  This paper proposes the MBURST, a novel multimodal solution for audio-visual
speech enhancements that consider the most recent neurological discoveries
regarding pyramidal cells of the prefrontal cortex and other brain regions. The
so-called burst propagation implements several criteria to address the credit
assignment problem in a more biologically plausible manner: steering the sign
and magnitude of plasticity through feedback, multiplexing the feedback and
feedforward information across layers through different weight connections,
approximating feedback and feedforward connections, and linearizing the
feedback signals. MBURST benefits from such capabilities to learn correlations
between the noisy signal and the visual stimuli, thus attributing meaning to
the speech by amplifying relevant information and suppressing noise.
Experiments conducted over a Grid Corpus and CHiME3-based dataset show that
MBURST can reproduce similar mask reconstructions to the multimodal
backpropagation-based baseline while demonstrating outstanding energy
efficiency management, reducing the neuron firing rates to values up to
\textbf{$70\%$} lower. Such a feature implies more sustainable implementations,
suitable and desirable for hearing aids or any other similar embedded systems.
