Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans.

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.

Relating Divisive Normalization to Neuronal Response Variability.

Cortical responses to repeated presentations of a sensory stimulus are variable. This variability is sensitive to several stimulus dimensions, suggesting that it may carry useful information beyond the average firing rate. Many experimental manipulations that affect response variability are also known to engage divisive normalization, a widespread operation that describes neuronal activity as the ratio of a numerator (representing the excitatory stimulus drive) and denominator (the normalization signal). Although it has been suggested that normalization affects response variability, we lack a quantitative framework to determine the relation between the two. Here we extend the standard normalization model, by treating the numerator and the normalization signal as variable quantities. The resulting model predicts a general stabilizing effect of normalization on neuronal responses, and allows us to infer the single-trial normalization strength, a quantity that cannot be measured directly. We test the model on neuronal responses to stimuli of varying contrast, recorded in primary visual cortex of male macaques. We find that neurons that are more strongly normalized fire more reliably, and response variability and pairwise noise correlations are reduced during trials in which normalization is inferred to be strong. Our results thus suggest a novel functional role for normalization, namely, modulating response variability. Our framework could enable a direct quantification of the impact of single-trial normalization strength on the accuracy of perceptual judgments, and can be readily applied to other sensory and nonsensory factors.SIGNIFICANCE STATEMENT Divisive normalization is a widespread neural operation across sensory and nonsensory brain areas, which describes neuronal responses as the ratio between the excitatory drive to the neuron and a normalization signal. Normalization plays a key role in several important computations, including adjusting the neuron's dynamic range, reducing redundancy, and facilitating probabilistic inference. However, the relation between normalization and neuronal response variability (a fundamental aspect of neural coding) remains unclear. Here we develop a new model and test it on primary visual cortex responses. We show that normalization has a stabilizing effect on neuronal activity, beyond the known suppression of firing rate. This modulation of variability suggests a new functional role for normalization in neural coding and perception.

Visual Motion Discrimination by Propagating Patterns in Primate Cerebral Cortex.

Visual stimuli can evoke waves of neural activity that propagate across the surface of visual cortical areas. The relevance of these waves for visual processing is unknown. Here, we measured the phase and amplitude of local field potentials (LFPs) in electrode array recordings from the motion-processing medial temporal (MT) area of anesthetized male marmosets. Animals viewed grating or dot-field stimuli drifting in different directions. We found that, on individual trials, the direction of LFP wave propagation is sensitive to the direction of stimulus motion. Propagating LFP patterns are also detectable in trial-averaged activity, but the trial-averaged patterns exhibit different dynamics and behaviors from those in single trials and are similar across motion directions. We show that this difference arises because stimulus-sensitive propagating patterns are present in the phase of single-trial oscillations, whereas the trial-averaged signal is dominated by additive amplitude effects. Our results demonstrate that propagating LFP patterns can represent sensory inputs at timescales relevant to visually guided behaviors and raise the possibility that propagating activity patterns serve neural information processing in area MT and other cortical areas.SIGNIFICANCE STATEMENT Propagating wave patterns are widely observed in the cortex, but their functional relevance remains unknown. We show here that visual stimuli generate propagating wave patterns in local field potentials (LFPs) in a movement-sensitive area of the primate cortex and that the propagation direction of these patterns is sensitive to stimulus motion direction. We also show that averaging LFP signals across multiple stimulus presentations (trial averaging) yields propagating patterns that capture different dynamic properties of the LFP response and show negligible direction sensitivity. Our results demonstrate that sensory stimuli can modulate propagating wave patterns reliably in the cortex. The relevant dynamics are normally masked by trial averaging, which is a conventional step in LFP signal processing.

Spectral Signatures of Feedforward and Recurrent Circuitry in Monkey Area MT.

Recordings of local field potential (LFP) in the visual cortex can show rhythmic activity at gamma frequencies (30-100 Hz). While the gamma rhythms in the primary visual cortex have been well studied, the structural and functional characteristics of gamma rhythms in extrastriate visual cortex are less clear. Here, we studied the spatial distribution and functional specificity of gamma rhythms in extrastriate middle temporal (MT) area of visual cortex in marmoset monkeys. We found that moving gratings induced narrowband gamma rhythms across cortical layers that were coherent across much of area MT. Moving dot fields instead induced a broadband increase in LFP in middle and upper layers, with weaker narrowband gamma rhythms in deeper layers. The stimulus dependence of LFP response in middle and upper layers of area MT appears to reflect the presence (gratings) or absence (dot fields and other textures) of strongly oriented contours. Our results suggest that gamma rhythms in these layers are propagated from earlier visual cortex, while those in the deeper layers may emerge in area MT.

Emergence of complex wave patterns in primate cerebral cortex.

Slow brain rhythms are attributed to near-simultaneous (synchronous) changes in activity in neuron populations in the brain. Because they are slow and widespread, synchronous rhythms have not been considered crucial for information processing in the waking state. Here we adapted methods from turbulence physics to analyze δ-band (1-4 Hz) rhythms in local field potential (LFP) activity, in multielectrode recordings from cerebral cortex in anesthetized marmoset monkeys. We found that synchrony contributes only a small fraction (less than one-fourth) to the local spatiotemporal structure of δ-band signals. Rather, δ-band activity is dominated by propagating plane waves and spatiotemporal structures, which we call complex waves. Complex waves are manifest at submillimeter spatial scales, and millisecond-range temporal scales. We show that complex waves can be characterized by their relation to phase singularities within local nerve cell networks. We validate the biological relevance of complex waves by showing that nerve cell spike rates are higher in presence of complex waves than in the presence of synchrony and that there are nonrandom patterns of evolution from one type of complex wave to another. We conclude that slow brain rhythms predominantly indicate spatiotemporally organized activity in local nerve cell circuits, not synchronous activity within and across brain regions.

Local and Global Correlations between Neurons in the Middle Temporal Area of Primate Visual Cortex.

In humans and other primates, the analysis of visual motion includes populations of neurons in the middle-temporal (MT) area of visual cortex. Motion analysis will be constrained by the structure of neural correlations in these populations. Here, we use multi-electrode arrays to measure correlations in anesthetized marmoset, a New World monkey where area MT lies exposed on the cortical surface. We measured correlations in the spike count between pairs of neurons and within populations of neurons, for moving dot fields and moving gratings. Correlations were weaker in area MT than in area V1. The magnitude of correlations in area MT diminished with distance between receptive fields, and difference in preferred direction. Correlations during presentation of moving gratings were stronger than those during presentation of moving dot fields, extended further across cortex, and were less dependent on the functional properties of neurons. Analysis of the timescales of correlation suggests presence of 2 mechanisms. A local mechanism, associated with near-synchronous spiking activity, is strongest in nearby neurons with similar direction preference and is independent of visual stimulus. A global mechanism, operating over larger spatial scales and longer timescales, is independent of direction preference and is modulated by the type of visual stimulus presented.

Integration and segregation of multiple motion signals by neurons in area MT of primate.

We used multielectrode arrays to measure the response of populations of neurons in primate middle temporal area to the transparent motion of two superimposed dot fields moving in different directions. The shape of the population response was well predicted by the sum of the responses to the constituent fields. However, the population response profile for transparent dot fields was similar to that for coherent plaid motion and hence an unreliable cue to transparency. We then used single-unit recording to characterize component and pattern cells from their response to drifting plaids. Unlike for plaids, component cells responded to the average direction of superimposed dot fields, whereas pattern cells could signal the constituent motions. This observation provides support for a strong prediction of the Simoncelli and Heeger (1998) model of motion analysis in area middle temporal, and suggests that pattern cells have a special status in the processing of superimposed dot fields.

Texture-dependent motion signals in primate middle temporal area.

Neurons in the middle temporal (MT) area of primate cortex provide an important stage in the analysis of visual motion. For simple stimuli such as bars and plaids some neurons in area MT--pattern cells--seem to signal motion independent of contour orientation, but many neurons--component cells--do not. Why area MT supports both types of receptive field is unclear. To address this we made extracellular recordings from single units in area MT of anaesthetised marmoset monkeys and examined responses to two-dimensional images with a large range of orientations and spatial frequencies. Component and pattern cell response remained distinct during presentation of these complex spatial textures. Direction tuning curves were sharpest in component cells when a texture contained a narrow range of orientations, but were similar across all neurons for textures containing all orientations. Response magnitude of pattern cells, but not component cells, increased with the spatial bandwidth of the texture. In addition, response variability in all neurons was reduced when the stimulus was rich in spatial texture. Fisher information analysis showed that component cells provide more informative responses than pattern cells when a texture contains a narrow range of orientations, but pattern cells had more informative responses for broadband textures. Component cells and pattern cells may therefore coexist because they provide complementary and parallel motion signals.

Visual motion integration by neurons in the middle temporal area of a New World monkey, the marmoset.

The middle temporal area (MT/V5) is an anatomically distinct region of primate visual cortex that is specialized for the processing of image motion. It is generally thought that some neurons in area MT are capable of signalling the motion of complex patterns, but this has only been established in the macaque monkey. We made extracellular recordings from single units in area MT of anaesthetized marmosets, a New World monkey. We show through quantitative analyses that some neurons (35 of 185; 19%) are capable of signalling pattern motion ('pattern cells'). Across several dimensions, the visual response of pattern cells in marmosets is indistinguishable from that of pattern cells in macaques. Other neurons respond to the motion of oriented contours in a pattern ('component cells') or show intermediate properties. In addition, we encountered a subset of neurons (22 of 185; 12%) insensitive to sinusoidal gratings but very responsive to plaids and other two-dimensional patterns and otherwise indistinguishable from pattern cells. We compared the response of each cell class to drifting gratings and dot fields. In pattern cells, directional selectivity was similar for gratings and dot fields; in component cells, directional selectivity was weaker for dot fields than gratings. Pattern cells were more likely to have stronger suppressive surrounds, prefer lower spatial frequencies and prefer higher speeds than component cells. We conclude that pattern motion sensitivity is a feature of some neurons in area MT of both New and Old World monkeys, suggesting that this functional property is an important stage in motion analysis and is likely to be conserved in humans.