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Redundancy reduction and sustained firing with stochastic depressing synapses
Mark S Goldman1, Pedro Maldonado, L F Abbott
1Volen Center and Department of Biology, Brandeis University, Waltham, Massachusetts 02454, USA. mark_g@mit.edu
Summary
Synapses in the central nervous system (CNS) exhibit activity-dependent depression, making transmission unreliable yet non-random. This process reduces spike train redundancy, influencing neural coding and network dynamics.
Area of Science:
- Neuroscience
- Computational Neuroscience
Background:
- Central nervous system (CNS) synapses often fail to transmit action potentials.
- Synaptic transmission probability is influenced by recent activity history, not random.
Purpose of the Study:
- To model synapse behavior with activity-dependent depression.
- To analyze spike train transmission through depressing synapses using realistic models.
Main Methods:
- Simulated realistic synapse models incorporating vesicle depletion and postrelease refractoriness.
- Analyzed spike trains from monkey V1 viewing natural scenes and other brain regions.
Main Results:
- Depressing synapses generate transmitted spike trains with reduced correlation and redundancy compared to presynaptic input.
- Positive autocorrelation in typical cortical spike trains can arise from feedforward and reverberatory inputs.
- Activity-dependent depression can shape neural firing patterns.
Conclusions:
- Synaptic depression is a key mechanism shaping neural information processing.
- The interplay of feedforward and reverberatory inputs explains sustained firing despite synaptic depression.
- Predicts input characteristics of cortical neurons based on observed firing patterns.