Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

… A neurorobot model explored the idea of dopaminergic and serotonergic opponency by
having the serotonergic system directly inhibit the dopaminergic system (Krichmar, 2013). In this …

It is generally assumed that the variability of neuronal morphology has an important effect
on both the connectivity and the activity of the nervous system, but this effect has not been …

Neural network simulators that take into account the spiking behavior of neurons are useful
for studying brain mechanisms and for various neural engineering applications. Spiking …

Neuromorphic hardware implements biological neurons and synapses to execute a spiking
neural network (SNN)-based machine learning. We present SpiNeMap, a design …

We investigated the effect of morphological differences on neuronal firing behavior within
the hippocampal CA3 pyramidal cell family by using three-dimensional reconstructions of …

We introduce L-Neuron (www.krasnow.gmu.edu/L-Neuron), a software package for the
generation and study of anatomically accurate neuronal analogs. L-Neuron is based on sets of …

Understanding how the human brain is able to efficiently perceive and understand a visual
scene is still a field of ongoing research. Although many studies have focused on the design …

Biological organisms have the ability to respond quickly to an ever-changing world. Because
this adaptability is so critical for survival, all vertebrates have sub-cortical structures, which …

… Dutt, and JL Krichmar, “An efficient simulation environment for modeling large-scale cortical
… Dutt, and JL Krichmar, “A GPU-accelerated cortical neural network model for visually guided …