Synaptic computation

Abstract

Neurons are often considered to be the computational engines of the brain, with synapses acting solely as conveyers of information. But the diverse types of synaptic plasticity and the range of timescales over which they operate suggest that synapses have a more active role in information processing. Long-term changes in the transmission properties of synapses provide a physiological substrate for learning and memory, whereas short-term changes support a variety of computations. By expressing several forms of synaptic plasticity, a single neuron can convey an array of different signals to the neural circuit in which it operates.

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Figure 1: Several processes determine how a presynaptic neuron influences the firing pattern of its postsynaptic targets.
Figure 2: Examples of excitatory postsynaptic currents (EPSCs) recorded in response to an irregular stimulus train with an average rate of 20 Hz at the climbing fibre, parallel fibre and Schaffer collateral synapses.
Figure 3: Synaptic modulation regulates synaptic dynamics and influences the transmission function of synapses.
Figure 4: Stochastic transmission from two model synapses.
Figure 5: The ability of coactivated synapses to activate their targets depends on whether the synaptic inputs have the same use-dependent plasticity.
Figure 6: Synaptic depression of thalamocortical synapses underlies sensory adaptation in the cortex.
Figure 7
Figure 8: STAs and TTAs for a model neuron.

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Abbott, L., Regehr, W. Synaptic computation. Nature 431, 796–803 (2004). https://doi.org/10.1038/nature03010

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