| 1999 |
Latham, Peter
Nirenberg, Sheila [⬀] |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Workshop: Neural Information and Coding Workshop: March 6 Thru 9, 1999: Big Sky, Montana @ University of California-Los Angeles
Nirenberg, Shelia
IBN-9818445
The Neural Information and Coding Workshop (NIC) is a small (-60 people), intensive, three-day meeting devoted to the neural code. The objectives are two-fold: to bring together experimental and theoretical neuroscientists to share new ideas and results, and to discuss how new experimental and theoretical approaches can be combined to better understand neural coding.
Three kinds of participants are included: 1) experimentalists working on various aspects of this problem (although this group is growing rapidly, it is not yet a community, so relevant advances can go unnoticed without a forum for presentation); 2) experimentalists who are developing new technologies, such as optical imaging and multi-unit recording, that will be necessary for further progress; 3) theoreticians, since we do not currently have a unified frame work for thinking about the neural code.
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0.939 |
| 2005 — 2009 |
Latham, Peter E |
R01Activity Code Description:
To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Mechanisms of Associative and Working Memory @ University College London
DESCRIPTION (provided by applicant): Associative memory is a fundamental property of the nervous system. It allows us to retrieve memories from partial or corrupted input, and thus plays a critical role in processing and categorizing information. The mechanisms that underlie associative memory, however, are not yet understood. The leading model, a theoretical model, proposes that the nervous system performs associative memory by implementing attractor networks. In such networks, input, in the form of patterns of action potentials, provides partial information about a memory; the dynamics of the network then drives the neural activity to an attractor - a stable state in activity space - that corresponds to a complete representation of the memory. While the attractor model is a valuable construct, it is an idealized one - there is a large gap between the model and real neuronal networks. Our goal is to close this gap, so that the attractor hypothesis can be rigorously tested. To do that, we will construct biologically realistic models that match the properties of specific brain areas associated with memory-related tasks, such as prefrontal, parietal and inferotemporal cortex. These models, which we will analyze using mean-field theory and large-scale simulations, will allow us to make experimentally testable predictions. Those predictions can then be used to determine whether attractor networks exist in the brain, and if so, what their underlying structure is.
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0.958 |
