Scene statistics and noise determine the relative arrangement of receptive field mosaics

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

The optimal spatial arrangement of ON and OFF receptive fields

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a second-order phase transition between aligned and anti-aligned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide novel theoretical predictions for the configuration of opponent pathways in the nervous system.

Kinetics of chemical processes in the human brain. Trigger effect and self-stabilization of N-acetylaspartic acid

A kinetic model was proposed for the response of nerve tissue to an external signal stimulus. The model is based on the views of a multistep and non-linear nature of the dynamic variation of the concentrations of N-acetylaspartic acid and N-acetylaspartate in the human nerve tissue. The substrate inhibition effect in this system is a necessary factor for the self-stabilization of N-acetylaspartate as a key brain metabolite. The existence of three stationary states accounts for the trigger behavior of the system.

Functional diversity among sensory neurons from efficient coding principles

In many sensory systems the neural signal is coded by the coordinated response of heterogeneous populations of neurons. What computational benefit does this diversity confer on information processing? We derive an efficient coding framework assuming that neurons have evolved to communicate signals optimally given natural stimulus statistics and metabolic constraints. Incorporating nonlinearities and realistic noise, we study optimal population coding of the same sensory variable using two measures: maximizing the mutual information between stimuli and responses, and minimizing the error incurred by the optimal linear decoder of responses. Our theory is applied to a commonly observed splitting of sensory neurons into ON and OFF that signal stimulus increases or decreases, and to populations of monotonically increasing responses of the same type, ON. Depending on the optimality measure, we make different predictions about how to optimally split a population into ON and OFF, and how to allocate the firing thresholds of individual neurons given realistic stimulus distributions and noise, which accord with certain biases observed experimentally.

Design and Implementation of Intermediate Frequency Generation and Analysis Module for Avionics Test

To test airborne avionics device, it is necessary to provide signal stimulus for the device under test (DUT) to simulate the real work environment. This paper proposes a hardware module which used to signal generate and analyze. The hardware structure and diagram of logic design are described. The generated waveforms and measurement results are presented. This test module combined with other necessary modules can achieve the test of L band airborne avionics such as ATC, TCAS and TACAN.