The anatomical connections from the whiskers to the rodent somatosensory (barrel) cortex form two parallel (lemniscal and paralemniscal) pathways. It is unclear whether the paralemniscal pathway is directly involved in tactile processing, because paralemniscal neuronal responses show poor spatial resolution, labile latencies and strong dependence on cortical feedback. Here we show that the paralemniscal system can transform temporally encoded vibrissal information into a rate code. We recorded the representations of the frequency of whisker movement along the two pathways in anaesthetized rats. In response to varying stimulus frequencies, the lemniscal neurons exhibited amplitude modulations and constant latencies. In contrast, paralemniscal neurons in both thalamus and cortex coded the input frequency as changes in latency. Because the onset latencies increased and the offset latencies remained constant, the latency increments were translated into a rate code: increasing onset latencies led to lower spike counts. A thalamocortical loop that includes cortical oscillations and thalamic gating can account for these results. Thus, variable latencies and effective cortical feedback in the paralemniscal system can serve the processing of temporal sensory cues, such as those that encode object location during whisking. In contrast, fixed time locking in the lemniscal system is crucial for reliable spatial processing.

The skilled generation of motor sequences involves the appropriate choice, ordering and timing of a sequence of simple, stereotyped movement elements. Nevertheless, a given movement element within a well-rehearsed sequence can be modified through interaction with its neighboring elements (co-articulation). We show that extensive training on a sequence of planar hand trajectories passing through several targets resulted in the co-articulation of movement components, and in the formation of new movement elements (primitives). Reduction in movement duration was accompanied by the gradual replacement of straight trajectories by longer curved ones, the latter affording the maximization of movement smoothness. Surprisingly, the curved trajectories were generated even when new target configurations were introduced, i.e., when target distances were scaled, movement direction reversed or when different start and end positions were used, indicating the acquisition of geometrically defined movement elements. However, the new trajectories were not shared by the untrained hand. Altogether, our results suggest that novel movement elements can be acquired through extensive training in adults.

How does processing of information change the internal representations used in subsequent stages of sensory pathways? To approach this question, we studied the representations of whisker movements in the lemniscal and paralemniscal pathways of the rat vibrissal system. We recently suggested that these two pathways encode movement frequency in different ways. We proposed that paralemniscal thalamocortical circuits, functioning as phase-locked loops (PLLs), translate temporally coded information into a rate code. Here we focus on the two major trigeminal nuclei of the brain stem, nucleus principalis and subnucleus interpolaris, and on their thalamic targets, the ventral posteromedial nucleus (VPM) and the medial division of the posterior nucleus (POm). This is the first study in which these brain stem and thalamic nuclei were explored together in the same animals and using the same stimuli. We studied both single- and multi-unit activity. We moved the whiskers both mechanically and by air puffs; here we present air-puff-induced movements because they are more similar to natural movements than movements induced by mechanical stimulations. We describe the basic properties of the responses in these brain stem and thalamic nuclei. The responses in both brain stem nuclei were similar; responses to air puffs were mostly tonic and followed the trajectory of whisker movement. The responses in the two thalamic nuclei were similar during low-frequency stimulations or during the first pulses of high-frequency stimulations, exhibiting more phasic responses than those of brain stem neurons. However, with frequencies >2 Hz, VPM and POm responses differed, generating different representations of the stimulus frequency. In the VPM, response amplitudes (instantaneous firing rates) and spike counts (total number of spikes per stimulus cycle) decreased as a function of the frequency. In the POm, latencies increased and spike count decreased as a function of the frequency. Having described the basic response properties in the four nuclei, we then focus on a specific test of our PLL hypothesis for coding in the paralemniscal pathway. We used short-duration air puffs, much shorter than whisker movements during natural whisking. The activity in this situation was consistent with the prediction we made on the basis of the PLL hypothesis.

Part of the information obtained by rodent whiskers is carried by the frequency of their movement. In the thalamus of anesthetized rats, the whisker frequency is represented by two different coding schemes: by amplitude and spike count (i.e., response amplitudes and spike counts decrease as a function of frequency) in the lemniscal thalamus and by latency and spike count (latencies increase and spike counts decrease as a function of frequency) in the paralemniscal thalamus (see accompanying paper). Here we investigated neuronal representations of the whisker frequency in the primary somatosensory ("barrel") cortex of the anesthetized rat, which receives its input from both the lemniscal and paralemniscal thalamic nuclei. Single and multi-units were recorded from layers 2/3, 4 (barrels only), 5a, and 5b during vibrissal stimulation. Typically, the input frequency was represented by amplitude and spike count in the barrels of layer 4 and in layer 5b (the "lemniscal layers") and by latency and spike count in layer 5a (the "paralemniscal layer"). Neurons of layer 2/3 displayed a mixture of the two coding schemes. When the pulse width of the stimulus was reduced from 50 to 20 ms, the latency coding in layers 5a and 2/3 was dramatically reduced, while the spike-count coding was not affected; in contrast, in layers 4 and 5b, the latencies remained constant, but the spike counts were reduced with 20-ms stimuli. The same effects were found in the paralemniscal and lemniscal thalamic nuclei, respectively (see accompanying paper). These results are consistent with the idea that thalamocortical loops of different pathways, although terminating within the same cortical columns, perform different computations in parallel. Furthermore, the mixture of coding schemes in layer 2/3 might reflect an integration of lemniscal and paralemniscal outputs.

Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands.Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep).Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations.Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements.