James Jeanne, PhD
Research & Publications
Biography
Research Summary
To appropriately interact with the external world, we must continuously evaluate incoming sensory information, integrate it with previously acquired information, and choose appropriate behavioral responses. How does the brain coordinate these goals? The operation of single neurons and small circuits can be explained by a range of cellular and biophysical mechanisms. “Higher-order” neural functions—such as sensory perception, memory, and decision making—can be characterized with increasingly sophisticated algorithmic descriptions. Yet a gap in our understanding still exists between these mechanisms and algorithms.
The long-term goal of the Jeanne Lab is to close this gap by understanding neural computation. Our mission is therefore
- to elucidate the biophysical, cellular, and circuit principles that govern neural computation
- to understand how neural computation gives rise to higher-order neural functions and behavior
Our approach is to study neural computation in the fruit fly. We use the fly because their small brains are simpler to understand, yet nonetheless capable of performing a suite of higher-order functions. We use a wide range of techniques, but have a strong emphasis on in vivo whole-cell patch clamp electrophysiology, 2-photon imaging, optogenetics, and behavior.
Research Interests
Behavior; Drosophila melanogaster; Electrophysiology; Insecta; Neurons; Synapses
Research Image
2-photon optogenetic stimulation of an olfactory neuron in the Drosophila brain
Selected Publications
- Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and AccuracyJeanne JM, Wilson RI. Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy Neuron 2015, 88: 1014-1026. PMID: 26586183, PMCID: PMC5488793, DOI: 10.1016/j.neuron.2015.10.018.
- Associative Learning Enhances Population Coding by Inverting Interneuronal Correlation PatternsJeanne JM, Sharpee TO, Gentner TQ. Associative Learning Enhances Population Coding by Inverting Interneuronal Correlation Patterns Neuron 2013, 78: 352-363. PMID: 23622067, PMCID: PMC3641681, DOI: 10.1016/j.neuron.2013.02.023.
- Local inhibition modulates learning-dependent song encoding in the songbird auditory cortexThompson JV, Jeanne JM, Gentner TQ. Local inhibition modulates learning-dependent song encoding in the songbird auditory cortex Journal Of Neurophysiology 2012, 109: 721-733. PMID: 23155175, PMCID: PMC3567384, DOI: 10.1152/jn.00262.2012.
- Emergence of Learned Categorical Representations within an Auditory Forebrain CircuitJeanne JM, Thompson JV, Sharpee TO, Gentner TQ. Emergence of Learned Categorical Representations within an Auditory Forebrain Circuit Journal Of Neuroscience 2011, 31: 2595-2606. PMID: 21325527, PMCID: PMC3060658, DOI: 10.1523/jneurosci.3930-10.2011.