A dynamical system view of cerebellar function
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Forward models for physiological motor control
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
A cerebellar model of timing and prediction in the control of reaching
Neural Computation
Sparse Distributed Memory
Parallel Fiber Coding in the Cerebellum for Life-Long Learning
Autonomous Robots
| Hi-index | 0.00 |
The cerebellar cortical circuitry may support a distinct second form of associative learning, complementary to the well-known synaptic plasticity (long term depression, LTD) that has been previously shown. As the granule cell axons ascend to the molecular layer, they make multiple synapses on the overlying Purkinje cells (PC). This ascending branch (AB) input, which has been ignored in models of cerebellar learning, is likely to be functionally distinct from the parallel fiber (PF) synaptic input. We predict that AB-PF correlations lead to Hebbian-type learning at the PF-PC synapse, including long term potentiation (LTP), and allowing the cortical circuit to combine AB-PF LTP for feedforward state prediction with climbing fiber LTD for feedback error correction. The new learning mechanism could therefore add computational capacity to cerebellar models and may explain more of the experimental data.