Lab Positions:
Project descriptions
Software Engineer : A software engineer for Neurogrid, a specialized neuromorphic hardware platform that will emulate (simulate in real-time) a million neurons in the cortex. This project's goal is to make it easy and affordable for neuroscientists to perform simulations at a scale that can link cellular-level mechanisms to the system-level functions that they enable, thereby accelerating progress in brain research. More information.
Postdocs: Positions are available for engineers, physicists or computer scientists interested in neuroscience, or for neurobiologists interested in modeling. Joint appointment in the Deisseroth, Moore, Newsome, Shenoy, Schnitzer, or Hestrin labs, among others, is also an option. Contact Prof. Boahen to propose a project, or to find out more about those listed below.
Grads: Rotations are available to participate in any of the projects listed below. Contact Prof. Boahen to find out more, or to propose an idea. A good way to get exposed to large-scale modeling is taking BIOE332.
Undergrads: For research experience, contact a lab-member directly to express your interest in working on his or her project. For grad school, chose the program that suits your career objectives. Our lab, like others at Stanford, has a mix of students from various departments (Bioengineering, Neuroscience, EE/CS, etc.).
| Title | Description | Requirements |
|---|---|---|
Visual Perception In collaboration with Yiannis Aloimonos |
There are three-dozen cortical areas in the visual system alone. How are their distinct representations of the visual world integrated into a unified percept? Explore this question in a model of two reciprocally-connected cortical areas (e.g., V1 and MT).
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Knowledge of image processing and exposure to systems neuroscience. |
Episodic Memory |
Some memories form in a way that preserves your sense of time and place. How is the sequence in which events occur captured? Explore this question in a model of two of the hippocampus’ three regions. |
Knowledge of dynamical systems and exposure to cognitive psychology. |
Olfactory Recognition In collaboration with Gilles Laurent |
The olfactory bulb transforms a spatial pattern (receptor input) into a spatiotemporal one. How does this make odors recognizable at different concentrations, or in mixtures? Explore this question in a model of the bulb. |
Knowledge of dynamical systems and exposure to pattern classification algorithms. |
Decision Making In collaboration with Bill Newsome |
When we’re playing a game and I change the rules on you, you figure it out after a few mistakes—and adapt your strategy. Explore how this is accomplished in a model of the prefrontal cortex. | Knowledge of dynamical systems and exposure to reinforcement learning. |
| Motor Control In collaboration with Mark Schnitzer |
When you reach for a cup, you see how close you are (error) and adjust your motor commands (feedback). The error signal comes too late for fast movements however. Explore how errors are anticipated and preempted in a model of the cerebellum. | Knowledge of control theory and exposure to reinforcement learning. |
Biological Learning Mechanisms |
Synaptic plasticity is governed by spike-timing, confirming a prediction Donald Hebb made in 1949, but is not graded, as widely assumed. Explore biologically plausible timing rules that work with bistable synapses and depend on contrast. | Knowledge of learning algorithms and exposure to synaptic physiology. |
| Neural Development In collaboration with Stephen Smith |
Growing axonal arbors sprout new branches and prune old ones. Movies reveal that synapse formation prevents pruning and promotes further sprouting. Explore the power of cascading anatomical and synaptic plasticity in a model of the retinotectal system. | Knowledge of self-organizing systems and exposure to developmental neuroscience. |
| During development, cortical cells come to prefer particular stimuli—from a vertical bar in V1 to a particular face in IT. How do these preferences arise such that neighboring neurons prefer similar stimuli? Explore this question in a model of V1. | Knowledge of self-organizing systems and exposure to developmental neuroscience. |
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| Title | Description | Requirements |
|---|---|---|
Design a learning chip that uses novel devices—the goal is to replace our 23-transistor-circuit for spike-timing-dependent plasticity with a single nanoscale device. |
You should understand how solid-state devices work. Knowing simulation and fabrication tools are pluses. |
|
Nanoneurons |
Demonstrate that silicon neurons based on a versatile silicon model of ion-channel populations work when built out of nanoscale transistors that behave stochastically. |
You should understand how analog CMOS circuits work. Knowing simulation and layout tools are pluses. |
Interchip Communication |
Design and layout asynchronous digital logic to route spikes in a 2-D array of neuromorphic chips by relaying them from chip to chip, an integral part of the Neurogrid project. |
You should understand how digital CMOS circuits work. Knowing simulation and layout tools are pluses. |
Neural Simulator |
Code a graphical user interface to configure properties and connectivity of silicon neurons and to stimulate them and record their responses in real-time, an integral part of the Neurogrid project. | You should be comfortable with programming, preferably in C/C++. Knowing OpenGL is a plus. |
On-chip DAC |
Design and layout a DAC to serve as a programmable bias-voltage generator, allowing us to tune our silicon neurons and synapses after they are fabricated. This is part of the Neurogrid project. | You should understand how analog and digital CMOS circuits work. Knowing simulation and layout tools are pluses. |
On-chip ADC |
Design and layout an ADC to serve as an on-chip oscilloscope, allowing us to probe any silicon neuron or synapse on the chip (through a scanner). This is part of the Neurogrid project. |
You should understand how analog and digital CMOS circuits work. Knowledge of simulation and layout tools are all pluses. |
Silicon Retina |
Design and layout a next-generation silicon retina—with 256-by-256 pixels! You can go all the way from simulating circuits to testing a fabricated chip you designed yourself. | You should understand how analog CMOS circuits work. Knowing simulation and layout tools are pluses. |
Silicon Cochlea |
Design and layout a next-generation silicon cochlea—with 720 channels! You can go all the way from simulating circuits to testing a fabricated chip you designed yourself. | You should understand how analog CMOS circuits work. Knowing simulation and layout tools are pluses. |
PCB Design |
Design and assemble a PCB to test a neuromorphic chip, or to add functionality to an existing test set-up. | You should be comfortable hacking hardware. Familiarity with CPLDs or FPGAs is a plus. |
Chip Testing |
Devise an automated procedure to collect and analyze data from thousands of silicon neurons. | You should be comfortable with MatLab. Familiarity with C/C++ is a plus. |
