In the model, they saw that in the interval between spikes, the Purkinje neuron's membrane voltage in slowly firing neurons was much lower than the rapidly firing ones. Once completed, the team found that for the first time, the new model was able to replicate the unique firing-rate dependent behavior. De Schutter's unit had just finished developing an updated model, an immense task primarily undertaken by now former postdoctoral researcher, Dr. "It was clear that a newer model including more data was needed."įortunately, Prof. Although the models were good at mimicking spikes, they lacked data about how the neurons acted in the intervals between spikes," Prof. "The existing models could not replicate this behavior and therefore could not explain why this happened.
But, when the firing rate is high, the impact of input spikes grows and makes the Purkinje cell fire earlier. Interestingly, when a Purkinje cell fires slowly, spikes from connected cells have little effect on the neuron's spiking. These spikes can perturb neighboring neurons through synaptic connections and alter their firing pattern," explained Prof. "Neurons are connected and entangled with many other neurons that are also transmitting electrical signals. The stronger the input to a neuron, the quicker that neuron fires.īut neurons don't fire in an independent manner. Spikes, or action potentials, follow an "all or nothing" principle - either they occur, or they don't - but the size of the electrical signal never changes, only the frequency. The rate at which a neuron fires electrical signals is one of the most crucial means of transmitting information to other neurons. These studies showed that the firing rate of a Purkinje neuron affected how it reacted to signals fired from other neighboring neurons. "But a few years ago, experimental research into these neurons uncovered a strange behavior that couldn't be replicated in any existing models." "Purkinje cells are an attractive target for computational modeling as there has always been a lot of experimental data to draw from," said Professor Erik De Schutter, who leads the Computation Neuroscience Unit. This dense region of the hindbrain receives inputs from the body and other areas of the brain in order to fine-tune the accuracy and timing of movement, among other tasks. The model focuses on Purkinje neurons, which are found within the cerebellum.
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