PSYC 5270 - Current Topics in Behavioral Neuroscience: Measuring and Modeling Neural Activity (Fall 2013)

Instructor: Ian Stevenson
Office: BOUS 112
Office hours by appointment
Meets in BOUS 160 on Mon 1:25 to 4:25 PM

This course aims to introduce students to recent advances in systems neuroscience with an emphasis on understanding populations of neurons. A tentative list of topics includes: new experimental techniques (large-scale electrical recording, brain machine interfaces, calcium imaging, optogenetics, connectomics), analyses (neural variability, correlations, oscillations), and advances in theory (neural coding, recurrent networks, Bayesian models of the brain). The course will focus on reading and discussion of recent research papers.

08.26Logistics and introduction to the course Alivisatos et al. Neuron, 2012.

09.02Labor Day (no class)

09.09Experimental Methods 1 - Brain Machine Interfaces Buzsaki. Nature Neuroscience, 2004.
Santhanam et al. Nature, 2006.
Velliste et al. Nature, 2006.
Hochberg et al. Nature, 2012.

09.16Experimental Methods 2 - Calcium Imaging Chen et al. Neuron, 2012.
Greenberg et al. Nature Neuroscience, 2008.
Grewe et al. Curr. Op. in Neurobio, 2009.
Ko et al. Nature, 2011.

09.23Experimental Methods 3 - Optogentics Lee et al. Nature, 2012.
Liu et al. Nature, 2012.
Steinberg et al. Nature Neuroscience, 2013.
Yizhar et al. Neuron, 2011.

09.30Presentations Escabi et al. J. Neurosci., 2002.
Rabinowitz et al. Neuron, 2011.
Olsen et al. Nature, 2012.
Fusi et al. Neuron, 2007.

10.07Analysis Methods 1 - Variability/Correlations Churchland et al. Neuron, 2011.
Cohen et al. Nature Neuroscience, 2009.
Hesselmann et al. PNAS, 2008.
Maimon et al. Neuron, 2009.

10.14Analysis Methods 2 - Oscillations Breakspear et al. F. Hum. Neuro. 2010.
Lakatos et al. Science, 2008.
Nacher et al. PNAS, 2013.

10.21Analysis Methods 3 - Neuron Modeling Kayser et al. Neuron, 2009.
Churchland et al. Nature, 2012.
MacDonald et al. Neuron, 2011.
Smith et al. Nat. Neuro., 2010.

10.28Theory 1 - Neural Coding Benucci et al. Nat. Neuro., 2009.
Chechik et al. Neuron, 2006.
Quiroga et al. Nat. Rev. Neuro., 2009.

11.04Presentations Jin et al. Hum. Brain. Map., 2012.
Frohlich et al. Neuron, 2010.
Rutishauser et al. Nature, 2010.

11.11Theory 2 - Recurrent Networks Fiser et al. Nature, 2004.
Renart et al. Science, 2010.
Reyes. Nature Neurosci, 2003.
van Vreeswijk et al. Science, 1996.
Vogels et al. Ann. Rev. Neuro., 2005.

11.18Theory 3 - Bayesian Brain Rubinov et al. PLoS Comp Biol, 2011.
Nishimoto et al. Curr. Biol., 2011.
Knill and Pouget. Trends in Neurosci, 2004.

11.25Fall Break (no class)

12.02Final Short Presentations

12.09Finals Week (no class)
Everyone will have a chance to give two presentations during the course (one long and one short). These talks can be on a topic of your choice within systems/behavioral/computational neuroscience. For the long talks it will be helpful to focus on a single paper or a small set of closely related papers so that you can thoroughly explain the motivation, methods, key results, and implications (what they did, why they did it, what they found, and why it matters). The format should be similar to a short conference talk. For long presentations allow 30min for the presentation with an additional 10min for questions/discussion. Final presentations will be in a shorter format -- allow 15min for the presentation with an additional 5min for questions.

If you'd like help selecting a topic or specific papers feel free to ask. Please send me the paper(s) you'd like to present beforehand.

Some Suggested Topics

Experimental: connectomics, adaptation, psychophysics and neural recording, photoreceptor transplantation
Analysis: voltage models of single neurons, spike-LFP interactions, coherence, information theory, reverse correlation, unitary events
Theory: activity dependent plasticity, active sensing, noise in the nervous system, natural image/sound statistics, synchrony

Enrollment information

3 credits. Instructor permission required. Open to graduate students.


Based on participation (50%) and student presentations (50%).


Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Center for Students with Disabilities as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

Academic Student Code

Academic dishonesty of any type will not be tolerated. Students should refer to the Student Code for specific guidelines (see section on Academic Integrity).

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