Neural avalanches at the edge-of-chaos?

Does the brain operate at criticality, to optimize neural computation? Literature uses different fingerprints of criticality in neural networks, leaving the relationship between them mostly unclear. Here, we compare two specific signatures of criticality, and ask whether they refer to observables at the same critical point, or to two differing phase transitions. Using a recurrent spiking neural network, we demonstrate that avalanche criticality does not necessarily lie at edge-of-chaos

Novel insights into cochlear information processing

Already Helmholtz profoundly addressed the question how the nonlinearity of the human hearing sensor, the cochlea, might shape human sound perception. At his time, research was, however, obstructed by the lack of experimental data regarding the amplification properties of the inner ear. In the meantime, accurate measuring methods have permitted the comparison of models of the hearing sensor with empirical data, leading to a strong revival of the interest into Helmholtz’s original research questions. In our paper, we describe some recent theoretical and modeling advances in the understanding of the nature of human pitch perception. We reveal a number of to date unexplained human auditory percept effects to be direct consequences of the nonlinear properties of the mammalian hearing sensor. Our insights also demonstrate, as a by-note, the limitations of the present reverse engineering approach towards cochlear implants

Avalanche and edge-of-chaos criticality do not necessarily co-occur in neural networks

There are indications that for optimizing neural computation, neural networks may operate at criticality. Previous approaches have used distinct fingerprints of criticality, leaving open the question whether the different notions would necessarily reflect different aspects of one and the same instance of criticality, or whether they could potentially refer to distinct instances of criticality. In this work, we choose avalanche criticality and edge-of-chaos criticality and demonstrate for a recurrent spiking neural network that avalanche criticality does not necessarily entrain dynamical edge-of-chaos criticality. This suggests that the different fingerprints may pertain to distinct phenomena. In biological neural networks, scale-free avalanches of neuronal firing events have suggested that such networks might preferably operate at criticality, in particular, since theoretical studies of artificial neural networks and of cellular automata have highlighted some potential computational benefits of such a state. In these studies, notions of either edge-of-chaos criticality or avalanche criticality were adhered to. Here, using a recurrent neural network of more realistic neurons compared with what has been considered previously, we scrutinize whether these two manifestations of criticality are necessarily co-occurring. Based on a realistic paradigm of neural networks, we show that a positive largest Lyapunov exponent—indicating chaotic dynamics of the network—is conserved as we tune the network from subcritical to critical and to supercritical avalanche behavior. This demonstrates that avalanche criticality does not necessarily co-occur with edge-of-chaos criticality