Computational modeling of excitatory/inhibitory balance impairments in schizophrenia.

Deficits in glutamatergic function are well established in schizophrenia (SZ) as reflected in "input" dysfunction across sensory systems. By contrast, less is known about contributions of the GABAergic system to impairments in excitatory/inhibitory balance. We investigated this issue by measuring contrast thresholds for orientation detection, orientation discriminability, and orientation-tilt-aftereffect curves in schizophrenia subjects and matched controls. These measures depend on the amplitude and width of underlying orientation tuning curves, which, in turn, depend on excitatory and inhibitory interactions. By simulating a well-established V1 orientation selectivity model and its link to perception, we demonstrate that reduced cortical excitation and inhibition are both necessary to explain our psychophysical data. Reductions in GABAergic feedback may represent a compensatory response to impaired glutamatergic input in SZ, or a separate pathophysiological event. We also found evidence for the widely accepted, but rarely tested, inverse relationship between orientation discriminability and tuning width.

A learning-style theory for understanding autistic behaviors.

Understanding autism's ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name-number association in a phonebook). However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response). The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm), restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity), impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn regularities.

Adaptation across the cortical hierarchy: low-level curve adaptation affects high-level facial-expression judgments.

Adaptation is ubiquitous in sensory processing. Although sensory processing is hierarchical, with neurons at higher levels exhibiting greater degrees of tuning complexity and invariance than those at lower levels, few experimental or theoretical studies address how adaptation at one hierarchical level affects processing at others. Nevertheless, this issue is critical for understanding cortical coding and computation. Therefore, we examined whether perception of high-level facial expressions can be affected by adaptation to low-level curves (i.e., the shape of a mouth). After adapting to a concave curve, subjects more frequently perceived faces as happy, and after adapting to a convex curve, subjects more frequently perceived faces as sad. We observed this multilevel aftereffect with both cartoon and real test faces when the adapting curve and the mouths of the test faces had the same location. However, when we placed the adapting curve 0.2 degrees below the test faces, the effect disappeared. Surprisingly, this positional specificity held even when real faces, instead of curves, were the adapting stimuli, suggesting that it is a general property for facial-expression aftereffects. We also studied the converse question of whether face adaptation affects curvature judgments, and found such effects after adapting to a cartoon face, but not a real face. Our results suggest that there is a local component in facial-expression representation, in addition to holistic representations emphasized in previous studies. By showing that adaptation can propagate up the cortical hierarchy, our findings also challenge existing functional accounts of adaptation.