Ramakrishnan Iyer

The paper studies how interactions between mouse visual cortical areas change over time using functional connectivity analysis of neural activity data.

The paper presents a dataset and analysis of spiking activity across multiple mouse visual areas, revealing that functional connectivity mirrors anatomical hierarchy.

The paper develops a multi-scale model of mouse primary visual cortex integrating structural and functional data to identify cell-class specific connectivity rules.

The paper investigates how neural representations transform from dLGN to V1 in mice, finding evidence for both sparse coding and linear separability.

The paper identifies direction-reversing neurons in the mouse visual system that change their preferred motion direction under different spatiotemporal stimuli, supported by an asymmetric filter model.

The paper analyzes neuronal variability in the cortex and finds that its distribution resembles deep learning noise, suggesting it may aid generalization during learning.

The paper develops a biophysically detailed in silico model of layer 4 of the mouse primary visual cortex, showing how functional synaptic connectivity reproduces experimental observations of neuronal responses to visual stimuli.

The paper develops generalized leaky integrate-and-fire models to classify neuron types in the mammalian neocortex using electrophysiological data.

The paper computes optimal lateral connections in mouse V1 neurons using natural scene statistics and neuron responses, showing like-to-like excitatory connectivity and three types of inhibition.