Dynamic Neural Fields with Intrinsic Plasticity

Abstract

Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building blocks in architectures to model sensorimotor embedding of cognitive processes. Typically, the parameters of a DNF in an architecture are manually tuned in order to achieve a specific dynamic behavior (e.g., decision making, selection, or working memory) for a given input pattern. This manual parameters search requires expert knowledge and time to find and verify a suited set of parameters. The DNF parametrization may be particular challenging if the input distribution is not known in advance, e.g., when processing sensory information. In this paper, we propose the autonomous adaptation of the DNF resting level and gain by a learning mechanism of intrinsic plasticity (IP). To enable this adaptation, an input and output measure for the DNF are introduced, together with a hyper parameter to define the desired output distribution. The online adaptation by IP gives the possibility to pre-define the DNF output statistics without knowledge of the input distribution and thus, also to compensate for changes in it. The capabilities and limitations of this approach are evaluated in a number of experiments.

Reconfigurable Computation in Spiking Neural Networks

“…Varying k means that a small network is already capable of performing many different functions while the low spike count may translate in low power approaches to computation. Thus, our results may potentially also contribute to the growing field of artificial cognitive computing and related topics [28][29][30][31][32][33][34][35].…”

“…reconfigurability and/or simple topology, for specific features as output stability and sensitivity to weak input signals. For example Heteroclinic Computing [5], [19], [27] offers sensitivity to arbitrarily small signals (up to the noise level), because it relies only on unstable states, while neural fields [8], [28] provide a soft-WTA with macroscopic stability (population dynamics) via short-range excitation and longrange inhibition. Our model makes use of stable orbits and can robustly compute in a given time over a broad range of input magnitudes.…”

Discrete Dynamics of Dynamic Neural Fields

“…Plasticity in neuroscience can be thought of as the ability of the brain to adapt to external activities by modifying some of its structure. Connections between plasticity and DNFs have been made in studies on intrinsic plasticity, see Strub et al ( 2017 ) or parallel works such as Neumann and Steil ( 2011 ), Pozo and Goda ( 2010 ), and the references therein. Robotics has been a great niche for DNFs with the works of Bicho et al ( 2000 ), Erlhagen and Schöner ( 2001 ), Erlhagen and Bicho ( 2006 ), and Bicho et al ( 2010 ).…”