Integrating Model-Based Approaches into a Neuroscience Curriculum-An Interdisciplinary Neuroscience Course in Engineering.

Contribution: This paper demonstrates curricular modules that incorporate engineering model-based approaches, including concepts related to circuits, systems, modeling, electrophysiology, programming, and software tutorials that enhance learning in undergraduate neuroscience courses. These modules can also be integrated into other neuroscience courses. Background: Educators in biological and physical sciences urge incorporation of computation and engineering approaches into biology. Model-based approaches can provide insights into neural function; prior studies show these are increasingly being used in research in biology. Reports about their integration in undergraduate neuroscience curricula, however, are scarce. There is also a lack of suitable courses to satisfy engineering students' interest in the challenges in the growing area of neural sciences. Intended Outcomes: (1) Improved student learning in interdisciplinary neuroscience; (2) enhanced teaching by neuroscience faculty; (3) research preparation of undergraduates; and 4) increased interdisciplinary interactions. Application Design: An interdisciplinary undergraduate neuroscience course that incorporates computation and model-based approaches and has both software- and wet-lab components, was designed and co-taught by colleges of engineering and arts and science. Findings: Model-based content improved learning in neuroscience for three distinct groups: 1) undergraduates; 2) Ph.D. students; and 3) post-doctoral researchers and faculty. Moreover, the importance of the content and the utility of the software in enhancing student learning was rated highly by all these groups, suggesting a critical role for engineering in shaping the neuroscience curriculum. The model for cross-training also helped facilitate interdisciplinary research collaborations.

Open Source Software Tools for Teaching Neuroscience.

We developed an interdisciplinary course in computational neuroscience to address the need for students trained in both biological/psychological and quantitative sciences. Increasingly, exposure to advanced math and physics is important to stay on the cutting edge of developments and research in neuroscience. Additionally, the ability to work in multidisciplinary teams will continue to be an asset as the field develops. This course brings together students from biology, psychology, biochemistry, engineering, physics, and mathematics. The course was designed to highlight the importance of math in understanding fundamental neuroscience concepts and to prepare students for professional careers in neuroscience. They learn neurobiology, via a 'biology to model and back again' approach involving wet- and software/modeling-labs, with the latter being the focus of this paper. We presented a subset of the software activities described here at the 2017 Faculty for Undergraduate Neuroscience Workshop.

Distinct current modules shape cellular dynamics in model neurons.

Numerous intrinsic currents are known to collectively shape neuronal membrane potential dynamics, or neuronal signatures. Although how sets of currents shape specific signatures such as spiking characteristics or oscillations has been studied individually, it is less clear how a neuron's suite of currents jointly shape its entire set of signatures. Biophysical conductance-based models of neurons represent a viable tool to address this important question. We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. This insight into the neurobiological organization of currents also suggests an intuitive, systematic, and robust methodology to develop biophysical single-cell models with multiple biological characteristics applicable for both hand- and automated-tuning approaches. We illustrate the methodology using two example case rodent pyramidal neurons, from the lateral amygdala and the hippocampus. The methodology also helped reveal that a single-core compartment model could capture multiple neuronal properties. Such biophysical single-compartment models have potential to improve the fidelity of large network models.