Associative memory

Uri Cohen examines how stable memories can emerge in recurrent networks without imposing specially engineered dynamics such as a Lyapunov gradient. The analysis concerns discrete fixed points in networks with graded activity and noise. Random matrix methods characterize both the bulk and outlier eigenvalues of the local Jacobians. This yields a stability threshold: either all fixed points are stable or all are unstable, depending on their number. That threshold differs from the usual storage capacity, which counts achievable fixed points irrespective of stability. Relating the stability thresh

A continuous-time oscillator network implements high-capacity associative memory for factorizing compositional representations. This talk studies applications to perceptual inference and combinatorial problems such as subset sum, compares factorization performance with existing approaches, and analyses convergence to explain the network's capacity advantages and efficient implementation.

…of Hopfield networks as models of associative memory.

The factorization of a large matrix into the product of two matrices is an important mathematical problem encountered in many tasks, ranging from dictionary learning to machine learning. Statistical physics can provide on the one hand theoretical limits on the possibility of factorizing matrices in the limit of infinite size, and also practical algorithms. While this program has been successful in the case of finite rank matrices, the regime of extensive rank (scaling linearly with the dimension of the matrix) turns out to be much harder. This talk will describe a new approach to matrix facto

Neurons with recurrent connectivity can store memory patterns as attractor states in their dynamics, forming a plausible basis for associative memory in the brain. Classical theoretical results concerning fully connected recurrent neural networks (RNNs) with binary neurons and Hebbian learning rules find that they can store at most O(N) memories, where N is the number of neurons. However, under the physiological constraint that neurons are sparsely connected, this capacity is dramatically reduced to O(K), where K is the average number of presynaptic neurons (estimated to be O(10^3 ∼ 10^4) in m

…of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map e

…Previous associative memory reconsolidation studies show that incomplete reminders (IR) are a subtype of prediction error that strengthen memory trace. These reminders consist in an incomplete trial that ends abru

It has long been established that the hippocampus plays a crucial role for episodic memory. As opposed to the modular approach, now it is generally assumed that being a complex structure, the HC performs multiplex interconnected functions, whose hierarchical organization provides basis for the higher cognitive functions such as semantics-based encoding and retrieval. However, the «where, when and how» properties of distinct memory aspects within and outside the HC are still under debate. Here we used a visual associative memory task as a probe to test the hypothesis about the differential invo

…components—a recurrent network and an associative memory—on a canonical rule

Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and non-sparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitato

…using place cells as a proxy for associative memory for assessing deficits in Alzheim