Behavioural studies differentiate the rodent dorsal striatum (DS) into lateral and medial regions; however, anatomical evidence suggests that it is a unified structure. To understand striatal dynamics and basal ganglia functions, it is essential to clarify the circuitry that supports this behavioural-based segregation. Here, we show that the mouse DS is made of two non-overlapping functional circuits divided by a boundary. Combining in vivo optopatch-clamp and extracellular recordings of spontaneous and evoked sensory activity, we demonstrate different coupling of lateral and medial striatum to the cortex together with an independent integration of the spontaneous activity, due to particular corticostriatal connectivity and local attributes of each region. Additionally, we show differences in slow and fast oscillations and in the electrophysiological properties between striatonigral and striatopallidal neurons. In summary, these results demonstrate that the rodent DS is segregated in two neuronal circuits, in homology with the caudate and putamen nuclei of primates.

Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods.

AbstractBehavioural studies differentiate the rodent dorsal striatum (DS) into lateral and medial regions based on their particular roles; however, anatomical evidence suggest that it is a unified structure. To understand striatal dynamics and basal ganglia functions, it is essential to clarify the circuitry that supports this behavioural-based segregation. Here, we show that the mouse DS is made of two non-overlapping functional circuits divided by a sharp boundary. Combining in vivo optopatch-clamp and extracellular recordings of spontaneous activity, we demonstrate different coupling of lateral and medial striatum to the cortex together with an independent integration of the slow and fast oscillations, due to particular cortico-striatal connectivity and local properties of each region. Additionally, distinct properties between striatonigral and striatopallidal neurons were revealed along DS. These results demonstrate that the rodent DS is segregated in two neuronal circuits, in homology with the caudate and putamen nuclei of primates.

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.

Barrel cortex integrates contra- and ipsilateral whiskers' inputs. While contralateral inputs depend on the thalamocortical innervation, ipsilateral ones are thought to rely on callosal axons. These are more abundant in the barrel cortex region bordering with S2 and containing the row A-whiskers representation, the row lying nearest to the facial midline. Here we ask what role this callosal axonal arrangement plays in ipsilateral tactile signalling. We found that novel object exploration with ipsilateral whiskers confines c-Fos expression within the highly callosal subregion. Targeting this area with in vivo patch-clamp recordings revealed neurons with uniquely strong ipsilateral responses dependent on the corpus callosum, as assessed by tetrodotoxin silencing and by optogenetic activation of the contralateral hemisphere. Still, in this area, stimulation of contra- or ipsilateral row A-whiskers evoked an indistinguishable response in some neurons, mostly located in layers 5/6, indicating their involvement in the midline representation of the whiskers' sensory space.