ArticleJournal of computational neuroscience1996
Modeling Hermissenda: II. Effects of variations in type-B cell excitability, synaptic strength, and network architecture.
Article in Journal of computational neuroscience, 1996. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed, 9 citations in OpenAlex.
- Subcellular, cellular, and circuit mechanisms underlying classical conditioning in Hermissenda crassicornis.Anatomical record. Part B, New anatomist · 2006Review
- Modeling Hermissenda: I. Differential contributions of IA and IC to type-B cell plasticity.Journal of computational neuroscience · 1996Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
Because the Hermissenda eye is relatively simple and its cells well characterized, it provides an attractive preparation for detailed computational analysis. To examine the neural mechanisms of learning in this system, we developed multicompartmental models of the type-A and type-B photoreceptors, simulated the eye, and asked three questions: First, how do conductance changes affect cells in a network as compared with those in isolation; second, what are the relative contributions of increases in B-cell excitability and synaptic strength to network output; and third, how do these contributions vary as a function of network architecture? We found that reductions in the type-B cells of two K+ currents, IA and IC, differentially affected the type-B cells themselves, with IC reductions increasing firing rate (excitability) in response to light, and IA reductions increasing quantal output (synaptic strength) onto postsynaptic targets. Increases in either type-B cel excitability or synaptic strength, induced directly or indirectly, each suppressed A-cell photoresponses, and the combined effect of both changes occurring together was greater than either alone. To examine the effects of network architecture, we compared the full network with a simple feedforward B-A pair and intermediate configurations. Compared with a feedforward pair, the complete network exhibited greater A-cell sensitivity to B-cell changes. This was due to many factors, including an increased number of B-cells (which increased B-cell impact on A-cells), A-B feedback inhibition (which slowed both cell types and altered spike timing relationships), and B-B lateral inhibition (which reduced B-cell sensitivity to intrinsic biophysical modifications). These results suggest that an emergent property of the network is an increase both in the rate of information acquisition ("learning") and in the amount of information that can be stored ("memory").
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