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Boahen, K.

Publications and source records attributed to Boahen, K..

3 recordsLinked to original sources

Axonal ensembles repeatedly cluster and order synapses along dendrites in mouse cortex

Neuronal ensembles--groups of neurons that exhibit coordinated activity during behavior--are a fundamental feature of cortical computation. Dendritic branches amplify clustered synaptic inputs through local nonlinearities, suggesting that presynaptic groups might organize their connections in specific spatial patterns to engage these mechanisms. Whether the same axon groups form synaptic clusters with consistent spatial arrangements across different target neurons remains unknown, but nanoscale connectomes would resolve such anatomical motifs if they exist. We analyzed millions of synaptic connections in a connectome of mouse visual cortex and found over 700,000 axon groups that repeatedly cluster their synapses onto dendritic branches of multiple pyramidal cells, with over 500,000 maintaining consistent distal-to-proximal arrangements. These repeated patterns occur far more frequently than expected from spatial proximity or layer-based connectivity rules. Axon groups preferentially target specific dendritic branches and position their synapses in stereotyped spatial configurations across multiple postsynaptic partners, revealing that functional ensembles leave characteristic anatomical signatures in cortical microarchitecture.

neuroscience↗

Neurogrid simulates cortical cell-types, active dendrites, and top-down attention

A central challenge for systems neuroscience and artificial intelligence is to understand how cognitive behaviors arise from large, highly interconnected networks of neurons. Digital simulation is linking cognitive behavior to neural activity to bridge this gap in our understanding at great expense in time and electricity. A hybrid analog-digital approach, whereby slow analog circuits, operating in parallel, emulate graded integration of synaptic currents by dendrites while a fast digital bus, operating serially, emulates all-or-none transmission of action potentials by axons, may improve simulation efficacy. Due to the latters serial operation, this approach has not scaled beyond millions of synaptic connections (per bus). This limit was broken by following design principles the neocortex uses to minimize its wiring. The resulting hybrid analog-digital platform, Neurogrid, scales to billions of synaptic connections, between up to a million neurons, and simulates cortical models in real-time using a few watts of electricity. Here, we demonstrate that Neurogrid simulates cortical models spanning five levels of experimental investigation: biophysical, dendritic, neuronal, columnar, and area. Bridging these five levels with Neurogrid revealed a novel way active dendrites could mediate top-down attention.

neuroscience↗

Influence of On-Off dynamics and selective attention on the spatial pattern of correlated variability in neocortex

Correlated activity fluctuations in neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity and change dynamically with cognitive states, such as attention. Yet, how anatomical connectivity and cognitive states define the population structure of correlations is not known. We measured correlations in single cortical columns and found that the magnitude of correlations, their attentional modulation and dependence on lateral distance are predicted by On-Off dynamics, synchronous fluctuations in population activity across cortical layers. We developed a network model, in which spatial connectivity correlates the On-Off dynamics across nearby columns. We show that attentional inputs modulate the spatial extent of On-Off dynamics, resulting in spatially non-uniform changes in correlations. We confirm this prediction in our columnar recordings by showing that attentional modulation of correlations depends on lateral distance. Our results reveal how heterogeneous spatial patterns of correlations arise from the connectivity and network dynamics during attention.

neuroscience↗