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The physics of the environment provide a rich spatiotemporal structure forour experience. Objects move in predictable ways and their features andidentity remain stable across time and space. How does the brain leverage thisstructure to make predictions about and learn from the environment? This thesisdescribes research centered around a mechanistic description of sensoryprediction called LeabraTI (TI: Temporal Integration) that explains preciselyhow predictive processing is accomplished in neocortical microcircuits. Thefundamental prediction of LeabraTI is that predictions and sensations areinterleaved across the same neural tissue at an overall rate of 10 Hz,corresponding to the widely studied alpha rhythm of posterior cortex.Experiments described herein tested this prediction by manipulating thespatiotemporal properties of three-dimensional object stimuli in a laboratorysetting. EEG results indicated that predictions were subserved by ~10 Hzoscillations that reliably tracked the onset of stimuli and differentiatedbetween spatially predictable and unpredictable object sequences. There was abehavioral advantage for combined spatial and temporal predictability fordiscrimination of unlearned objects, but prolonged study of objects under thiscombined predictability context impaired discriminability relative to otherlearning contexts. This counterintuitive pattern of results was accounted forby a neural network model that learned three-dimensional viewpoint invariancewith LeabraTI's spatiotemporal prediction rule. Synaptic weight scaling fromprolonged learning built viewpoint invariance, but led to confusion betweenambiguous views of objects, producing slightly lower performance on average.Overall, this work advances a biological architecture for sensory predictionaccompanied by empirical evidence that supports learning of realistic time- andspace-varying inputs.

What happens next and when "next" happens: Mechanisms of spatial and temporal prediction