The ability to learn new skills and perfect them with practice applies not only to physical skills but also to abstract skills1, like motor planning or neuroprosthetic actions. Although plasticity in corticostriatal circuits has been implicated in learning physical skills2–4, it remains unclear if similar circuits or processes are required for abstract skill learning. We utilized a novel behavioral paradigm in rodents to investigate the role of corticostriatal plasticity in abstract skill learning. Rodents learned to control the pitch of an auditory cursor to reach one of two targets by modulating activity in primary motor cortex irrespective of physical movement. Degradation of the relation between action and outcome, as well as sensory-specific devaluation and omission tests, demonstrated that these learned neuroprosthetic actions were intentional and goal-directed, rather than habitual. Striatal neurons changed their activity with learning, with more neurons modulating their activity in relation to target-reaching as learning progressed. Concomitantly, strong relations between the activity of neurons in motor cortex and the striatum emerged. Specific deletion of striatal NMDA receptors impaired the development of this corticostriatal plasticity, and disrupted the ability to learn neuroprosthetic skills. These results suggest that corticostriatal plasticity is necessary for abstract skill learning, and that neuroprosthetic movements capitalize on the neural circuitry involved in natural motor learning.

Abstract& We compared two tasks that are widely used in research on mentalizing-false belief stories and animations of rigid geometric shapes that depict social interactions-to investigate whether the neural systems that mediate the representation of others' mental states are consistent across these tasks. Whereas false belief stories activated primarily the anterior paracingulate cortex (APC), the posterior cingulate cortex/precuneus (PCC/ PC), and the temporo-parietal junction (TPJ)-components of the distributed neural system for theory of mind (ToM)-the social animations activated an extensive region along nearly the full extent of the superior temporal sulcus, including a locus in the posterior superior temporal sulcus (pSTS), as well as the frontal operculum and inferior parietal lobule (IPL)-components of the distributed neural system for action understanding-and the fusiform gyrus. These results suggest that the representation of covert mental states that may predict behavior and the representation of intentions that are implied by perceived actions involve distinct neural systems. These results show that the TPJ and the pSTS play dissociable roles in mentalizing and are parts of different distributed neural systems. Because the social animations do not depict articulated body movements, these results also highlight that the perception of the kinematics of actions is not necessary to activate the mirror neuron system, suggesting that this system plays a general role in the representation of intentions and goals of actions. Furthermore, these results suggest that the fusiform gyrus plays a general role in the representation of visual stimuli that signify agency, independent of visual form. &

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 2-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 striking modifications of firing correlations within spatially localized networks at fine scales.

Emerging applications in brain-machine interface systems require high-resolution, chronic multisite cortical recordings, which cannot be obtained with existing technologies due to high power consumption, high invasiveness, or inability to transmit data wirelessly. In this paper, we describe a microsystem based on electrocorticography (ECoG) that overcomes these difficulties, enabling chronic recording and wireless transmission of neural signals from the surface of the cerebral cortex. The device is comprised of a highly flexible, high-density, polymer-based 64-channel electrode array and a flexible antenna, bonded to 2.4 mm × 2.4 mm CMOS integrated circuit (IC) that performs 64-channel acquisition, wireless power and data transmission. The IC digitizes the signal from each electrode at 1 kS/s with 1.2 μV input referred noise, and transmits the serialized data using a 1 Mb/s backscattering modulator. A dual-mode power-receiving rectifier reduces data-dependent supply ripple, enabling the integration of small decoupling capacitors on chip and eliminating the need for external components. Design techniques in the wireless and baseband circuits result in over 16× reduction in die area with a simultaneous 3× improvement in power efficiency over the state of the art. The IC consumes 225 μW and can be powered by an external reader transmitting 12 mW at 300 MHz, which is over 3× lower than IEEE and FCC regulations.

It has been postulated that selective temporal coordination between neurons and development of functional neuronal assemblies are fundamental for brain function and behavior. Still, there is little evidence that functionally relevant coordination emerges preferentially in neuronal assemblies directly controlling behavioral output. We investigated coherence between primary motor cortex and the dorsal striatum as rats learn an abstract operant task. Striking coherence developed between these regions during learning. Interestingly, coherence was selectively increased in cells controlling behavioral output relative to adjacent cells. Furthermore, the temporal offset of these interactions aligned closely with corticostriatal conduction delays, demonstrating highly precise timing. Spikes from either region were followed by a consistent phase in the other, suggesting that network feedback reinforces coherence. Together, these results demonstrate that temporally precise coherence develops during learning specifically in output-relevant neuronal populations and further suggest that correlations in oscillatory activity serve to synchronize widespread brain networks to produce behavior.