What Does the Anatomical Organization of the Entorhinal Cortex Tell Us?

The entorhinal cortex is commonly perceived as a major input and output structure of the hippocampal formation, entertaining the role of the nodal point of cortico-hippocampal circuits. Superficial layers receive convergent cortical information, which is relayed to structures in the hippocampus, and hippocampal output reaches deep layers of entorhinal cortex, that project back to the cortex. The finding of the grid cells in all layers and reports on interactions between deep and superficial layers indicate that this rather simplistic perception may be at fault. Therefore, an integrative approach on the entorhinal cortex, that takes into account recent additions to our knowledge database on entorhinal connectivity, is timely. We argue that layers in entorhinal cortex show different functional characteristics most likely not on the basis of strikingly different inputs or outputs, but much more likely on the basis of differences in intrinsic organization, combined with very specific sets of inputs. Here, we aim to summarize recent anatomical data supporting the notion that the traditional description of the entorhinal cortex as a layered input-output structure for the hippocampal formation does not give the deserved credit to what this structure might be contributing to the overall functions of cortico-hippocampal networks

Synaptic mechanisms for associative learning in the cerebellar nuclei

Associative learning during delay eyeblink conditioning (EBC) depends on an intact cerebellum. However, the relative contribution of changes in the cerebellar nuclei to learning remains a subject of ongoing debate. In particular, little is known about the changes in synaptic inputs to cerebellar nuclei neurons that take place during EBC and how they shape the membrane potential of these neurons. Here, we probed the ability of these inputs to support associative learning in mice, and investigated structural and cell-physiological changes within the cerebellar nuclei during learning. We find that optogenetic stimulation of mossy fiber afferents to the anterior interposed nucleus (AIP) can substitute for a conditioned stimulus and is sufficient to elicit conditioned responses (CRs) that are adaptively well-timed. Further, EBC induces structural changes in mossy fiber and inhibitory inputs, but not in climbing fiber inputs, and it leads to changes in subthreshold processing of AIP neurons that correlate with conditioned eyelid movements. The changes in synaptic and spiking activity that precede the CRs allow for a decoder to distinguish trials with a CR. Our data reveal how structural and physiological modifications of synaptic inputs to cerebellar nuclei neurons can facilitate learning.</p

Synaptic mechanisms for associative learning in the cerebellar nuclei

Associative learning during delay eyeblink conditioning (EBC) depends on an intact cerebellum. However, the relative contribution of changes in the cerebellar nuclei to learning remains a subject of ongoing debate. In particular, little is known about the changes in synaptic inputs to cerebellar nuclei neurons that take place during EBC and how they shape the membrane potential of these neurons. Here, we probed the ability of these inputs to support associative learning in mice, and investigated structural and cell-physiological changes within the cerebellar nuclei during learning. We find that optogenetic stimulation of mossy fiber afferents to the anterior interposed nucleus (AIP) can substitute for a conditioned stimulus and is sufficient to elicit conditioned responses (CRs) that are adaptively well-timed. Further, EBC induces structural changes in mossy fiber and inhibitory inputs, but not in climbing fiber inputs, and it leads to changes in subthreshold processing of AIP neurons that correlate with conditioned eyelid movements. The changes in synaptic and spiking activity that precede the CRs allow for a decoder to distinguish trials with a CR. Our data reveal how structural and physiological modifications of synaptic inputs to cerebellar nuclei neurons can facilitate learning.</p

Cerebellar plasticity and associative memories are controlled by perineuronal nets.

Perineuronal nets (PNNs) are assemblies of extracellular matrix molecules, which surround the cell body and dendrites of many types of neuron and regulate neural plasticity. PNNs are prominently expressed around neurons of the deep cerebellar nuclei (DCN), but their role in adult cerebellar plasticity and behavior is far from clear. Here we show that PNNs in the mouse DCN are diminished during eyeblink conditioning (EBC), a form of associative motor learning that depends on DCN plasticity. When memories are fully acquired, PNNs are restored. Enzymatic digestion of PNNs in the DCN improves EBC learning, but intact PNNs are necessary for memory retention. At the structural level, PNN removal induces significant synaptic rearrangements in vivo, resulting in increased inhibition of DCN baseline activity in awake behaving mice. Together, these results demonstrate that PNNs are critical players in the regulation of cerebellar circuitry and function

Coexpression of vesicular glutamate transporters 1 and 2, glutamic acid decarboxylase and calretinin in rat entorhinal cortex

We studied the distribution and coexpression of vesicular glutamate transporters (VGluT1, VGluT2), glutamic acid decarboxylase (GAD) and calretinin (CR, calcium-binding protein) in rat entorhinal cortex, using immunofluorescence staining and multichannel confocal laser scanning microscopy. Images were computer processed and subjected to automated 3D object recognition, colocalization analysis and 3D reconstruction. Since the VGluTs (in contrast to CR and GAD) occurred in fibers and axon terminals only, we focused our attention on these neuronal processes. An intense, punctate VGluT1-staining occurred everywhere in the entorhinal cortex. Our computer program resolved these punctae as small 3D objects. Also VGluT2 showed a punctate immunostaining pattern, yet with half the number of 3D objects per tissue volume compared with VGluT1, and with statistically significantly larger 3D objects. Both VGluTs were distributed homogeneously across cortical layers, with in MEA VGluT1 slightly more densely distributed than in LEA. The distribution pattern and the size distribution of GAD 3D objects resembled that of VGluT2. CR-immunopositive fibers were abundant in all cortical layers. In double-stained sections we noted ample colocalization of CR and VGluT2, whereas coexpression of CR and VGluT1 was nearly absent. Also in triple-staining experiments (VGluT2, GAD and CR combined) we noted coexpression of VGluT2 and CR and, in addition, frequent coexpression of GAD and CR. Modest colocalization occurred of VGluT2 and GAD, and incidental colocalization of all three markers. We conclude that the CR-containing axon terminals in the entorhinal cortex belong to at least two subpopulations of CR-neurons: a glutamatergic excitatory and a GABAergic inhibitory

Interpreting thoughts during sleep

Rapid eye movements during sleep are a readout of thoughts during mouse dreams

#1 Interpreting thoughts during sleep

Rapid eye movements during sleep are a readout of thoughts during mouse dreams

Data from: Whole-cell properties of cerebellar nuclei neurons in vivo

Cerebellar nuclei neurons integrate sensorimotor information and form the final output of the cerebellum, projecting to premotor brainstem targets. This implies that, in contrast to specialized neurons and interneurons in cortical regions, neurons within the nuclei encode and integrate complex information that is most likely reflected in a large variation of intrinsic membrane properties and integrative capacities of individual neurons. Yet, whether this large variation in properties is reflected in a heterogeneous physiological cell population of cerebellar nuclei neurons with well or poorly defined cell types remains to be determined. Indeed, the cell electrophysiological properties of cerebellar nuclei neurons have been identified in vitro in young rodents, but whether these properties are similar to the in vivo adult situation has not been shown. In this comprehensive study we present and compare the in vivo properties of 144 cerebellar nuclei neurons in adult ketamine-xylazine anesthetized mice. We found regularly firing (N=88) and spontaneously bursting (N=56) neurons. Membrane-resistance, capacitance, spike half-width and firing frequency all widely varied as a continuum, ranging from 9.63 to 3352.1 MΩ, from 6.7 to 772.57 pF, from 0.178 to 1.98 ms, and from 0 to 176.6 Hz, respectively. At the same time, several of these parameters were correlated with each other. Capacitance decreased with membrane resistance (R2=0.12, P<0.001), intensity of rebound spiking increased with membrane resistance (for 100 ms duration R2=0.1503, P=0.0011), membrane resistance decreased with membrane time constant (R2=0.045, P=0.031) and increased with spike half-width (R2=0.023, P<0.001), while capacitance increased with firing frequency (R2=0.29, P<0.001). However, classes of neuron subtypes could not be identified using merely k-clustering of their intrinsic firing properties and/or integrative properties following activation of their Purkinje cell input. Instead, using whole-cell parameters in combination with morphological criteria revealed by intracellular labelling with Neurobiotin (N=18) allowed for electrophysiological identification of larger (29.3–50 m soma diameter) and smaller (< 21.2 m) cerebellar nuclei neurons with significant differences in membrane properties. Larger cells had a lower membrane resistance and a shorter spike, with a tendency for higher capacitance. Thus, in general cerebellar nuclei neurons appear to offer a rich and wide continuum of physiological properties that stand in contrast to neurons in most cortical regions such as those of the cerebral and cerebellar cortex, in which different classes of neurons operate in a narrower territory of electrophysiological parameter space. The current dataset will help computational modelers of the cerebellar nuclei to update and improve their cerebellar motor learning and performance models by incorporating the large variation of the in vivo properties of cerebellar nuclei neurons. The cellular complexity of cerebellar nuclei neurons may endow the nuclei to perform the intricate computations required for sensorimotor coordination