Slow Firing Single Units Are Essential for Optimal Decoding of Silent Speech

Summary The motivation of someone who is locked-in, that is, paralyzed and mute, is to find relief for their loss of function. The data presented in this report is part of an attempt to restore one of those lost functions, namely, speech. An essential feature of the development of a speech prosthetic is optimal decoding of patterns of recorded neural signals during silent or covert speech, that is, speaking ‘inside the head’ with no audible output due to the paralysis of the articulators. The aim of this paper is to illustrate the importance of both fast and slow single unit firings recorded from an individual with locked-in syndrome and from an intact participant speaking silently. Long duration electrodes were implanted in the motor speech cortex for up to 13 years in the locked-in participant. The data herein provide evidence that slow firing single units are essential for optimal decoding accuracy. Additional evidence indicates that slow firing single units can be conditioned in the locked-in participant five years after implantation, further supporting their role in decoding.

Synaptic diversity naturally arises from neural decoding of heterogeneous populations

The synaptic inputs to single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. We propose that synaptic diversity arises because neurons decode information from upstream populations. Focusing on a single sensory variable, orientation, we construct a probabilistic decoder that estimates the stimulus orientation from the responses of a realistic, hypothetical input population of neurons. We provide a straightforward mapping from the decoder weights to real excitatory synapses, and find that optimal decoding requires diverse input weights. Analytically derived weights exhibit diversity whenever upstream input populations consist of noisy, correlated, and heterogeneous neurons, as is typically found in vivo. In fact, in silico weight diversity was necessary to accurately decode orientation and matched the functional heterogeneity of dendritic spines imaged in vivo. Our results indicate that synaptic diversity is a necessary component of information transmission and reframes studies of connectivity through the lens of probabilistic population codes. These results suggest that the mapping from synaptic inputs to somatic selectivity may not be directly interpretable without considering input covariance and highlights the importance of population codes in pursuit of the cortical connectome.

A Novel LT Scheme without Feedback Messages for IoT of Smart City Scenarios

For IoTs of smart city scenarios always with the low cost, low power consumption, and high transmission delay properties, the traditional protocols based on feedback messages, e.g., the Automatic Repeat reQuest (ARQ) schemes, would dramatically affect the transmission efficiency. Therefore, the LT codes with only one feedback message in each entire coding process can be used to substitute the traditional protocols. As in many IoTs of smart city scenarios, the data must have both high transmission efficiency and timeliness requirements; thus, the negative effect of only the feedback message in each entire coding process cannot be neglected in such transmission environments. To enhance the transmission efficiency of such ensembles, a novel LT scheme without feedback messages is proposed in this paper. By presenting the definitions of optimal decoding overhead and recovery ratio per symbol, the optimal decoding overhead of LT codes can be found directly, then the encoding overhead of the encoder can be predesigned also. For this reason, the feedback messages in LT schemes can be removed. By using the proposed LT scheme, the transmission efficiency of IoT of smart city scenarios can be enhanced.