|M.Sc Student||Shimron Efrat|
|Subject||A Model for "What" and "Where" Information in the Cortex|
|Department||Department of Medicine||Supervisors||PROF. Ron Meir|
|PROFESSOR EMERITUS Yehoshua Zeevi|
|Full Thesis text|
In contrast to the classical theory of segregated cortical representation of information regarding the identity ("what") and location ("where") of a visual object, recent experimental results indicate that neurons in the primate prefrontal cortex convey combined information about both. Moreover, this information was found to be maintained after stimulus removal, suggesting that these neurons contribute to working memory. To date, the mechanisms that underlie this integrated representation are unknown. In this study, we propose a model of a cortical network with biologically realistic properties that demonstrates a combined representation of stimulus identity and location. The network is comprised of neurons embedded in a two-dimensional surface, and involves two types of connections. The first, memory-related connections, have weights that are modified using Hebbian learning to store a set of memory patterns in the network; Based on experimental evidence related to the locality and sparseness of cortical connectivity, the probability of creating these connections is assumed to be a narrow Gaussian function of distance. Secondly, lateral inhibition is implemented through a Mexican hat type of interaction; accordingly, each neuron projects inhibitory synapses on neurons that are located on a ring that is close, but not adjacent, to it. When the network is given a localized cue in the form of a short-time injected current, the attractor dynamics of the memory-related connections drive it to a state that retrieves the cued pattern, which codes the stimulus identity. At the same time, the lateral inhibition mechanism restricts the activity to a small area around the stimulated location, so the network exhibits an activity "bump" that codes the stimulus location. Interestingly, we also find that the combined representation of stimulus identity and position can be obtained in networks with small world topology, which is considered more realistic than the fully local one. In this connectivity scheme, a small fraction of the memory-related local connections is replaced by random, long-term connections. Furthermore, our results indicate that the integrated representation is robust with regard to the signal-to-noise ratio of the cue. In our model, the combination of associative memory and lateral inhibition mechanisms enables the network to exhibit an activity profile that reflects both the "where" and "what" properties of a visual stimulus. The interplay between these mechanisms results in a tradeoff between the conveyed information qualities. Such tradeoffs are common in biological systems, and they reflect a constraint imposed by the universal principle of uncertainty.