Passing the Message: Representation Transfer in Modular Balanced Networks

Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems’ computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge towards which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks. Author summary To interact with the external environment in real-time, cortical microcircuits must employ efficient and reliable mechanisms for passing information between different modules and for integrating input from multiple sources. In this study we investigate, from a functional perspective, how structural features influence these mechanisms in the context of stimulus representation, integration and transfer in modular spiking networks. We demonstrate that biologically plausible patterned connectivity, inspired by cortical topographic maps, is necessary for information to propagate across multiple modules in a useful manner. Compared to purely random projections, topographic maps improve computational performance and efficiency considerably, leading to more stable responses and increased robustness against interference effects. In addition, architectural specificities also play an important role in how networks combine information from different sources. Our results suggest that early and local integration of input streams enables more accurate non-linear computations in deeper modules than first transferring intermediate representations and only then performing the computations downstream. These findings demonstrate that the wiring architecture can profoundly impact information transmission and integration in neuronal circuits, with structured connectivity in form spatially segregated topographic projections playing a potentially key role in sustaining accurate cortical communication over multiple networks.