Nicol Spencer Harper

This paper introduces a model where neurons adjust synaptic strengths to minimize somato-dendritic mismatch, enabling network learning and reproducing experimental observations of plasticity.

This study develops a spiking retinal model trained to predict future visual stimuli, showing it captures retinal responses and processes across species.

This paper develops a hierarchical recurrent temporal prediction model to explain neuronal response properties in the visual cortex by incorporating local and inter-areal feedback connectivity.

This paper introduces a computational model of auditory cortical responses using gated recurrency, showing improved prediction over existing methods and revealing memory differences between primary and secondary auditory cortex.

This study shows that a recurrent neural network optimized for predicting sensory inputs explains the functional and anatomical connectivity patterns in primary visual cortex.

The paper develops a spiking neural network to predict natural movies, revealing key differences between excitatory and inhibitory V1 neurons that aid in sensory prediction.

This paper introduces a temporal predictive coding model implemented in recurrent networks that can predict future stimuli and produce neuron-like responses without complex computations.

The paper shows that hierarchical temporal prediction explains changes in neuronal tuning for motion from retina to higher visual cortex.

The paper shows that cochlear tuning properties resemble those of an artificial neural network optimized for temporal prediction of natural sounds.

This study shows how auditory cortical neurons adapt to sound reverberation by adjusting their filtering properties to dereverberate sounds.

The paper investigates omission responses in rat auditory cortex using extracellular recordings and finds they occur in local field potentials and multiunit activity but not in spiking activity.

The paper analyzes the statistical features of natural sound textures to understand their distribution and redundancy.

The paper shows that simple spectral models of the auditory periphery can predict cortical responses to natural sounds as well as complex biophysical models.

The paper introduces a dynamic network model to explain temporal receptive fields in auditory cortex neurons, showing it can match performance of models with longer time delays.

The paper shows that low-level auditory processing in rats explains beat perception in music through increased firing rates on beats.

This study shows that sensory cortex neurons are tuned to features that best predict future inputs, as demonstrated by neural networks trained to predict video/audio frames.