11,240 research outputs found

    Intelligent Network Management and Functional Cerebellum Synthesis

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    Transdisciplinary modeling of the cerebellum across histology, physiology, and network engineering provides preliminary results at three organization levels: input/output links to central nervous system networks; links between the six neuron populations in the cerebellum; and computation among the neurons of the populations. Older models probably underestimated the importance and role of climbing fiber input which seems to supply write as well as read signals, not just to Purkinje but also to basket and stellate neurons. The well-known mossy fiber-granule cell-Golgi cell system should also respond to inputs originating from climbing fibers. Corticonuclear microcomplexing might be aided by stellate and basket computation and associate processing. Technological and scientific implications of the proposed cerebellum model are discussed

    Neuroimaging evidence implicating cerebellum in support of sensory/cognitive processes associated with thirst.

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    Recent studies implicate the cerebellum, long considered strictly a motor control structure, in cognitive, sensory, and affective phenomenon. The cerebellum, a phylogenetically ancient structure, has reciprocal ancient connections to the hypothalamus, a structure important in vegetative functions. The present study investigated whether the cerebellum was involved in vegetative functions and the primal emotions engendered by them. Using positron emission tomography, we examined the effects on the cerebellum of the rise of plasma sodium concentration and the emergence of thirst in 10 healthy adults. The correlation of regional cerebral blood flow with subjects' ratings of thirst showed major activation in the vermal central lobule. During the development of thirst, the anterior and posterior quadrangular lobule, lingula, and the vermis were activated. At maximum thirst and then during irrigation of the mouth with water to alleviate dryness, the cerebellum was less activated. However, 3 min after drinking to satiation, the anterior quadrangular lobule and posterior cerebellum were highly activated. The increased cerebellar activity was not related to motor behavior as this did not occur. Instead, responses in ancient cerebellar regions (vermis, fastigal nucleus, archicerebellum) may be more directly related to vegetative and affective aspects of thirst experiences, whereas activity in neocerebellar (posterior) regions may be related to sensory and cognitive aspects. Moreover, the cerebellum is apparently not involved in the computation of thirst per se but rather is activated during changes in thirst/satiation state when the brain is "vigilant" and is monitoring its sensory systems. Some neocerebellar activity may also reflect an intentionality for gratification by drinking inherent in the consciousness of thirst

    Logarithmic distributions prove that intrinsic learning is Hebbian

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    In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability

    A Neural Model of Timed Response Learning in the Cerebellum

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    A spectral timing model is developed to explain how the cerebellum learns adaptively timed responses during the rabbit's conditioned nictitating membrane response (NMR). The model posits two learning sites that respectively enable conditioned excitation and timed disinhibition of the response. Long-term potentiation of mossy fiber pathways projecting to interpositus nucleus cells allows conditioned excitation of the response's adaptive gain. Long-term depression of parallel fiber- Purkinje cell synapses in the cerebellar cortex allows learning of an adaptively timed reduction in Purkinje cell inhibition of the same nuclear cells. A spectrum of partially timed responses summate to generate an accurately timed population response. In agreement with physiological data, the model Purkinje cell activity decreases in the interval following the onset of the conditioned stimulus, and nuclear cell responses match conditioned response (CR) topography. The model reproduces key behavioral features of the NMR, including the properties that CR peak amplitude occurs at the unconditioned stimulus (US) onset, a discrete CR peak shift occurs with a change in interstimulus interval (ISI) between conditioned stim- ulus (CS) and US, mixed training at two different ISis produces a double-peaked CR, CR acquisition and rate of responding depend unimodally on the lSI, CR onset latency decreases during training, and maladaptively-timed, small-amplitude CRs result from ablation of cerebellar cortex.National Science Foundation (IRI-90-24877); Office of Naval Research (N00014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0225
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