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.
| London, Michael; Roth, Arnd; Beeren, Lisa et al. (2010) Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466:123-7 |
| Roudi, Yasser; Nirenberg, Sheila; Latham, Peter E (2009) Pairwise maximum entropy models for studying large biological systems: when they can work and when they can't. PLoS Comput Biol 5:e1000380 |
| Beck, Jeffrey M; Ma, Wei Ji; Kiani, Roozbeh et al. (2008) Probabilistic population codes for Bayesian decision making. Neuron 60:1142-52 |
| Roudi, Yasser; Latham, Peter E (2007) A balanced memory network. PLoS Comput Biol 3:1679-700 |
| Ma, Wei Ji; Beck, Jeffrey M; Latham, Peter E et al. (2006) Bayesian inference with probabilistic population codes. Nat Neurosci 9:1432-8 |
| Latham, Peter E; Nirenberg, Sheila (2005) Synergy, redundancy, and independence in population codes, revisited. J Neurosci 25:5195-206 |
| Latham, Peter E; Nirenberg, Sheila (2004) Computing and stability in cortical networks. Neural Comput 16:1385-412 |
| Series, Peggy; Latham, Peter E; Pouget, Alexandre (2004) Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nat Neurosci 7:1129-35 |
| Nirenberg, Sheila; Latham, Peter E (2003) Decoding neuronal spike trains: how important are correlations? Proc Natl Acad Sci U S A 100:7348-53 |
| Latham, Peter E; Deneve, Sophie; Pouget, Alexandre (2003) Optimal computation with attractor networks. J Physiol Paris 97:683-94 |
Showing the most recent 10 out of 12 publications
