Nonlinear Response Functions of Drilled Orifice Injectors

Neural circuits are structured with layers of converging and diverging connectivity, and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered individually. These results show that the combination of selective nonlinearities and a convergent architecture - both elements that reduce information when acting separately - can promote efficient coding.Significance StatementComputation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about inputs. Past work has detailed how optimized nonlinearities and circuit weights can maximize information, but here, we show that incorporating non-invertible nonlinearities into a circuit with divergence and convergence, can enhance encoded information despite the suboptimality of these components individually. This study extends a broad literature on efficient coding to convergent circuits. Our results suggest that neural circuits may preserve more information using suboptimal components than one might expect.

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Semiclassical nonlinear response functions for coupled anharmonic vibrations

The Journal of Chemical Physics ◽

10.1063/1.3266566 ◽

2009 ◽

Vol 131 (20) ◽

pp. 204504 ◽

Cited By ~ 7

Author(s):

Scott M. Gruenbaum ◽

Roger F. Loring

Keyword(s):

Nonlinear Response ◽

Response Functions ◽

Anharmonic Vibrations

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Combined Stress and Resistance Modeling With the Nessus Software System

9th Biennial Conference on Reliability, Stress Analysis, and Failure Prevention ◽

10.1115/detc1991-0008 ◽

1991 ◽

Author(s):

B. H. Thacker ◽

S. V. Harren ◽

H. R. Millwater

Keyword(s):

Nonlinear Response ◽

Structural Reliability ◽

Mean Value ◽

Response Functions ◽

Structural Components ◽

Combined Stress ◽

Probabilistic Algorithm ◽

Highly Nonlinear ◽

Advanced Mean Value ◽

Resistance Modeling

Abstract Computing the reliability of structural components with uncertainties in both stress and resistance variables is investigated. The NESSUS™ probabilistic software is used to perform the reliability computations using both Fast Probability Integration (FPI) and Monte Carlo methods. Results are compared for an example reliability analysis using both closed-form expressions and finite elements. The Advanced Mean Value probabilistic algorithm is used and is shown to yield highly accurate results, even for highly nonlinear response functions. The results show clearly the effectiveness of the newly incorporated algorithms available in the NESSUS software for computing structural reliability.

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