Topic: feedback pathways

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when

Animals continuously merge information across their senses, and use these combined signals to guide their behaviour. In the brain, these multisensory computations are performed by circuits rich with complex connections like skips [1] - bypassing certain layers, and feedback pathways which allow information to flow from later (multisensory) to earlier (‘unisensory’) parts of the network [2]. However, pure-feedforward models have been shown to be optimal for many multisensory tasks [3,4], commonly used to test experimental subjects or computational models; raising the question, what role do non-