Theoretical Exploration of Flexible Transmitter Model
This provides foundational insights into a new bio-plausible neuron model, potentially advancing neural network theory and applications in temporal-spatial signal processing.
The paper theoretically analyzes the Flexible Transmitter (FT) neuron model, showing that FT networks are universal approximators with potentially exponential complexity reduction compared to standard neural networks and have no local minima, enabling global optimization via local search.
Neural network models generally involve two important components, i.e., network architecture and neuron model. Although there are abundant studies about network architectures, only a few neuron models have been developed, such as the MP neuron model developed in 1943 and the spiking neuron model developed in the 1950s. Recently, a new bio-plausible neuron model, Flexible Transmitter (FT) model, has been proposed. It exhibits promising behaviors, particularly on temporal-spatial signals, even when simply embedded into the common feedforward network architecture. This paper attempts to understand the properties of the FT network (FTNet) theoretically. Under mild assumptions, we show that: i) FTNet is a universal approximator; ii) the approximation complexity of FTNet can be exponentially smaller than those of commonly-used real-valued neural networks with feedforward/recurrent architectures and is of the same order in the worst case; iii) any local minimum of FTNet is the global minimum, implying that it is possible to identify global minima by local search algorithms.