surprising event. In contrast, the absence of a sensory stimulus generates large DA responses associated with uncertainty: was the stimulus truly absent, or did a low-amplitude vibration go undetected? In addition, the responses of DA neurons to the stimulus itself increase with vibration amplitude, but only when monkeys correctly detect its presence. This finding suggests that DA activity is not related to actual intensity but rather to perceived intensity. Therefore, in addition to their well-known role in reward prediction, DA neurons code subjective sensory experience and uncertainty arising internally from perceptual decisions.
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. On the basis of our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
When a sensory stimulus is presented, many cortical areas are activated, but how does the representation of a sensory stimulus evolve in time and across cortical areas during a perceptual judgment? ...We investigated this question by analyzing the responses from single neurons, recorded in several cortical areas of parietal and frontal lobes, while trained monkeys reported the presence or absence of a mechanical vibration of varying amplitude applied to the skin of one fingertip. Here we show that the strength of the covariations between neuronal activity and perceptual judgments progressively increases across cortical areas as the activity is transmitted from the primary somatosensory cortex to the premotor areas of the frontal lobe. This finding suggests that the neuronal correlates of subjective sensory experience gradually build up across somatosensory areas of the parietal lobe and premotor cortices of the frontal lobe.
A crucial role of cortical networks is the conversion of sensory inputs into perception. In the cortical somatosensory network, neurons of the primary somatosensory cortex (S1) show invariant sensory ...responses, while frontal lobe neuronal activity correlates with the animal's perceptual behavior. Here, we report that in the secondary somatosensory cortex (S2), neurons with invariant sensory responses coexist with neurons whose responses correlate with perceptual behavior. Importantly, the vast majority of the neurons fall along a continuum of combined sensory and categorical dynamics. Furthermore, during a non-demanding control task, the sensory responses remain unaltered while the sensory information exhibits an increase. However, perceptual responses and the associated categorical information decrease, implicating a task context-dependent processing mechanism. Conclusively, S2 neurons exhibit intriguing dynamics that are intermediate between those of S1 and frontal lobe. Our results contribute relevant evidence about the role that S2 plays in the conversion of touch into perception.
Coherent oscillations in the theta-to-gamma frequency range have been proposed as a mechanism that coordinates neural activity in large-scale cortical networks in sensory, motor, and cognitive tasks. ...Whether this mechanism also involves coherent oscillations at delta frequencies (1–4 Hz) is not known. Rather, delta oscillations have been associated with slow-wave sleep. Here, we show coherent oscillations in the delta frequency band between parietal and frontal cortices during the decision-making component of a somatosensory discrimination task. Importantly, the magnitude of this delta-band coherence is modulated by the different decision alternatives. Furthermore, during control conditions not requiring decision making, delta-band coherences are typically much reduced. Our work indicates an important role for synchronous activity in the delta frequency band when large-scale, distant cortical networks coordinate their neural activity during decision making.
The relatively random spiking times of individual neurons are a source of noise in the brain. We show that in a finite-sized cortical attractor network, this can be an advantage, for it leads to ...probabilistic behavior that is advantageous in decision-making, by preventing deadlock, and is important in signal detectability. We show how computations can be performed through stochastic dynamical effects, including the role of noise in enabling probabilistic jumping across barriers in the energy landscape describing the flow of the dynamics in attractor networks. The results obtained in neurophysiological studies of decision-making and signal detectability are modelled by the stochastical neurodynamics of integrate-and-fire networks of neurons with probabilistic neuronal spiking. We describe how these stochastic neurodynamical effects can be analyzed, and their importance in many aspects of brain function, including decision-making, memory recall, short-term memory, and attention.
Working memory is a crucial component of most cognitive tasks. Its neuronal mechanisms are still unclear despite intensive experimental and theoretical explorations. Most theoretical models of ...working memory assume both time-invariant neural representations and precise connectivity schemes based on the tuning properties of network neurons. A different, more recent class of models assumes randomly connected neurons that have no tuning to any particular task, and bases task performance purely on adjustment of network readout. Intermediate between these schemes are networks that start out random but are trained by a learning scheme. Experimental studies of a delayed vibrotactile discrimination task indicate that some of the neurons in prefrontal cortex are persistently tuned to the frequency of a remembered stimulus, but the majority exhibit more complex relationships to the stimulus that vary considerably across time. We compare three models, ranging from a highly organized line attractor model to a randomly connected network with chaotic activity, with data recorded during this task. The random network does a surprisingly good job of both performing the task and matching certain aspects of the data. The intermediate model, in which an initially random network is partially trained to perform the working memory task by tuning its recurrent and readout connections, provides a better description, although none of the models matches all features of the data. Our results suggest that prefrontal networks may begin in a random state relative to the task and initially rely on modified readout for task performance. With further training, however, more tuned neurons with less time-varying responses should emerge as the networks become more structured.
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