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R Maex

Publications and source records attributed to R Maex.

10 recordsLinked to original sources
PubMed2005

Deletion of FMR1 in Purkinje cells enhances parallel fiber LTD, enlarges spines, and attenuates cerebellar eyelid conditioning in Fragile X syndrome.

Absence of functional FMRP causes Fragile X syndrome. Abnormalities in synaptic processes in the cerebral cortex and hippocampus contribute to cognitive deficits in Fragile X patients. So far, the potential roles of cerebellar deficits have not been investigated. Here, we demonstrate that both global and Purkinje cell-specific knockouts of Fmr1 show deficits in classical delay eye-blink conditioning in that the percentage of conditioned responses as well as their peak amplitude and peak velocity are reduced. Purkinje cells of these mice show elongated spines and enhanced LTD induction at the parallel fiber synapses that innervate these spines. Moreover, Fragile X patients display the same cerebellar deficits in eye-blink conditioning as the mutant mice. These data indicate that a lack of FMRP leads to cerebellar deficits at both the cellular and behavioral levels and raise the possibility that cerebellar dysfunctions can contribute to motor learning deficits in Fragile X patients.

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PubMed2002

Peripheral stimuli excite coronal beams of Golgi cells in rat cerebellar cortex.

Cerebellar granule cells constitute the largest neurone population of the brain. Their axons run as parallel fibres along the coronal axis, and the one-dimensional spread of excitation that is expected to result from this arrangement is a key assumption of theories of cerebellar function. In many studies using various techniques, however, it was not possible to evoke such a beam-like propagation of excitation with natural stimuli. We recorded, in Crus I and II of anaesthetised rats, pairs of Golgi cells aligned along the parallel fibre axis and synchronising spontaneously. Each pair was subjected to two stimulation protocols: punctate and semi-continuous. Local punctate facial stimulation evoked distinct fast and late responses of variable strength and latency (fast: 4.0-10.2 ms; late: 13.6-22.7 ms). Semi-continuous stimulation with a brush increased the firing rate, and modified the precision and phase of synchronisation. Differences between a pair in response strength and phase to brush stimulation correlated strongly with the difference in latency to punctate stimulation. These observations were reproduced in a model of the granular layer. The stimulus activated a central patch of mossy fibres, and Golgi cells received short- and long-range excitation from mossy and parallel fibres, respectively. The strength and latency of the punctate response of a model Golgi cell were found to vary with its position, reflecting a systematic change in the contribution of mossy and parallel fibres to its excitation with distance from the activated patch. During brush stimulation, model Golgi cells inside the patch fired more precisely synchronised, whereas the other Golgi cells responded with a lag proportional to their distance from the patch, thereby reproducing the experimentally observed changes in synchronisation. Taken together with the previously reported large receptive fields of Golgi cells and with their spontaneous synchronisation, the variable, position-dependent latency of evoked Golgi cell responses indicates a beam-like spread of excitation along the parallel fibres in rat cerebellar cortex.

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PubMed2000

Weak common parallel fibre synapses explain the loose synchrony observed between rat cerebellar golgi cells.

1. In anaesthetized rats, pairs of cerebellar Golgi cells fired synchronously at rest, provided they were aligned along the parallel fibre axis. The observed synchrony was much less precise, however, than that which would be expected to result from common, monosynaptic parallel fibre excitation. 2. To explain this discrepancy, the precision and frequency of spike synchronization (i.e. the width and area of the central peak on the spike train cross-correlogram) were computed in a generic model for varying input, synaptic and neuronal parameters. 3. Correlation peaks between model neurons became broader, and peak area smaller, when the number of afferents increased and each single synapse decreased proportionally in strength. Peak width was inversely proportional to firing rate, but independent of the percentage of shared afferents. Peak area, in contrast, scaled with the percentage of shared afferents but was almost firing rate independent. 4. Broad correlation peaks between pairs of model neurons resulted from the loose spike timing between single model neurons and their afferents. This loose timing reflected a need for long-term synaptic integration to fire the neurons. Model neurons could accomplish this through firing rate adaptation mediated by a Ca2+-activated K+ channel. 5. We conclude that loose synchrony may be entirely explained by shared input from monosynaptic, non-synchronized afferents. The inverse relationship between peak width and firing rate allowed us to distinguish common parallel fibre input from firing rate covariance as a primary cause of loose synchrony between cerebellar Golgi cells in anaesthetized rats.

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PubMed1999

Parallel fibers synchronize spontaneous activity in cerebellar Golgi cells.

Cerebellar Golgi cells inhibit their afferent interneurons, the excitatory granule cells. Such a feedback inhibition causes both inhibitory and excitatory neurons in the circuit to synchronize. Our modeling work predicts that the long granule cell axons, the parallel fibers, entrain many Golgi cells and their afferent granule cells in a single synchronous rhythm. Spontaneous activity of 42 pairs of putative Golgi cells was recorded in anesthetized rats to test these predictions. In 25 of 26 pairs of Golgi cells that were positioned along the transverse axis, and presumed to receive common parallel fiber input, spontaneous activity showed a high level of coherence (mean Z score > 6). Conversely, 12 of 16 Golgi cell pairs positioned along the parasagittal axis (no common parallel fiber input) were not synchronized; 4 of 16 of them showed only low levels of synchronicity (mean Z score < 4). For transverse pairs the accuracy of the coherence, measured as the width at half-height of the central peak of the cross-correlogram, was rather low (29.8 +/- 12.5 msec) but increased with Golgi cell firing rate, as predicted by the model. These results suggest that in addition to their role as gain controllers, cerebellar Golgi cells may control the timing of granule cell spiking.

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PubMed1998

Synchronization of golgi and granule cell firing in a detailed network model of the cerebellar granule cell layer.

The granular layer of the cerebellum has a disproportionately large number of excitatory (granule cells) versus inhibitory neurons (Golgi cells). Its synaptic organization is also unique with a dense reciprocal innervation between granule and Golgi cells but without synaptic contacts among the neurons of either population. Physiological recordings of granule or Golgi cell activity are scarce, and our current thinking about the way the granular layer functions is based almost exclusively on theoretical considerations. We computed the steady-state activity of a large-scale model of the granular layer of the rat cerebellum. Within a few tens of milliseconds after the start of random mossy fiber input, the populations of Golgi and granule cells became entrained in a single synchronous oscillation, the basic frequency of which ranged from 10 to 40 Hz depending on the average rate of firing in the mossy fiber population. The long parallel fibers ensured, through alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-mediated synapses, a coherent excitation of Golgi cells, while the regular firing of each Golgi cell synchronized all granule cells within its axonal radius through transient activation of their gamma-aminobutyric acid-A (GABAA) receptor synapses. Individual granule cells often remained silent during a few successive oscillation cycles so that their average firing rates, which could be quite variable, reflected the average activities of their mossy fiber afferents. The synchronous, rhythmic firing pattern was robust over a broad range of biologically realistic parameter values and to parameter randomization. Three conditions, however, made the oscillations more transient and could desynchronize the entire network in the end: a very low mossy fiber activity, a very dominant excitation of Golgi cells through mossy fiber synapses (rather than through parallel fiber synapses), and a tonic activation of granule cell GABAA receptors (with an almost complete absence of synaptically induced inhibitory postsynaptic currents). These three conditions were associated with a reduction in the parallel fiber activity, and synchrony could be restored by increasing the mossy fiber firing rate. The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. According to the model, the granular layer of the cerebellum is desynchronized when the mossy fiber firing rate is low.

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PubMed1996

The cerebellum: cortical processing and theory.

Several advances over the past year have made necessary a complete re-evaluation of the function of the cerebellum and of the role of cerebellar synaptic plasticity. These advances include the discovery of parallel fiber induced long-term depression, the presence of normal motor coordination in the absence of cerebellar long-term depression in knock-out mice, and the strong activation of the cerebellar nuclei while sensory tasks are performed.

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PubMed1996

Model circuit of spiking neurons generating directional selectivity in simple cells.

1. We here consider the property of directional selectivity (DS) in simple cells of layer 4 of cat area 17 as an instance of a receptive field (RF) transformation between two monosynaptically connected neuron populations: the afferent geniculate (lateral geniculate nucleus, LGN) cells and their target, layer 4 simple cells. We have studied this particular RF transformation because the large set of experimental data available allowed us to restrain the synaptic organization of our model layer 4 circuitry. 2. The one-compartment, spiking model neurons of the layer 4 circuitry are excitatory (adapting) or inhibitory (nonadapting). They all have simple-cell RFs composed of two spatially separated ON and OFF subregions. The sequence of the subregions across the neurons' RFs, which is determined by the geniculocortical inputs they receive, varies independently from their preferred direction of stimulus motion, which is determined by spatial asymmetries in their corticocortical inputs. 3. Synaptic transmission in the model layer 4 circuitry is mediated via non-N-methyl-D-aspartate (non-NMDA) receptors (geniculocortical excitation), via NMDA receptors (corticocortical excitation), and via gamma-aminobutyric acid-A receptors (corticocortical inhibition). Excitatory and inhibitory cortical neurons receive the same afferents. However, excitatory neurons form efferent synapses exclusively with neurons having the same RF characteristics, and preferentially with those having the same RF position. Inhibitory neurons form synapses preferentially with neurons having different RF characteristics or adjacent RF positions. 4. By comparing the neurons' numerically computed responses to visual stimuli with those of actual simple cells, the topology of the corticocortical connections has been constrained. The experimental responses to stationary and moving, and to bar as well as grating, stimuli are consistently reproduced with a single constant parameter setting. 5. Subsequently, the model has been analyzed from a system-theoretic approach and has been manipulated in order to find the components critical for its proper functioning. Variations on the model have been simulated for evaluating the performance of alternative connection schemes. 6. Spatially opponent inhibition between model simple cells with antagonistic RF subregions is necessary for the restoration of linearity lost at the LGN output. It hyperpolarizes model simple cells when the contrast polarity of an efficient stimulus is reversed and prevents, particularly in directionally nonselective cells, a frequency doubling of the responses to sine wave gratings of low spatial frequencies. 7. Directionally opponent inhibition between model simple cells preferring opposite directions of motion is necessary for the generation of genuine DS (a ratio of firing rates > 2 for opposite directions of motion). 8. The corticocortical excitatory polysynaptic feedback loops in the model are able to provide the time delays needed to generate DS, and even to preserve DS at very low speeds. The strength spatial extension, and time course of this corticocortical feedback excitation, together with the dynamics of the geniculate afferents and the width of the RF, determine the tuning of model simple cells in the temporal and velocity domain. 9. The present model generates directionally selective responses to stimulus motion over distances smaller than the width of a single subregion and as small as the spacing between the afferent geniculate RFs. The direction-selective mechanism acts uniformly across the entire width of a subregion. Thus the position invariance of DS arises in the present model at the same level as DS itself. The same holds for the stimulus polarity (light vs. dark) invariance of DS. Consequently, there is no need for highly hierarchical models in which all these characteristics accumulate in simple cells by pooling from lower-order subunits or neurons.

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PubMed1991

Subtraction inhibition combined with a spiking threshold accounts for cortical direction selectivity.

We have modeled simple-cell direction selectivity by a nonlinearity consisting of a subtraction inhibition followed by half-wave rectification and compared the performance of this model to that of different versions of the elaborated Reichardt detector for similar inputs and parameter settings. Not only does the subtraction model fit the experimental data more closely than the elaborated Reichardt detector, but the subtraction model also is more plausible from a physiological and anatomical point of view. Moreover, the subtraction model operates optimally at plausible spatiotemporal parameter settings. Therefore, we conclude that there is no need to invoke specific synaptic interactions, such as implied in the Reichardt detector, to account for simple-cell direction selectivity.

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