CONTROL OF THE TRANSITION TO CHAOS IN NEURAL NETWORKS WITH RANDOM CONNECTIVITY

1993 ◽  
Vol 03 (02) ◽  
pp. 279-291 ◽  
Author(s):  
B. DOYON ◽  
B. CESSAC ◽  
M. QUOY ◽  
M. SAMUELIDES

The occurrence of chaos in recurrent neural networks is supposed to depend on the architecture and on the synaptic coupling strength. It is studied here for a randomly diluted architecture. We produce a bifurcation parameter independent of the connectivity that allows a sustained activity and the occurrence of chaos when reaching a critical value. Even for weak connectivity and small size, we find numerical results in accordance with the theoretical ones previously established for fully connected infinite sized networks. Moreover the route towards chaos is numerically checked to be a quasiperiodic one, whatever the type of the first bifurcation is. In the discussion, we connect these results to some recent theoretical results about highly diluted networks. Hints are provided for further investigations to elicit the role of chaotic dynamics in the cognitive processes of the brain.

Author(s):  
Ani Calinescu ◽  
Janet Efstathiou

Networked systems, natural or designed, have always been part of life. Their sophistication degree and complexity have increased through either natural evolution or technological progress. However, recent theoretical results have shown that a previously unexpected number of different classes of networks share similar network architectures and universal laws. Examples of such networks include metabolic pathways and ecosystems, the Internet and the World Wide Web, and organizational, social, and neural networks. Complex systems-related research questions investigated by researchers nowadays include: how consciousness arises out of the interactions of the neurons in the brain and between the brain and the environment (Amaral & Ottino, 2004; Barabási, 2005; Barabási & Oltvai, 2004; Neuman, 2003b) and how this understanding could be used for designing networked organizations or production networks whose behavior satisfies a given specification.


We know that the brain is the seat of the mind. Constructing the reductive model of the conscious mind requires an indication of the laws according to which the mind emerges from biophysical processes occurring in natural brains. Because in Part I, the authors presented the theoretical model referring to the ideal structures of the imagined neural network, we now have easier task, because we need to indicate in the brains of the living beings those processes that functionally correspond to our postulates. Such suitability is not guaranteed by known processes occurring in specialized parts of the brain. The role of the primary sensory areas is a detailed analysis of sensory stimuli with specific modality. They result in analysis of the meaning of all useful stimuli and their interpretation used in various parts of the cortex. The high specialization of individual cortex areas is striking and are the result of evolutionary development of the brain. New brain structures, such as the new cortex, were added on the outskirts of existing structures, improving their performance in the ever more demanding environments, where other intelligent beings ravened. But even as we know the brain organization, we struggle to understand how it works. How neurons that make the brain work together to create the conscious mind. To discover functionally effective processes in the brain, one need to reach for the biophysical properties of the astrocyt-neural network. In this chapter, the authors suggest that some concepts of neuro-electro-dynamics and the phenomena of neuro- and synapto-genesis as well as synaptic couplings may explain the processes of categorization, generalization and association leading to the formation of extensive, semihierarchical brain structures constituting neural representations of perceptions, objects and phenomena. Natural brains meet the embodiment condition. They are products of evolution, so they have intentionality, their own goals and needs. So they can naturally show emotions, drives and instincts that motivate to act. This determines the nature of constructed mental representations. They are the subject of psychological research, which shows the motivation of pain and pleasure in the field of intelligent activities, as well as the motivation of curiosity and the need for understanding in the domain of propositional and phenomenal consciousness. They describe the way pain is felt in organisms as basic quale. The role of other qualia for “how-it-is-like to feel something” and their subjective character was explained, as well as their interspecies specificity was characterized. In this chapter, the authors present an elementary biophysical phenomenon, that is a flash of consciousness. This phenomenon is synaptic coupling formed in the course of learning. They justify that the stream of such phenomena is the foundation of consciousness. They also point out that the astrocytic-neural network meets all the conditions required to generate conscious sensations.


2021 ◽  
Author(s):  
Xiangbin Teng ◽  
Ru-Yuan Zhang

Complex human behaviors involve perceiving continuous stimuli and planning actions at sequential time points, such as in perceiving/producing speech and music. To guide adaptive behavior, the brain needs to internally anticipate a sequence of prospective moments. How does the brain achieve this sequential temporal anticipation without relying on any external timing cues? To answer this question, we designed a premembering task: we tagged three temporal locations in white noise by asking human listeners to detect a tone presented at one of the temporal locations. We selectively probed the anticipating processes guided by memory in trials with only flat noise using novel modulation analyses. A multiscale anticipating scheme was revealed: the neural power modulation in the delta band encodes noise duration on a supra-second scale; the modulations in the alpha-beta band range mark the tagged temporal locations on a subsecond scale and correlate with tone detection performance. To unveil the functional role of those neural observations, we turned to recurrent neural networks (RNNs) optimized for the behavioral task. The RNN hidden dynamics resembled the neural modulations; further analyses and perturbations on RNNs suggest that the neural power modulations in the alpha/beta band emerged as a result of selectively suppressing irrelevant noise periods and increasing sensitivity to the anticipated temporal locations. Our neural, behavioral, and modelling findings convergingly demonstrate that the sequential temporal anticipation involves a process of dynamic gain control: to anticipate a few meaningful moments is also to actively ignore irrelevant events that happen most of the time.


2019 ◽  
Vol 3 (2) ◽  
pp. 27-33
Author(s):  
Maria Antonia Velez Tuarez ◽  
Ronald Ivan Zamora Delgado ◽  
Olga Viviana Torres Teran ◽  
Maria Elena Moya Martine

This article provided a brief analysis of an important human organ and the influences it has on personal and formal learning in the educational field. The specific topics that were investigated are the brain and its importance in learning, characteristics of the hemispheres of the brain learning and the contribution of neuroscience in the teaching-learning process. The first topic mentions how the brain influences learning and the role of memory in that process. The next topic focuses on the characteristics and functions performed by the two brain hemispheres. The latest content deals with the contribution of neuroscience in the educational field, here is detailed on how neural networks combined with the environment where the student performs to make learning possible. The descriptive methodology, based on the review of current bibliographic sources, was used. The purpose of this document is to provide the reader with true and up-to-date sources of information on an organ that integrates complex and necessary ideas for the human being.


2020 ◽  
Author(s):  
Hwayeon Ryu ◽  
Sue Ann Campbell

AbstractWe study a model for a network of synaptically coupled, excitable neurons to identify the role of coupling delays in generating different network behaviors. The network consists of two distinct populations, each of which contains one excitatory-inhibitory neuron pair. The two pairs are coupled via delayed synaptic coupling between the excitatory neurons, while each inhibitory neuron is connected only to the corresponding excitatory neuron in the same population. We show that multiple equilibria can exist depending on the strength of the excitatory coupling between the populations. We conduct linear stability analysis of the equilibria and derive necessary conditions for delay-induced Hopf bifurcation. We show that these can induce two qualitatively different phase-locked behaviors, with the type of behavior determined by the sizes of the coupling delays. Numerical bifurcation analysis and simulations supplement and confirm our analytical results. Our work shows that the resting equilibrium point is unaffected by the coupling, thus the network exhibits bistability between a rest state and an oscillatory state. This may help understand how rhythms spontaneously arise neuronal networks.


2019 ◽  
Author(s):  
Ryan Golden ◽  
Jean Erik Delanois ◽  
Pavel Sanda ◽  
Maxim Bazhenov

AbstractArtificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new learning is interleaved with periods of sleep for memory consolidation. In this study, we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on multiple tasks. New task training moved the synaptic weight configuration away from the manifold representing old tasks leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by pushing the synaptic weight configuration towards the intersection of the solution manifolds representing multiple tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.


Author(s):  
Ani Calinescu ◽  
Janet Efstathiou

Networked systems, natural or designed, have always been part of life. Their sophistication degree and complexity have increased through either natural evolution or technological progress. However, recent theoretical results have shown that a previously unexpected number of different classes of networks share similar network architectures and universal laws. Examples of such networks include metabolic pathways and ecosystems, the Internet and the World Wide Web, and organizational, social, and neural networks. Complex systems-related research questions investigated by researchers nowadays include: how consciousness arises out of the interactions of the neurons in the brain and between the brain and the environment (Amaral & Ottino, 2004; Barabási, 2005; Barabási & Oltvai, 2004; Neuman, 2003b) and how this understanding could be used for designing networked organizations or production networks whose behavior satisfies a given specification.


Author(s):  
Ivy Cheng ◽  
Shaheen Hamdy

Abstract Dysphagia is a common and devastating complication following brain damage. Over the last 2 decades, dysphagia treatments have shifted from compensatory to rehabilitative strategies that facilitate neuroplasticity, which is the reorganization of neural networks that is essential for functional recovery. Moreover, there is growing interest in the application of cortical and peripheral neurostimulation to promote such neuroplasticity. Despite some preliminary positive findings, the variability in responsiveness toward these treatments remains substantial. The purpose of this review is to summarize findings on the effects of neurostimulation in promoting neuroplasticity for dysphagia rehabilitation and highlight the need to develop more effective treatment strategies. We then discuss the role of metaplasticity, a homeostatic mechanism of the brain to regulate plasticity changes, in helping to drive neurorehabilitation. Finally, a hypothesis on how metaplasticity could be applied in dysphagia rehabilitation to enhance treatment outcomes is proposed.


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