Topic: Memory capacity

It seems we see the world in full detail. However, the eye is not a camera nor is the brain a computer. Incredible metabolic constraints render us unable to encode more than a fraction of information available in each glance. Instead, our illusion of stable and complete perception is accomplished by parsimonious representation relying on natural order inherent in the surrounding environment. I will begin by discussing previous behavioral work from our lab demonstrating one such strategy by which the visual system represents average properties of Gestalt-grouped sets of individual objects, warp

Contrary to typical artificial neural network (ANN) design, biological neurons are not identical. Neurons differ substantially in their physiological properties. Heterogeneity has been hypothesized to increase the dimensionality of the neural dynamics, which improves the encoding properties of a network [1], promotes robustness and stability [2], and maximizes information flow in large networks [3]. We aim to show the functional effect of heterogeneity in rate-based recurrent neural networks. To vary the degree of heterogeneity, we introduce neuron types, with each neuron type having its own

A central question in systems neuroscience is what features of neural networks determine their memory capacity and whether these features are optimized in the brain. Here, we provide an analytical estimate of the memory capacity for a general class of network models. Our derivation extends previous theoretical results [1], which assumed homogeneous connectivity and coding levels (i.e., cell activation probabilities in the memory patterns), to models with arbitrary network architectures (i.e., different constraints on the arrangement of connections between cells) and heterogeneous coding levels