During motor learning,¹ as well as during neuroprosthetic learning,^2-4 animals learn to control motor cortex activity in order to generate behavior. Two different populations of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations,^5,6 it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a calcium-imaging-based brain-machine interface (CaBMI)³ and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that the animals learned to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that the motor cortex is more efficient at controlling the activity of pyramidal tract neurons, which are embedded deep in the cortex, and relaying motor commands outside the telencephalon.

Brain-machine interfaces are not only promising for neurological applications, but also powerful for investigating neuronal ensemble dynamics during learning. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.

Basal ganglia play a central role in regulating behavior, but the organization of their outputs to other brain areas is incompletely understood. We investigate the largest output nucleus, the substantia nigra pars reticulata (SNr), and delineate the organization and physiology of its projection populations in mice. Using genetically targeted viral tracing and whole-brain anatomical analysis, we identify over 40 SNr targets that encompass a roughly 50-fold range of axonal densities. Retrograde tracing from the volumetrically largest targets indicates that the SNr contains segregated subpopulations that differentially project to functionally distinct brain stem regions. These subpopulations are electrophysiologically specialized and topographically organized and collateralize to common diencephalon targets, including the motor and intralaminar thalamus as well as the pedunculopontine nucleus and the midbrain reticular formation. These findings establish that SNr signaling is organized as dense, parallel outputs to specific brain stem targets concurrent with extensive collateral branches that encompass the majority of SNr axonal boutons.