A refined information processing capacity metric allows an in-depth analysis of memory and nonlinearity trade-offs in neurocomputational systems.

Since dynamical systems are an integral part of many scientific domains and can be inherently computational, analyses that reveal in detail the functions they compute can provide the basis for far-reaching advances in various disciplines. One metric that enables such analysis is the information processing capacity. This method not only provides us with information about the complexity of a system's computations in an interpretable form, but also indicates its different processing modes with different requirements on memory and nonlinearity. In this paper, we provide a guideline for adapting the application of this metric to continuous-time systems in general and spiking neural networks in particular. We investigate ways to operate the networks deterministically to prevent the negative effects of randomness on their capacity. Finally, we present a method to remove the restriction to linearly encoded input signals. This allows the separate analysis of components within complex systems, such as areas within large brain models, without the need to adapt their naturally occurring inputs.

Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model.

The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli.