Significance: We perceive our environment through multiple independent sources of sensory input. The brain is tasked with deciding whether multiple signals are produced by the same or different events (i.e., solve the problem of causal inference). Here, we train a neural network to solve causal inference by either combining or separating visual and vestibular inputs in order to estimate self- and scene motion. We find that the network recapitulates key neurophysiological (i.e., congruent and opposite neurons) and behavioral (e.g., reliability-based cue weighting) properties of biological systems. We show how congruent and opposite neurons support motion estimation and how the balance in activity between these subpopulations determines whether to combine or separate multisensory signals.

Perception is often modelled as a process of active inference, whereby prior expectations are combined with noisy sensory measurements to estimate the structure of the world. This mathematical framework has proven critical to understanding perception, cognition, motor control, and social interaction. While theoretical work has shown how priors can be computed from environmental statistics, their neural instantiation could be realised through multiple competing encoding schemes. Using a data-driven approach, here we extract the brain’s representation of visual orientation and compare this with simulations from different sensory coding schemes. We found that the tuning of the human visual system is highly conditional on stimulus-specific variations in a way that is not predicted by previous proposals. We further show that the adopted encoding scheme effectively embeds an environmental prior for natural image statistics within the sensory measurement, providing the functional architecture necessary for optimal inference in the earliest stages of cortical processing.

Edited MRS sequences are widely used for studying γ‐aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA‐edited (GABA+, TE = 68 ms MEGA‐PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL‐MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor‐specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single‐rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single‐rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.

A canonical feature of sensory systems is that they adapt to prolonged or repeated inputs, suggesting the brain encodes the temporal context in which stimuli are embedded. Sensory adaptation has been observed in the central nervous systems of many animal species, using techniques sensitive to a broad range of spatiotemporal scales of neural activity. Two competing models have been proposed to account for the phenomenon. One assumes that adaptation reflects reduced neuronal sensitivity to sensory inputs over time (the “fatigue” account); the other posits that adaptation arises due to increased neuronal selectivity (the “sharpening” account). To adjudicate between these accounts, we exploited the well-known “tilt aftereffect”, which reflects adaptation to orientation information in visual stimuli. We recorded whole-brain activity with millisecond precision from human observers as they viewed oriented gratings before and after adaptation, and used inverted encoding modeling to characterize feature-specific neural responses. We found that both fatigue and sharpening mechanisms contribute to the tilt aftereffect, but that they operate at different points in the sensory processing cascade to produce qualitatively distinct outcomes. Specifically, fatigue operates during the initial stages of processing, consistent with tonic inhibition of feedforward responses, whereas sharpening occurs ~200 ms later, consistent with feedback or local recurrent activity. Our findings reconcile two major accounts of sensory adaptation, and reveal how this canonical process optimizes the detection of change in sensory inputs through efficient neural coding.

Seeing movement promotes survival. It results from an uncertain interplay between evolution and experience, making it hard to isolate the drivers of computational architectures found in brains. Here we seek insight into motion perception using a neural network (MotionNet) trained on moving images to classify velocity. The network recapitulates key properties of motion direction and speed processing in biological brains, and we use it to derive, and test, understanding of motion (mis)perception at the computational, neural, and perceptual levels. We show that diverse motion characteristics are largely explained by the statistical structure of natural images, rather than motion per se. First, we show how neural and perceptual biases for particular motion directions can result from the orientation structure of natural images. Second, we demonstrate an interrelation between speed and direction preferences in (macaque) MT neurons that can be explained by image autocorrelation. Third, we show that natural image statistics mean that speed and image contrast are related quantities. Finally, using behavioral tests (humans, both sexes), we show that it is knowledge of the speed-contrast association that accounts for motion illusions, rather than the distribution of movements in the environment (the “slow world” prior) as premised by Bayesian accounts. Together, this provides an exposition of motion speed and direction estimation, and produces concrete predictions for future neurophysiological experiments. More broadly, we demonstrate the conceptual value of marrying artificial systems with biological characterization, moving beyond “black box” reproduction of an architecture to advance understanding of complex systems, such as the brain.SIGNIFICANCE STATEMENTUsing an artificial systems approach, we show that physiological properties of motion can result from natural image structure. In particular, we show that the anisotropic distribution of orientations in natural statistics is sufficient to explain the cardinal bias for motion direction. We show that inherent autocorrelation in natural images means that speed and direction are related quantities, which could shape the relationship between speed and direction tuning of MT neurons. Finally, we show that movement speed and image contrast are related in moving natural images, and that motion misperception can be explained by this speed-contrast association not a “slow world” prior.

Equilibrium between excitation and inhibition (E/I balance) is key to healthy brain function. Conversely, disruption of normal E/I balance has been implicated in a range of central neurological pathologies. Magnetic resonance spectroscopy (MRS) provides a non-invasive means of quantifying in vivo concentrations of excitatory and inhibitory neurotransmitters, which could be used as diagnostic biomarkers. Using the ratio of excitatory and inhibitory neurotransmitters as an index of E/I balance is common practice in MRS work, but recent studies have shown inconsistent evidence for the validity of this proxy. This is underscored by the fact that different measures are often used in calculating E/I balance such as glutamate and Glx (glutamate and glutamine). Here we used a large MRS dataset obtained at ultra-high field (7 T) measured from 193 healthy young adults and focused on two brain regions - prefrontal and occipital cortex - to resolve this inconsistency. We find evidence that there is an inter-individual common ratio between GABA+ (γ-aminobutyric acid and macromolecules) and Glx in occipital, but not prefrontal cortex. We further replicate the prefrontal result in a legacy dataset (n=78) measured at high-field (3 T) strength. By contrast, with ultra-high field MRS data, we find extreme evidence that there is a common ratio between GABA+ and glutamate in both prefrontal and occipital cortices, which cannot be explained by participant demographics, signal quality, fractional tissue volume, or other metabolite concentrations. These results are consistent with previous electrophysiological and theoretical work supporting E/I balance. Our findings indicate that MRS-detected GABA+ and glutamate (but not Glx), are a reliable measure of E/I balance.