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, I A and I C, differentially affected the type-B cells themselves, with I C reductions increasing firing rate (excitability) in response to light, and I A reductions increasing quantal output (synaptic strength) onto postsynaptic targets. Increases in either type-B cell 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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Fost, J.W., Clark, G.A. Modeling Hermissenda: II. Effects of variations in type-B cell excitability, synaptic strength, and network architecture. J Comput Neurosci 3, 155–172 (1996). https://doi.org/10.1007/BF00160810
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DOI: https://doi.org/10.1007/BF00160810