Beyond spiking networks: The computational advantages of dendritic amplification and input segregation.

The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons and cannot achieve state-of-the-art performance in machine learning. Recent works have proposed that input segregation (neurons receive sensory information and higher-order feedback in segregated compartments), and nonlinear dendritic computation would support error backpropagation in biological neurons.

Endovascular revascularization of critical limb ischemia: the role of ultrasound-guided popliteal sciatic nerve block for the procedural pain management.

Objectives: To evaluate the impact of the ultrasound-guided popliteal sciatic nerve block (PSNB) for pain management during endovascular treatment of chronic limb-threatening ischemia (CLTI).

Material And Methods: From November 2020 to January 2022, 111 CLTI patients that underwent endovascular procedures were prospectively enrolled in this prospective single-arm interventional study. Ultrasound-guided PSNB was used for procedural pain control.

Error-based or target-based? A unified framework for learning in recurrent spiking networks.

The field of recurrent neural networks is over-populated by a variety of proposed learning rules and protocols. The scope of this work is to define a generalized framework, to move a step forward towards the unification of this fragmented scenario. In the field of supervised learning, two opposite approaches stand out, error-based and target-based.