Modifying the recurrent connectivity of spiking networks provides sufficient flexibility to generate arbitrarily complex recurrent dynamics, suggesting that individual neurons in a recurrent network have the capability to support near universal dynamics.
Mesoscale cortical calcium activity correlating with single cortical and thalamic cell spiking reveal rich dynamics and support a novel approach for investigating in vivo functional networks in the mammalian brain.
A spiking network model that examines the transformation of odor information from olfactory bulb to piriform cortex demonstrates how intrinsic cortical circuitry preserves representations of odor identity across odorant concentrations.
Computational modelling shows that coupled theta and gamma oscillations in the auditory cortex can decompose speech into its syllabic constituents, and organize the neural spiking at faster timescale into a decodable format.
A multi-compartment spiking neural network model demonstrates that biologically feasible deep learning can be achieved if sensory inputs and higher-order feedback are received by different dendritic compartments.