Statistical and dynamical properties of large cortical network models: insights into semantic memory and language

This thesis introduces several variants to the classical autoassociative memory model in order to capture different characteristics of large cortical networks, using semantic memory as a paradigmatic example in which to apply the results. Chapter 2 is devoted to the development of the sparse Potts model network as a simplification of a multi modular memory performing computations both at the local and the global level. If a network storing p global patterns has N local modules, each one active in S possible ways with a global sparseness a, and if each module is connected to cM other modules, the storage capacity scales like \u3b1c 61 pmax /cM 1d S 2 /a with logarithmic corrections. Chapter 3 further introduces adaptation and correlations among patterns, as a result of which a latching dynamics appears, consistent in the spontaneous hopping between global attractor states after an initial cue-guided retrieval, somehow similar to a free association process. The complexity of the latching series depends on the equilibrium between self-excitation of the local networks and global inhibition represented by the parameter U. Finally, Chapter 4 develops a consistent way to store and retrieve correlated patterns, which works as long as any statistical dependence between units can be neglected. The popularity of units must be introduced into the learning rule, as a result of which a new property of associative memories appears: the robustness of a memory is inverse to the information it conveys. As in some accounts of semantic memory deficits, random damage results in selective impairments, associated to the entropy measure Sf of each memory, since the minimum connectivity required to sustain its retrieval is, in optimal conditions, cM 1d pSf , and still proportional to pSf but possibly with a larger coefficient in the general case. Present in the entire thesis, but specially in this last Chapter, the conjecture stating that autoassociative memories are limited in the amount of information stored per synapse results consistent with the results

Uninformative memories will prevail: the storage of correlated representations and its consequences

Autoassociative networks were proposed in the 80's as simplified models of memory function in the brain, using recurrent connectivity with hebbian plasticity to store patterns of neural activity that can be later recalled. This type of computation has been suggested to take place in the CA3 region of the hippocampus and at several levels in the cortex. One of the weaknesses of these models is their apparent inability to store correlated patterns of activity. We show, however, that a small and biologically plausible modification in the `learning rule' (associating to each neuron a plasticity threshold that reflects its popularity) enables the network to handle correlations. We study the stability properties of the resulting memories (in terms of their resistance to the damage of neurons or synapses), finding a novel property of autoassociative networks: not all memories are equally robust, and the most informative are also the most sensitive to damage. We relate these results to category-specific effects in semantic memory patients, where concepts related to `non-living things' are usually more resistant to brain damage than those related to `living things', a phenomenon suspected to be rooted in the correlation between representations of concepts in the cortex.Comment: 24 pages, 3 Figures. Submitted to HFSP Journal. New version has .EPS figures. Now accepted in the HFSP Journal. New version includes deep structural changes following reviewers suggestion

Diagnosis for ecological intensification of maize-based smallholder farming systems in the Costa Chica, Mexico

Enhanced utilization of ecological processes for food and feed production as part of the notion of ecological intensification starts from location-specific knowledge of production constraints. A diagnostic systems approach which combined social-economic and production ecological methods at farm and field level was developed and applied to diagnose extent and causes of the perceived low productivity of maize-based smallholder systems in two communities of the Costa Chica in South West Mexico. Social-economic and production ecological surveys were applied and complemented with model-based calculations. The results demonstrated that current nutrient management of crops has promoted nutrition imbalances, resulting in K- and, less surprisingly N-limited production conditions, reflected in low yields of the major crops maize and roselle and low resource use efficiencies. Production on moderate to steep slopes was estimated to result in considerable losses of soil and organic matter. Poor crop production, lack of specific animal fodder production systems and strong dependence on animal grazing within communal areas limited recycling of nutrients through manure. In combination with low prices for the roselle cash crop, farmers are caught in a vicious cycle of cash shortage and resource decline. The production ecological findings complemented farmers opinions by providing more insight in background and extent of livelihood constraints. Changing fertilizer subsidies and rethinking animal fodder production as well as use of communal lands requires targeting both formal and informal governance structures. The methodology has broader applicability in smallholder systems in view of its low demand on capital intensive resource