Abstract
Based on the behavior of living beings, which react mostly to external stimuli, we introduce a neural-network model that uses external patterns as a fundamental tool for the process of recognition. In this proposal, external stimuli appear as an additional field, and basins of attraction, representing memories, arise in accordance with this new field. This is in contrast to the more-common attractor neural networks, where memories are attractors inside well-defined basins of attraction. We show that this procedure considerably increases the storage capabilities of the neural network; this property is illustrated by the standard Hopfield model, which reveals that the recognition capacity of our model may be enlarged, typically, by a factor . The primary challenge here consists in calibrating the influence of the external stimulus, in order to attenuate the noise generated by memories that are not correlated with the external pattern. The system is analyzed primarily through numerical simulations. However, since there is the possibility of performing analytical calculations for the Hopfield model, the agreement between these two approaches can be tested—matching results are indicated in some cases. We also show that the present proposal exhibits a crucial attribute of living beings, which concerns their ability to react promptly to changes in the external environment. Additionally, we illustrate that this new approach may significantly enlarge the recognition capacity of neural networks in various situations; with correlated and non-correlated memories, as well as diluted, symmetric, or asymmetric interactions (synapses). This demonstrates that it can be implemented easily on a wide diversity of models.
Highlights
The area of neural networks (NNs) has experienced impressive developments in the last few decades [1,2,3,4], essential characteristics and reactions of the brain are still far from being satisfactorily replicated in these models
In these types of attractor neural networks (ANNs) models, the influence of an external stimulus on a specific neuron belonging to the NN can be essentially split into two contributions; namely, a signal, connected with the external stimulus and correlated to a stored pattern, as well as noise, produced by all stored memories that are not related to the external stimulus
The results presented above show, clearly, that the stimulus-dependent neural network (SDNN) modifies the recognized pattern, in a smooth way, according to changes in the external pattern, which is a common characteristic of living beings
Summary
The area of neural networks (NNs) has experienced impressive developments in the last few decades [1,2,3,4], essential characteristics and reactions of the brain are still far from being satisfactorily replicated in these models. Living beings had to react quickly and more efficiently to changes in the environment—this task was at the origin of the brain and is recorded “au fer et au feu” in the way that it works This stimuli-dependence of the brain is behind all of its assignments, the old ones (essentially instinctive reactions), as well as the biologically newer ones (e.g., those related to cognition). Realistic pattern-recognition NN models should reproduce the actions sketched above (at least, most of them), by following the route of new concepts in complex systems [12,13,14,15] These systems essentially live at the chaos–order or ordered–disordered borderlines, and effective NN models should be defined at these borderlines; chaotic, ordered, or disordered regimes can never yield appropriate proposals for NNs. These systems essentially live at the chaos–order or ordered–disordered borderlines, and effective NN models should be defined at these borderlines; chaotic, ordered, or disordered regimes can never yield appropriate proposals for NNs Along these lines, we will introduce a stimuli-dependent neural-network model, and its effectiveness in pattern recognition will be demonstrated; certainly, similar scenarios can be constructed for other brain process.
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