An STDP-based encoding method for associative and composite data

STDP-based associative memory formation and retrieval

Spike-timing-dependent plasticity (STDP) is a biological process in which the precise order and timing of neuronal spikes affect the degree of synaptic modification. While there has been numerous research focusing on the role of STDP in neural coding, the functional implications of STDP at the macroscopic level in the brain have not been fully explored yet. In this work, we propose a neurodynamical model based on STDP that renders storage and retrieval of a group of associative memories. We showed that the function of STDP at the macroscopic level is to form a "memory plane" in the neural state space which dynamically encodes high dimensional data. We derived the analytic relation between the input, the memory plane, and the induced macroscopic neural oscillations around the memory plane. Such plane produces a limit cycle in reaction to a similar memory cue, which can be used for retrieval of the original input.

Plasticity in networks of active chemical cells with pulse coupling

A method for controlling the coupling strength is proposed for pulsed coupled active chemical micro-cells. The method is consistent with Hebb's rules. The effect of various system parameters on this "spike-timing-dependent plasticity" is studied. In addition to networks of two and three coupled active cells, the effect of this "plasticity" on the dynamic modes of a network of four pulse-coupled chemical micro-cells unidirectionally coupled in a circle is studied. It is shown that the proposed adjustment of the coupling strengths leads to spontaneous switching between network eigenmodes.

Weighted entropic associative memory and phonetic learning

The Entropic Associative Memory (EAM) holds declarative but distributed representations of remembered objects. These are characterized as functions from features to discrete values in an abstract amodal space. Memory objects are registered or remembered through a declarative operation; memory recognition is defined as a logical test and cues of objects not contained in the memory are rejected directly without search; and memory retrieval is a constructive operation. In its original formulation, the content of basic memory units or cells was either on or off, hence all stored objects had the same weight or strength. In the present weighted version (W-EAM) we introduce a basic learning mechanism to the effect that the values of the cells used in the representation of an object are reinforced by the memory register operation. As memory cells are shared by different representations, the corresponding associations are reinforced too. The memory system supports a second form of learning: the distributed representation generalizes and renders a large set of potential or latent units that can used for recognizing novel inputs, which can in turn be used for improving the performance of both the deep neural networks used for modelling perception and action, and of the memory operations. This process can be performed recurrently in open-ended fashion and can be used in long term learning. An experiment in the phonetic domain using the Mexican Spanish DIMEx100 Corpus was carried out. This corpus was collected in a controlled noise-free environment, and was transcribed manually by human trained phoneticians, but consists of a relatively small number of utterances. DIMEx100 was used to produced the initial state of the perceptual and motor modules, and for testing the performance of the memory system at such state. Then the incremental learning cycle was modelled using the Spanish CIEMPIESS Corpus, consisting of a very large number of noisy untagged speech utterances collected from radio and TV. The results support the viability of the Weighted Entropic Associative Memory for modelling cognitive processes, such as phonetic representation and learning, for the construction of applications, such as speech recognition and synthesis, and as a computational model of natural memory.