Phase of neural oscillations as a reference frame for attention-based routing in visual cortex

Selective attention allows the brain to efficiently process the image projected onto the retina, selectively focusing neural processing resources on behaviorally relevant visual information. While previous studies have documented the crucial role of the action potential rate of single neurons in relaying such information, little is known about how the activity of single neurons relative to their neighboring network contributes to the efficient representation of attended stimuli and transmission of this information to downstream areas. Here, we show in the dorsal visual pathway of monkeys (medial superior temporal (MST) area) that neurons fire spikes preferentially at a specific phase of the ongoing population beta (~20 Hz) oscillations of the surrounding local network. This preferred spiking phase shifts towards a later phase when monkeys selectively attend towards (rather than away from) the receptive field of the neuron. This shift of the locking phase is positively correlated with the speed at which animals report a visual change. Furthermore, our computational modelling suggests that neural networks can manipulate the preferred phase of coupling by imposing differential synaptic delays on postsynaptic potentials. This distinction between the locking phase of neurons activated by the spatially attended stimulus vs. that of neurons activated by the unattended stimulus, may enable the neural system to discriminate relevant from irrelevant sensory inputs and consequently filter out distracting stimuli information by aligning the spikes which convey relevant/irrelevant information to distinct phases linked to periods of better/worse perceptual sensitivity for higher cortices. This strategy may be used to reserve the narrow windows of highest perceptual efficacy to the processing of the most behaviorally relevant information, ensuring highly efficient responses to attended sensory events.

How multisensory neurons solve causal inference

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction (“congruent” neurons), while others prefer opposing directions (“opposite” neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.

Primate Extrastriate Cortical Area MST: A Gateway between Sensation and Cognition

Primate visual cortex consists of dozens of distinct brain areas, each providing a highly specialized component to the sophisticated task of encoding the incoming sensory information and creating a representation of our visual environment that underlies our perception and action. One such area is the medial superior temporal cortex (MST), a motion-sensitive, direction-selective part of the primate visual cortex. It receives most of its input from the middle temporal (MT) area, but MST cells have larger receptive fields and respond to more complex motion patterns. The finding that MST cells are tuned for optic flow patterns has led to the suggestion that the area plays an important role in the perception of self-motion. This hypothesis has received further support from studies showing that some MST cells also respond selectively to vestibular cues. Furthermore, the area is part of a network that controls the planning and execution of smooth pursuit eye movements and its activity is modulated by cognitive factors, such as attention and working memory. This review of more than 90 studies focuses on providing clarity of the heterogeneous findings on MST in the macaque cortex and its putative homolog in the human cortex. From this analysis of the unique anatomical and functional position in the hierarchy of areas and processing steps in primate visual cortex, MST emerges as a gateway between perception, cognition, and action planning. Given this pivotal role, this area represents an ideal model system for the transition from sensation to cognition.

A spiking neural circuit model for learning multi-sensory integration

Neurons in visual and vestibular information integration areas of macaque brain such as medial superior temporal (MSTd) and ventral intraparietal (VIP) have been classified into congruent neurons and opposite neurons, which prefer congruent inputs and opposite inputs from the two sensory modalities, respectively. In this work, we propose a mechanistic spiking neural model that can account for the emergence of congruent and opposite neurons and their interactions in a neural circuit for multi-sensory integration. The spiking neural circuit model is adopted from an established model for the circuits of the primary visual cortex with little changes in parameters. The network can learn, based on the basic Hebbian learning principle, the correct topological organization and behaviors of the congruent and opposite neurons that have been proposed to play a role in multi-sensory integration. This work explore the constraints and the conditions that lead to the development of a proposed neural circuit for cue integration. It also demonstrates that such neural circuit might indeed be a canonical circuit shared by computations in many cortical areas.

Distributed Visual Category Processing Across Medial Superior Temporal and Lateral Intraparietal Cortices

Categorization is an essential cognitive and perceptual process for recognition and decision making. The posterior parietal cortex (PPC), particularly the lateral intraparietal (LIP) area has been suggested to transform visual feature encoding into cognitive or abstract category representations. By contrast, areas closer to sensory input, such as the middle temporal (MT) area, encode stimulus features but not more abstract categorical information during categorization tasks. Here, we compare the contributions of PPC subregions in category computation by recording neuronal activity in the medial superior temporal (MST) and LIP areas during a categorization task. MST is a core motion processing area interconnected with MT, and often considered an intermediate processing stage between MT and LIP. Here we show that MST shows robust decision-correlated category encoding and working memory encoding similar to LIP, suggesting that MST plays a substantial role in cognitive computation, extending beyond its widely recognized role in visual motion processing.

Changes in the Proportion of Inhibitory Interneuron Types from Sensory to Executive Areas of the Primate Neocortex: Implications for the Origins of Working Memory Representations

Neuronal spiking activity encoding working memory (WM) is robust in primate association cortices but weak or absent in early sensory cortices. This may be linked to changes in the proportion of neuronal types across areas that influence circuits’ ability to generate recurrent excitation. We recorded neuronal activity from areas middle temporal (MT), medial superior temporal (MST), and the lateral prefrontal cortex (LPFC) of monkeys performing a WM task and classified neurons as narrow (NS) and broad spiking (BS). The ratio NS/BS decreased from MT > MST > LPFC. We analyzed the Allen Institute database of ex vivo mice/human intracellular recordings to interpret our data. Our analysis suggests that NS neurons correspond to parvalbumin (PV) or somatostatin (SST) interneurons while BS neurons are pyramidal (P) cells or vasoactive intestinal peptide (VIP) interneurons. We labeled neurons in monkey tissue sections of MT/MST and LPFC and found that the proportion of PV in cortical layers 2/3 decreased, while the proportion of CR cells increased from MT/MST to LPFC. Assuming that primate CR/CB/PV cells perform similar computations as mice VIP/SST/PV cells, our results suggest that changes in the proportion of CR and PV neurons in layers 2/3 cells may favor the emergence of activity encoding WM in association areas.

A Causal Role of Area hMST for Self-Motion Perception in Humans

Previous studies in the macaque monkey have provided clear causal evidence for an involvement of the medial-superior-temporal area (MST) in the perception of self-motion. These studies also revealed an overrepresentation of contraversive heading. Human imaging studies have identified a functional equivalent (hMST) of macaque area MST. Yet, causal evidence of hMST in heading perception is lacking. We employed neuronavigated transcranial magnetic stimulation (TMS) to test for such a causal relationship. We expected TMS over hMST to induce increased perceptual variance (i.e., impaired precision), while leaving mean heading perception (accuracy) unaffected. We presented 8 human participants with an optic flow stimulus simulating forward self-motion across a ground plane in one of 3 directions. Participants indicated perceived heading. In 57% of the trials, TMS pulses were applied, temporally centered on self-motion onset. TMS stimulation site was either right-hemisphere hMST, identified by a functional magnetic resonance imaging (fMRI) localizer, or a control-area, just outside the fMRI localizer activation. As predicted, TMS over area hMST, but not over the control-area, increased response variance of perceived heading as compared with noTMS stimulation trials. As hypothesized, this effect was strongest for contraversive self-motion. These data provide a first causal evidence for a critical role of hMST in visually guided navigation.

Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

Multisensory integration areas such as dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas in macaques combine visual and vestibular cues to produce better estimates of self-motion. Congruent and opposite neurons, two types of neurons found in these areas, prefer congruent inputs and opposite inputs from the two modalities, respectively. A recently proposed computational model of congruent and opposite neurons reproduces their tuning properties and shows that congruent neurons optimally integrate information while opposite neurons compute disparity information. However, the connections in the network are fixed rather than learned, and in fact the connections of opposite neurons, as we will show, cannot arise from Hebbian learning rules. We therefore propose a new model of multisensory integration in which congruent neurons and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, and show that these neurons exhibit experimentally observed tuning properties.

Emergence of opposite neurons in a firing-rate model of multisensory integration

ABSTRACTOpposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.