The Morpho/Functional Discrepancy in the Cerebellar Cortex: Looks Alone are Deceptive

In a recent report we demonstrated that stimulation of cerebellar mossy fibers synchronously activates Purkinje cells that are located directly above the site of stimulation. We found that the activated Purkinje cells are arranged in a radial patch on the cerebellar surface and that this organization is independent of the integrity of the inhibitory system. This arrangement of activity is counterintuitive. The anatomical structure with the extensive parallel fiber system implies that mossy fiber stimulation will activate Purkinje cells along a beam of parallel fibers. In this short review we highlight this discrepancy between anatomical structure and functional dynamics and suggest a plausible underlying mechanism

Stars and Stripes in the Cerebellar Cortex: A Voltage Sensitive Dye Study

The lattice-like structure of the cerebellar cortex and its anatomical organization in two perpendicular axes provided the foundations for many theories of cerebellar function. However, the functional organization does not always match the anatomical organization. Thus direct measurement of the functional organization is central to our understanding of cerebellar processing. Here we use voltage sensitive dye imaging in the isolated cerebellar preparation to characterize the spatio-temporal organization of the climbing and mossy fiber (MF) inputs to the cerebellar cortex. Spatial and temporal parameters were used to develop reliable criteria to distinguish climbing fiber (CF) responses from MF responses. CF activation excited postsynaptic neurons along a parasagittal cortical band. These responses were composed of slow (∼25 ms), monophasic depolarizing signals. Neither the duration nor the spatial distribution of CF responses were affected by inhibition. Activation of MF generated responses that were organized in radial patches, and were composed of a fast (∼5 ms) depolarizing phase followed by a prolonged (∼100 ms) negative wave. Application of a GABAA blocker eliminated the hyperpolarizing phase and prolonged the depolarizing phase, but did not affect the spatial distribution of the response, thus suggesting that it is not the inhibitory system that is responsible for the inability of the MF input to generate beams of activity that propagate along the parallel fiber system

Oscillatory activity, phase differences, and phase resetting in the inferior olivary nucleus

© 2013 Lefler, Torben-Nielsen and Yarom. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etcThe generation of temporal patterns is one of the most fascinating functions of the brain. Unlike the response to external stimuli temporal patterns are generated within the system and recalled for a specific use. To generate temporal patterns one needs a timing machine, a "master clock" that determines the temporal framework within which temporal patterns can be generated and implemented. Here we present the concept that in this putative "master clock" phase and frequency interact to generate temporal patterns. We define the requirements for a neuronal "master clock" to be both reliable and versatile. We introduce this concept within the inferior olive nucleus which at least by some scientists is regarded as the source of timing for cerebellar function. We review the basic properties of the subthreshold oscillation recorded from olivary neurons, analyze the phase relationships between neurons and demonstrate that the phase and onset of oscillation is tightly controlled by synaptic input. These properties endowed the olivary nucleus with the ability to act as a "master clock."Peer reviewedFinal Published versio

Regularity, Variability and Bi-Stability in the Activity of Cerebellar Purkinje Cells

Recent studies have demonstrated that the membrane potential of Purkinje cells is bi-stable and that this phenomenon underlies bi-modal simple spike firing. Membrane potential alternates between a depolarized state, that is associated with spontaneous simple spike firing (up state), and a quiescent hyperpolarized state (down state). A controversy has emerged regarding the relevance of bi-stability to the awake animal, yet recordings made from behaving cat Purkinje cells have demonstrated that at least 50% of the cells exhibit bi-modal firing. The robustness of the phenomenon in vitro or in anaesthetized systems on the one hand, and the controversy regarding its expression in behaving animals on the other hand suggest that state transitions are under neuronal control. Indeed, we have recently demonstrated that synaptic inputs can induce transitions between the states and suggested that the role of granule cell input is to control the states of Purkinje cells rather than increase or decrease firing rate gradually. We have also shown that the state of a Purkinje cell does not only affect its firing but also the waveform of climbing fiber-driven complex spikes and the associated calcium influx. These findings call for a reconsideration of the role of Purkinje cells in cerebellar function. In this manuscript we review the recent findings on Purkinje cell bi-stability and add some analyses of its effect on the regularity and variability of Purkinje cell activity

Excitatory and inhibitory synaptic mechanisms at the first stage of integration in the electroreception system of the shark

High impulse rate in afferent nerves is a common feature in many sensory systems that serve to accommodate a wide dynamic range. However, the first stage of integration should be endowed with specific properties that enable efficient handling of the incoming information. In elasmobranches, the afferent nerve originating from the ampullae of Lorenzini targets specific neurons located at the Dorsal Octavolateral Nucleus (DON), the first stage of integration in the electroreception system. Using intracellular recordings in an isolated brainstem preparation from the shark we analyze the properties of this afferent pathway. We found that stimulating the afferent nerve activates a mixture of excitatory and inhibitory synapses mediated by AMPA-like and GABA(A) receptors, respectively. The excitatory synapses that are extremely efficient in activating the postsynaptic neurons display unusual voltage dependence, enabling them to operate as a current source. The inhibitory input is powerful enough to completely eliminate the excitatory action of the afferent nerve but is ineffective regarding other excitatory inputs. These observations can be explained by the location and efficiency of the synapses. We conclude that the afferent nerve provides powerful and reliable excitatory input as well as a feed-forward inhibitory input, which is partially presynaptic in origin. These results question the cellular location within the DON where cancelation of expected incoming signals occurs

A Paradoxical Isopotentiality: A Spatially Uniform Noise Spectrum in Neocortical Pyramidal Cells

Membrane ion channels and synapses are among the most important computational elements of nerve cells. Both have stochastic components that are reflected in random fluctuations of the membrane potential. We measured the spectral characteristics of membrane voltage noise in vitro at the soma and the apical dendrite of layer 4/5 (L4/5) neocortical neurons of rats near the resting potential. We found a remarkable similarity between the voltage noise power spectra at the soma and the dendrites, despite a marked difference in their respective input impedances. At both sites, the noise levels and the input impedance are voltage dependent; in the soma, the noise level increased from σ = 0.33 ± 0.28 mV at 10 mV hyperpolarization from the resting potential to σ = 0.59 ± 0.3 at a depolarization of 10 mV. At the dendrite, the noise increased from σ = 0.34 ± 0.28 to σ = 0.56 ± 0.30 mV, respectively. TTX reduced both the input impedance and the voltage noise, and eliminated their voltage dependence at both locations. We describe a detailed compartmental model of a L4/5 neuron with simplified electrical properties that successfully reproduces the difference in input impedance between dendrites and soma and demonstrates that spatially uniform conductance-base noise sources leads to an apparent isopotential structure which exhibits a uniform power spectra of voltage noise at all locations. We speculate that a homogeneous distribution of noise sources insures that variability in synaptic amplitude as well as timing of action potentials is location invariant

The generation of phase differences and frequency changes in a network model of inferior olive subthreshold oscillations

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedicationIt is commonly accepted that the Inferior Olive (IO) provides a timing signal to the cerebellum. Stable subthreshold oscillations in the IO can facilitate accurate timing by phase-locking spikes to the peaks of the oscillation. Several theoretical models accounting for the synchronized subthreshold oscillations have been proposed, however, two experimental observations remain an enigma. The first is the observation of frequent alterations in the frequency of the oscillations. The second is the observation of constant phase differences between simultaneously recorded neurons. In order to account for these two observations we constructed a canonical network model based on anatomical and physiological data from the IO. The constructed network is characterized by clustering of neurons with similar conductance densities, and by electrical coupling between neurons. Neurons inside a cluster are densely connected with weak strengths, while neurons belonging to different clusters are sparsely connected with stronger connections. We found that this type of network can robustly display stable subthreshold oscillations. The overall frequency of the network changes with the strength of the inter-cluster connections, and phase differences occur between neurons of different clusters. Moreover, the phase differences provide a mechanistic explanation for the experimentally observed propagating waves of activity in the IO. We conclude that the architecture of the network of electrically coupled neurons in combination with modulation of the inter-cluster coupling strengths can account for the experimentally observed frequency changes and the phase differences.Peer reviewedFinal Published versio