Decoding the Spike-Band Subthreshold Motor Cortical Activity.

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

A behavioral paradigm for cortical control of a robotic actuator by freely moving rats in a one-dimensional two-target reaching task.

BACKGROUND: Controlling the trajectory of a neuroprosthesis to reach distant targets is a commonly used brain-machine interface (BMI) task in primates and has not been available for rodents yet. NEW METHOD: Here, we describe a novel, fine-tuned behavioral paradigm and setup which enables this task for rats in one-dimensional space for reaching two distant targets depending on their limited cognitive and visual capabilities compared to those of primates. An online transform was used to convert the activity of a pair of primary motor cortex (M1) units into two robotic actions. The rats were shaped to adapt to the transform and direct the robotic actuator toward the selected target by modulating the activity of the M1 neurons. RESULTS: All three rats involved in the study were capable of achieving randomly selected targets with at least 78% accuracy. A total of 9 out of 16 pairs of units examined were eligible for exceeding this success criterion. Two out of three rats were capable of reversal learning, where the mapping between the activity of the M1 units and the robotic actions were reversed. COMPARISON WITH EXISTING METHODS: The present work is the first demonstration of trajectory-based control of a neuroprosthetic device by rodents to reach two distant targets using visual feedback. CONCLUSION: The behavioral paradigm and setup introduced here can be used as a cost-effective platform for elucidating the information processing principles in the neural circuits related to neuroprosthetic control and for studying the performance of novel BMI technologies using freely moving rats.

Effects of different soil tillage-sowing systems on plant development and emergence traits of second crop soybean / Efeitos de diferentes sistemas de semeadura direta no solo e desenvolvimento de plantas e características de emergência de soja segunda safra

Effects of different soil tillage and sowing systems (Conventional Soil Tillage ­ CST: Moldboard plow + gobble disc + disk harrow + harrow + sowing machine; Reduced Soil Tillage ­ RST: rototiller-combined soil tillage machine + sowing machine; Ridge Sowing ­ RS: gobble disc + ridge-sowing machine; Direct Sowing ­ DS ­ no-till) on plant development and emergence traits of second-crop soybean were investigated in this study under Antalya provincial conditions. Experiments were conducted over the experimental fields of the Aksu Branch of Bati Akdeniz Agricultural Research Institute for 3 years (2013, 2014, 2015) as a fixed experiment. The number of days to 50% emergence, number of days to 50% flowering, plant height, number of pods per plant, the first pod height, number of plants per m2, 1000-seed weight, and yield were considered as plant development parameters. Mean emergence time, germination rate index, emergence ratio, space ratio, tillering ratio, and acceptable plant spacing ratio were considered as plant emergence parameters. Different soil tillage and sowing systems generally had significant effects on investigated traits at p<0.01 and p<0.05 levels.

Efeitos de diferentes sistemas de preparo do solo e de semeadura (preparo convencional do solo - CST: arado de aiveca + disco de pastilha + grade de disco + grade + semeadora; preparo do solo reduzido - RST: máquina de preparo do solo combinada com rototiller + máquina de semeadura; semeadura em crista - RS: disco de gobble + semeadora em crista; semeadura direta - DS - plantio direto) sobre o desenvolvimento da planta e as características de emergência da soja de segunda safra foram investigadas neste estudo nas condições da província de Antalya. Os experimentos foram conduzidos nos campos experimentais da filial de Aksu do Bati Akdeniz Agricultural Research Institute por 3 anos (2013, 2014, 2015) como um experimento fixo. O número de dias para 50% de emergência, número de dias para 50% de floração, altura da planta, número de vagens por planta, altura da primeira vagem, número de plantas por m2, peso de 1000 sementes e rendimento foram considerados como parâmetros de desenvolvimento da planta. Tempo médio de emergência, índice de taxa de germinação, razão de emergência, razão de espaço, razão de perfilhamento e razão de espaçamento de plantas aceitável foram considerados como parâmetros de emergência das plantas. Diferentes sistemas de preparo do solo e de semeadura geralmente tiveram efeitos significativos nas características investigadas nos níveis p <0,01 e p <0,05.

Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.