'Neurons in Action' in Action - Educational settings for simulations and tutorials using NEURON

Neurons in Action (NIA1, 2000; NIA1.5, 2004; NIA2, 2007), a set of tutorials and linked simulations, is designed to acquaint students with neuronal physiology through interactive, virtual laboratory experiments. Here we explore the uses of NIA in lecture, both interactive and didactic, as well as in the undergraduate laboratory, in the graduate seminar course, and as an examination tool through homework and problem set assignments. NIA, made with the simulator NEURON (http://www.neuron.yale.edu/neuron/), displays voltages, currents, and conductances in a membrane patch or signals moving within the dendrites, soma and/or axon of a neuron. Customized simulations start with the plain lipid bilayer and progress through equilibrium potentials; currents through single Na and K channels; Na and Ca action potentials; voltage clamp of a patch or a whole neuron; voltage spread and propagation in axons, motoneurons and nerve terminals; synaptic excitation and inhibition; and advanced topics such as channel kinetics and coincidence detection. The user asks and answers "what if" questions by specifying neuronal parameters, ion concentrations, and temperature, and the experimental results are then plotted as conductances, currents, and voltage changes. Such exercises provide immediate confirmation or refutation of the student's ideas to guide their learning. The tutorials are hyperlinked to explanatory information and to original research papers. Although the NIA tutorials were designed as a sequence to empower a student with a working knowledge of fundamental neuronal principles, we find that faculty are using the individual tutorials in a variety of educational situations, some of which are described here. Here we offer ideas to colleagues using interactive software, whether NIA or another tool, for educating students of differing backgrounds in the subject of neurophysiology.

Quasi-Convolution Pyramidal Blurring

Efficient image blurring techniques based on the pyramid algorithm can be implemented on modern graphics hardware; thus, image blurring with arbitrary blur width is possible in real time even for large images. However, pyramidal blurring methods do not achieve the image quality provided by convolution filters; in particular, the shape of the corresponding filter kernel varies locally, which potentially results in objectionable rendering artifacts. In this work, a new analysis filter is designed that significantly reduces this variation for a particular pyramidal blurring technique. Moreover, the pyramidal blur algorithm is generalized to allow for a continuous variation of the blur width. Furthermore, an efficient implementation for programmable graphics hardware is presented. The proposed method is named “quasi-convolution pyramidal blurring” since the resulting effect is very close to image blurring based on a convolution filter for many applications.