期刊
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷 4, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2010.00156
关键词
electrosensory; mormyrid; learning dynamics; stability; descending control; stochastic
资金
- National Science Foundation [CRCNS IIS-0827722, IIS-0812687]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0812687] Funding Source: National Science Foundation
Adaptive sensory processing influences the central nervous system's interpretation of incoming sensory information. One of the functions of this adaptive sensory processing is to allow the nervous system to ignore predictable sensory information so that it may focus on important novel information needed to improve performance of specific tasks. The mechanism of spike-timing-dependent plasticity (STDP) has proven to be intriguing in this context because of its dual role in long-term memory and ongoing adaptation to maintain optimal tuning of neural responses. Some of the clearest links between STDP and adaptive sensory processing have come from in vitro, in vivo, and modeling studies of the electrosensory systems of weakly electric fish. Plasticity in these systems is anti-Hebbian, so that presynaptic inputs that repeatedly precede, and possibly could contribute to, a postsynaptic neuron's firing are weakened. The learning dynamics of anti-Hebbian STDP learning rules are stable if the timing relations obey strict constraints. The stability of these learning rules leads to clear predictions of how functional consequences can arise from the detailed structure of the plasticity. Here we review the connection between theoretical predictions and functional consequences of anti-Hebbian STDP, focusing on adaptive processing in the electrosensory system of weakly electric fish. After introducing electrosensory adaptive processing and the dynamics of anti-Hebbian STDP learning rules, we address issues of predictive sensory cancelation and novelty detection, descending control of plasticity, synaptic scaling, and optimal sensory tuning. We conclude with examples in other systems where these principles may apply.
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