A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling.

Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.

Real-time human-robot interaction underlying neurorobotic trust and intent recognition.

In the past three decades, the interest in trust has grown significantly due to its important role in our modern society. Everyday social experience involves "confidence" among people, which can be interpreted at the neurological level of a human brain. Recent studies suggest that oxytocin is a centrally-acting neurotransmitter important in the development and alteration of trust. Its administration in humans seems to increase trust and reduce fear, in part by directly inhibiting the amygdala. However, the cerebral microcircuitry underlying this mechanism is still unknown. We propose the first biologically realistic model for trust, simulating spiking neurons in the cortex in a real-time human-robot interaction simulation. At the physiological level, oxytocin cells were modeled with triple apical dendrites characteristic of their structure in the paraventricular nucleus of the hypothalamus. As trust was established in the simulation, this architecture had a direct inhibitory effect on the amygdala tonic firing, which resulted in a willingness to exchange an object from the trustor (virtual neurorobot) to the trustee (human actor). Our software and hardware enhancements allowed the simulation of almost 100,000 neurons in real time and the incorporation of a sophisticated Gabor mechanism as a visual filter. Our brain was functional and our robotic system was robust in that it trusted or distrusted a human actor based on movement imitation.