FOXP2-immunoreactive corticothalamic neurons in neocortical layers 6a and 6b are tightly regulated by neuromodulatory systems.

The / gene, linked to fine motor control in vertebrates, is a potential candidate gene thought to play a prominent role in human language production. It is expressed specifically in a subset of corticothalamic (CT) pyramidal cells (PCs) in layer 6 (L6) of the neocortex. These L6 FOXP2+ PCs project exclusively to the thalamus, with L6a PCs targeting first-order or both first- and higher-order thalamic nuclei, whereas L6b PCs connect only to higher-order nuclei.

Activity dependent modulation of glial gap junction coupling in the thalamus.

Astrocytes and oligodendrocytes in the ventrobasal thalamus are electrically coupled through gap junctions. We have previously shown that these cells form large panglial networks, which have a key role in the transfer of energy substrates to postsynapses for sustaining neuronal activity. Here, we show that the efficiency of these transfer networks is regulated by synaptic activity: preventing the generation and propagation of action potentials resulted in reduced glial coupling.

Network-neuron interactions underlying sensory responses of layer 5 pyramidal tract neurons in barrel cortex.

Neurons in the cerebral cortex receive thousands of synaptic inputs per second from thousands of presynaptic neurons. How the dendritic location of inputs, their timing, strength, and presynaptic origin, in conjunction with complex dendritic physiology, impact the transformation of synaptic input into action potential (AP) output remains generally unknown for in vivo conditions. Here, we introduce a computational approach to reveal which properties of the input causally underlie AP output, and how this neuronal input-output computation is influenced by the morphology and biophysical properties of the dendrites.

Simulation-based inference for efficient identification of generative models in computational connectomics.

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning.