4 research outputs found
Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model
A vast amount of information about the external world continuously flows into the brain, whereas its capacity to process such information is limited. Attention enables the brain to allocate its resources of information processing to selected sensory inputs for reducing its computational load, and effects of attention have been extensively studied in visual information processing. However, how the microcircuit of the visual cortex processes attentional information from higher areas remains largely unknown. Here, we explore the complex interactions between visual inputs and an attentional signal in a computational model of the visual cortical microcircuit. Our model not only successfully accounts for previous experimental observations of attentional effects on visual neuronal responses, but also predicts contrasting differences in the attentional effects of top-down signals between cortical layers: attention to a preferred stimulus of a column enhances neuronal responses of layers 2/3 and 5, the output stations of cortical microcircuits, whereas attention suppresses neuronal responses of layer 4, the input station of cortical microcircuits. We demonstrate that the specific modulation pattern of layer-4 activity, which emerges from inter-laminar synaptic connections, is crucial for a rapid shift of attention to a currently unattended stimulus. Our results suggest that top-down signals act differently on different layers of the cortical microcircuit
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
Chronic and acute implants of multi-electrode arrays that cover several
mm of neural tissue provide simultaneous access to population signals like
extracellular potentials and the spiking activity of 100 or more individual
neurons. While the recorded data may uncover principles of brain function, its
interpretation calls for multiscale computational models with corresponding
spatial dimensions and signal predictions. Such models can facilitate the
search of mechanisms underlying observed spatiotemporal activity patterns in
cortex. Multi-layer spiking neuron network models of local cortical circuits
covering ~1 mm have been developed, integrating experimentally obtained
neuron-type specific connectivity data and reproducing features of in-vivo
spiking statistics. With forward models, local field potentials (LFPs) can be
computed from the simulated spiking activity. To account for the spatial scale
of common neural recordings, we extend a local network and LFP model to 4x4
mm. The upscaling preserves the neuron densities, and introduces
distance-dependent connection probabilities and delays. As detailed
experimental connectivity data is partially lacking, we address this
uncertainty in model parameters by testing parameter combinations within
biologically plausible bounds. Based on model predictions of spiking activity
and LFPs, we find that the upscaling procedure preserves the overall spiking
statistics of the original model and reproduces asynchronous irregular spiking
across populations and weak pairwise spike-train correlations observed in
sensory cortex. In contrast with the weak spike-train correlations, the
correlation of LFP signals is strong and distance-dependent, compatible with
experimental observations. Enhanced spatial coherence in the low-gamma band may
explain the recent experimental report of an apparent band-pass filter effect
in the spatial reach of the LFP.Comment: 44 pages, 9 figures, 5 table