Frances ChanceAssistant Professor, Neurobiology and Behavior |
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Research Interests |
sensory processing by cortical circuitry, computational modeling | |
| URL | chancelab.bio.uci.edu | |
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Research Abstract |
How are the responses of neurons in behaving animals generated by underlying neuronal circuitry? In my laboratory, we seek to answer this question by exploring how the biophysical properties of neurons and synapses produce the functional response characteristics of neurons. We hope to uncover basic features of neuronal integration and response generation by recording from neurons in brain slices, and to reveal the network mechanisms that underlie functioning neural circuits by studying computational models of brain circuitry. Our ultimate goal is to provide a basis for understanding how information is processed by neural circuits within the brain. One question that my research has focused on is the effect of background synaptic input on neuronal responses. In vivo, cortical neurons receive an enormous barrage of excitatory and inhibitory background inputs along with the stimulus-dependent input that drives their responses. The presence of this background input is a fundamental difference between the in vivo and in vitro environments, and its functional role has been a long-standing puzzle in neuroscience. In my laboratory we record from brain slices because this preparation allows us to easily identify and record from individual neurons. We then use a technique called the dynamic clamp to mimic background synaptic input, bringing this aspect of the in vivo environment into the in vitro preparation. In this way we can examine aspects of how neurons process information in the intact brain while still having all the advantages of the slice preparation. One finding that has arisen from this research is that background synaptic input can have a multiplicative scaling effect on neuronal firing rates, acting like a volume control for neuronal responses. The result described above provides a mechanism by which neurons can multiply two different signals. This greatly expands the range of computations that biological neural circuits can perform. We explore this added functionality by constructing model neural networks, heavily based on what is known about cortical circuitry, and incorporating this novel form of modulation. A wide variety of multiplicative gain modulation effects (for example that of attention in visual areas) have been reported in recordings from behaving animals. Our research seeks to explain the underlying network mechanisms that lead to these effects, and in this way contribute to understanding how cortical circuits function and process information. |
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| Publications | Chance, F.S., Abbott, L.F. & Reyes, A.D. (2002) Gain Modulation from Background Synaptic Input. Neuron 35: 773-782. | |
| Abbott, L.F. & Chance, F.S. (2002) Rethinking the taxonomy of visual neurons. Nature Neuroscience 5: 391-392. | ||
| Brunel, N., Chance, F.S., Fourcard, N. & Abbott, L.F. (2001) Effects of Synaptic Noise and Filtering on the Frequency Response of Spiking Neurons. Physical Review Letters 86: 2186-2189. | ||
| Chance, F.S. & Abbott, L.F. (2000) Divisive Inhibition in Recurrent Networks. Network 11: 119-129. | ||
| Chance, F.S., Nelson, S.B. & Abbott, L.F. (1999) Complex Cells as Cortically Amplified Simple Cells. Nature Neuroscience 2: 277-282 | ||
| Chance, F.S, Nelson, S.B. & Abbott, L.F. (1998) Synaptic Depression and the Temporal Response Characteristics of V1 Cells. Journal of Neuroscience 18: 4785-4799. | ||
| Graduate Programs |
Neurobiology and Behavior Interdepartmental Neuroscience Program |
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| Link to this profile | http://www.faculty.uci.edu/profile.cfm?faculty_id=4960 | |
| Last updated | 10/06/2003 | |