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Brain Motor Centers and Pathways
Published in Nassir H. Sabah, Neuromuscular Fundamentals, 2020
It will be noted that negative feedback in a given neuronal population tends to regulate the level of activity of the given population and prevent excessive activity. Feedforward inhibition, with its disynaptic delay, narrows the window for excitatory inputs to exercise their effect. Feedforward inhibition in the form of lateral inhibition (Section 7.2.2) that regulates the spatial spread of activity is also found in the cerebellar cortex. An example is inhibition by basket cells, mentioned previously. Another is due to Golgi cells, whose axon collaterals project transversely in the same folium or even in nearby folia. Golgi cell activity results in a center-surround pattern of granule cell excitation surrounded by inhibition. This lateral inhibition, together with the feedback inhibition depicted in Figure 12.15a can give rise to self-sustained oscillations.
Simplified cerebellum-like spiking neural network as short-range timing function for the talking robot
Published in Connection Science, 2018
Each Granular neuron has its own unique temporal pattern, which is active or inactive for a short period. The range can vary from 100 ms to 1000 ms due to the random recurrent neural network between Granular cell (GR) and Golgi cell (GO). We assume that the input signal from the Mossy fiber (MF) is the predictive timing, which is the conditional stimulus in other cerebellum network studies. The predictive timing in our model is a fixed 5 second 30 Hz Poisson spike signal. The actual sound input is pre-processed and transformed into a 30 Hz Poisson signal with the same duration to serve as a Climbing Fiber (CF). Long-term depression (LTD) at the parallel fiber (PF) adjusts the synaptic weight between the GRs to the Purkinje cells (PKJs). The adjustment coefficient in our model is big, and the cerebellum has a super-fast learning rate. Due to LTD, the synaptic weight reduces the input signal from the GRs to PKJs at the range of time when the sound is active at CF. Thus, the PKJ would not fire a spike at that range of time. Because of the inhibitory signal input from PJK to DCN, the DCN is released to fire a spike signal at the same range of time input from the CF. We consider the timing signal for the talking robot to regenerate a specific duration of a vowel. The inhibitory connection from DCN to IO prevents the cerebellum from over-training. LTP restores the weight connection of GRs to PKJs to the initial value if the learning signal is off for certain time. Due to the fact that most Granular cells will be active or inactive during a specific short duration, if the sound signal input is a long duration signal (more than 2 seconds for example), the output of the network would be the same with predictive timing since all synaptic weight from Granular cell to PKJ are reduced. So we assume this network would work well for short-time duration learning. For long duration, the network would not be able to learn the timing.