Publications by authors named "J C Schemmel"
Background: Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform.
View Article and Find Full Text PDFArticle Synopsis
- The BrainScaleS-2 system is an advanced analog neuromorphic platform used in computational neuroscience and spike-based machine learning.
- The system now features a configurable real-time event interface, enhancing its connection with external sensors and actuators for improved performance.
- A demonstration involves using PyTorch to train a spiking neural network for controlling brushless DC motors, enabling high-speed robotics research focused on event-driven control and online learning.
We report on a survey of 258 psychotherapists from Germany, focusing on their experiences with memory recovery in general, suggestive therapy procedures, evaluations of recovered memories, and memory recovery in training and guidelines. Most therapists (78%) reported instances of memory recovery encompassing negative and positive childhood experiences, but usually in a minority of patients. Also, most therapists (82%) reported to have held assumptions about unremembered trauma.
View Article and Find Full Text PDFObjectives: We tested the effect of true and fabricated baseline statements from the same sender on veracity judgments.
Hypotheses: We predicted that presenting a combination of true and fabricated baseline statements would improve truth and lie detection accuracy, while presenting a true baseline would improve only truth detection, and presenting a fabricated baseline would only improve lie detection compared with presenting no baseline statement.
Method: In a 4 × 2 within-subjects design, 142 student participants ( = 23.
Article Synopsis
- Neuromorphic systems are new types of computer systems that help scientists explore and research better, but making them easy to use and efficient is tricky.
- The BrainScaleS-2 system is a special kind of neuromorphic hardware that uses unique software features to make it easier for researchers to run experiments.
- The text talks about improvements like faster training methods, new types of neurons, and better access for users, plus plans for making the hardware even bigger and easier to work with in the future.