We have compared two different resource arbitration architectures in our developed data acquisition and stimuli generator system for neuroscience research, entirely specified in a high-level Hardware Description Language (HDL). One of them was designed with a decoupled and latency insensitive modular approach, allowing for easier code reuse, while the other adopted a centralized scheme, constructed specifically for our application. The usage of a high-level HDL allowed straightforward and stepwise code modifications to transform one architecture into the other. Despite the logic complexity penalty of synthesizing our hardware from a highly abstract language, both architectures were implemented in a very small programmable logic device without even consuming all the hardware resources. While the decoupled design has shown more resilience to input activity bursts, the centralized one gave an economy of about 10-15% in the device logic element usage. This system is not only useful for neuroscience protocols that require timing determinism and synchronous stimuli generation, but has also demonstrated that high-level languages can be effectively used for synthesizing hardware in small programmable devices.

We study the reconstruction of visual stimuli from spike trains, representing the reconstructed stimulus by a Volterra series up to second order. We illustrate this procedure in a prominent example of spiking neurons, recording simultaneously from the two H1 neurons located in the lobula plate of the fly Chrysomya megacephala. The fly views two types of stimuli, corresponding to rotational and translational displacements. Second-order reconstructions require the manipulation of potentially very large matrices, which obstructs the use of this approach when there are many neurons. We avoid the computation and inversion of these matrices using a convenient set of basis functions to expand our variables in. This requires approximating the spike train four-point functions by combinations of two-point functions similar to relations, which would be true for gaussian stochastic processes. In our test case, this approximation does not reduce the quality of the reconstruction. The overall contribution to stimulus reconstruction of the second-order kernels, measured by the mean squared error, is only about 5% of the first-order contribution. Yet at specific stimulus-dependent instants, the addition of second-order kernels represents up to 100% improvement, but only for rotational stimuli. We present a perturbative scheme to facilitate the application of our method to weakly correlated neurons.