The Time-Budget Perspective of the Role of Time Dimension in Modular Network Dynamics During Functions of the Brain PDF Download
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Author: Daya S. Gupta
Publisher:
ISBN:
Category : Science
Languages : en
Pages :
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Book Description
Information processing plays a key role in the daily activities of human and nonhuman primates. Information processing in the brain, underlying behavior, is constrained by the four-dimensional nature of external physical surroundings. In contrast to three geometric dimensions, there are no known peripheral sensory organs for the perception of time dimension. However, the representation of time dimension in modular neural networks is critical for the brain functions that require interval timing or the temporal coupling of action with perception. Recent experimental and theoretical studies are shedding light on how the representation of time dimension in neural circuits plays a key role in the diverse functions of the brain, which also includes motor interactions with environment as well as social interactions, such as verbal and nonverbal communication. Although different lines of evidence strongly suggest that rhythmic neural activities represent time dimension in the brain, how the information represented by rhythmic activities is processed to time behavioral responses by the brain remains unclear. Theoretical considerations suggest that the rhythmic activities represent a physical aspect of the time dimension rather than the source of simple additive temporal units for coding time intervals in neural circuits.
Author: Daya S. Gupta
Publisher:
ISBN:
Category : Science
Languages : en
Pages :
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Book Description
Information processing plays a key role in the daily activities of human and nonhuman primates. Information processing in the brain, underlying behavior, is constrained by the four-dimensional nature of external physical surroundings. In contrast to three geometric dimensions, there are no known peripheral sensory organs for the perception of time dimension. However, the representation of time dimension in modular neural networks is critical for the brain functions that require interval timing or the temporal coupling of action with perception. Recent experimental and theoretical studies are shedding light on how the representation of time dimension in neural circuits plays a key role in the diverse functions of the brain, which also includes motor interactions with environment as well as social interactions, such as verbal and nonverbal communication. Although different lines of evidence strongly suggest that rhythmic neural activities represent time dimension in the brain, how the information represented by rhythmic activities is processed to time behavioral responses by the brain remains unclear. Theoretical considerations suggest that the rhythmic activities represent a physical aspect of the time dimension rather than the source of simple additive temporal units for coding time intervals in neural circuits.
Author: Mark Burke
Publisher: BoD – Books on Demand
ISBN: 1789232163
Category : Science
Languages : en
Pages : 190
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Book Description
Nonhuman primates (referred to here as primates) provide an invaluable source of information for a multitude of scientific fields including ecology, evolution, biology, psychology, and biomedicine. This volume addresses various topics related to primate research that includes phylogeny, natural observations, primate ecosystem, sociocognitive abilities, disease pathophysiology, and neuroscience. Topics discussed here provide a platform for which to address human evolution, habitat preservation, human psyche, and pathophysiology of disease.
Author: Daya Shankar Gupta
Publisher: Frontiers Media SA
ISBN: 2889451496
Category : Brain
Languages : en
Pages : 139
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Book Description
Optimized interaction of the brain with environment requires the four-dimensional representation of space-time in the neuronal circuits. Information processing is an important part of this interaction, which is critically dependent on time-dimension. Information processing has played an important role in the evolution of mammals, and has reached a level of critical importance in the lives of primates, particularly the humans. The entanglement of time-dimension with information processing in the brain is not clearly understood at present. Time-dimension in physical world – the environment of an organism – can be represented by the interval of a pendulum swing (the cover page depicts temporal unit with the help of a swinging pendulum). Temporal units in neural processes are represented by regular activities of pacemaker neurons, tonic regular activities of proprioceptors and periodic fluctuations in the excitability of neurons underlying brain oscillations. Moreover, temporal units may be representationally associated with time-bins containing bits of information (see the Editorial), which may be studied to understand the entanglement of time-dimension with neural information processing. The optimized interaction of the brain with environment requires the calibration of neural temporal units. Neural temporal units are calibrated as a result of feedback processes occurring during the interaction of an organism with environment. Understanding the role of time-dimension in the brain information processing requires a multidisciplinary approach, which would include psychophysics, single cell studies and brain recordings. Although this Special Issue has helped us move forward on some fronts, including theoretical understanding of calibration of time-information in neural circuits, and the role of brain oscillations in timing functions and integration of asynchronous sensory information, further advancements are needed by developing correct computational tools to resolve the relationship between dynamic, hierarchical neural oscillatory structures that form during the brain’s interaction with environment.
Author: MohammadMehdi Kafashan
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 215
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Book Description
The brain is inherently a dynamical system whose networks interact at multiple spatial and temporal scales. Understanding the functional role of these dynamic interactions is a fundamental question in neuroscience. In this research, we approach this question through the development of new methods for characterizing brain dynamics from real data and new theories for linking dynamics to function. We perform our study at two scales: macro (at the level of brain regions) and micro (at the level of individual neurons). In the first part of this dissertation, we develop methods to identify the underlying dynamics at macro-scale that govern brain networks during states of health and disease in humans. First, we establish an optimization framework to actively probe connections in brain networks when the underlying network dynamics are changing over time. Then, we extend this framework to develop a data-driven approach for analyzing neurophysiological recordings without active stimulation, to describe the spatiotemporal structure of neural activity at different timescales. The overall goal is to detect how the dynamics of brain networks may change within and between particular cognitive states. We present the efficacy of this approach in characterizing spatiotemporal motifs of correlated neural activity during the transition from wakefulness to general anesthesia in functional magnetic resonance imaging (fMRI) data. Moreover, we demonstrate how such an approach can be utilized to construct an automatic classifier for detecting different levels of coma in electroencephalogram (EEG) data. In the second part, we study how ongoing function can constraint dynamics at micro-scale in recurrent neural networks, with particular application to sensory systems. Specifically, we develop theoretical conditions in a linear recurrent network in the presence of both disturbance and noise for exact and stable recovery of dynamic sparse stimuli applied to the network. We show how network dynamics can affect the decoding performance in such systems. Moreover, we formulate the problem of efficient encoding of an afferent input and its history in a nonlinear recurrent network. We show that a linear neural network architecture with a thresholding activation function is emergent if we assume that neurons optimize their activity based on a particular cost function. Such an architecture can enable the production of lightweight, history-sensitive encoding schemes.
Author: Monica Frega
Publisher: Springer
ISBN: 331930237X
Category : Technology & Engineering
Languages : en
Pages : 151
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Book Description
The book presents a new, powerful model of neuronal networks, consisting of a three-dimensional neuronal culture in which 3D neuronal networks are coupled to micro-electrode-arrays (MEAs). It discusses the main advantages of the three-dimensional system compared to its two-dimensional counterpart, and shows that the network dynamics, recorded during both spontaneous and stimulated activity, differs between the two models, with the 3D system being better able to emulate the in vivo behaviour of neural networks. The book offers an extensive analysis of the system, from the theoretical background, to its design and applications in neuro-pharmacological studies. Moreover, it includes a concise yet comprehensive introduction to both 2D and 3D neuronal networks coupled to MEAs, and discusses the advantages, limitations and challenges of their applications as cellular and tissue-like in vitro experimental model systems.
Author: Daya Shankar Gupta
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages :
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Book Description
There has been a considerable interest in the role of time-dimension in functions of the brain, which has been limited to time perception and timing of behavior. However, during past few years it has become increasingly clear that the role of the time-dimension includes other complex cognitive functions, such as motor control of a vehicle, sensory perception and processing imageries to name a few. Role of the accurate representation of time-dimension is important for several neural mechanisms, which include temporal coupling, coincidence detection, and processing of Shannon information. These mechanisms play key roles in processing information during the interaction of the brain with the physical surroundings.
Author: Nicholas Hardy
Publisher:
ISBN:
Category :
Languages : en
Pages : 153
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Book Description
Time is centrally involved in most tasks the brain performs. However, the neurobiological mechanisms of timing remain a mystery. Signatures of temporal processing related to sensory and motor behavior have been observed in several brain regions and behavioral contexts. This activity is often complex, representing time in the activity of large populations of neurons. A major question is whether this observed activity is generated by a specialized clock in the brain or whether it is arises locally via the emergent dynamics of neural networks. State dependent theories of timing argue for the latter: neural activity evolving over time produces a trajectory of network states that can encode temporal information. In this work, I examine the role of network dynamics in encoding temporal information. Combining mathematical models, in vitro neural recordings, and human psychophysics, the studies presented here describe potential network level mechanisms for timing in the brain. Chapter 2 presents research examining sequential activity observed in brain regions characterized by recurrent connectivity. This study describes a theoretical mechanism that recurrent neural networks may use to autonomously produce sequential activity and encode temporal information. Next, Chapter 3 examines the mechanisms of producing the same complex movement at a variety of speeds, a fundamental feature of motor timing. This study combines theoretical and psychophysical studies to predict and test a novel feature of motor timing: temporal accuracy improves with speed, termed the Weber-speed effect. Finally, Chapter 4 examines how cortical neural networks encode temporal information. Using organotypic slice cultures, this study demonstrates that the cortex processes temporal input patterns in a state dependent manner, supporting theoretical predictions. Taken together, the results of this work strongly support state dependent theories of timing, providing insight into the neural basis temporal processing.
Author: N. Pradhan
Publisher: Nova Science Publishers
ISBN:
Category : Medical
Languages : en
Pages : 452
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Book Description
Nonlinear Dynamics & Brain Functioning
Author: Sensen Liu
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 153
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Book Description
The brain produces complex patterns of activity that occur at different spatio-temporal scales. One of the fundamental questions in neuroscience is to understand how exactly these dynamics are related to brain function, for example our ability to extract and process information from the sensory periphery. This dissertation presents two distinct lines of inquiry related to different aspects of this high-level question. In the first part of the dissertation, we study the dynamics of burst suppression, a phenomenon in which brain electrical activity exhibits bistable dynamics. Burst suppression is frequently encountered in individuals who are rendered unconscious through general anesthesia and is thus a brain state associated with profound reductions in awareness and, presumably, information processing. Our primary contribution in this part of the dissertation is a new type of dynamical systems model whose analysis provides insights into the mechanistic underpinnings of burst suppression. In particular, the model yields explanations for the emergence of the characteristic two time-scales within burst suppression, and its synchronization across wide regions of the brain.The second part of the dissertation takes a different, more abstract approach to the question of multiple time-scale brain dynamics. Here, we consider how such dynamics might contribute to the process of learning in brain and brain-like networks, so as to enable neural information processing and subsequent computation. In particular, we consider the problem of optimizing information-theoretic quantities in recurrent neural networks via synaptic plasticity. In a recurrent network, such a problem is challenging since the modification of any one synapse (connection) has nontrivial dependency on the entire state of the network. This form of global learning is computationally challenging and moreover, is not plausible from a biological standpoint. In our results, we overcome these issues by deriving a local learning rule, one that modifies synapses based only on the activity of neighboring neurons. To do this, we augment from first principles the dynamics of each neuron with several auxiliary variables, each evolving at a different time-scale. The purpose of these variables is to support the estimation of global information-based quantities from local neuronal activity. It turns out that the synthesized dynamics, while providing only an approximation of the true solution, nonetheless are highly efficacious in enabling learning of representations of afferent input. Later, we generalize this framework in two ways, first to allow for goal-directed reinforcement learning and then to allow for information-based neurogenesis, the creation of neurons within a network based on task needs. Finally, the proposed learning dynamics are demonstrated on a range of canonical tasks, as well as a new application domain: the exogenous control of neural activity.
Author: Omri Harish
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
One of the most fundamental brain capabilities, that is vital for any high level cognitive function, is to store task-relevant information for short periods of time; this capability is known as working memory (WM). In recent decades there is accumulating evidence of taskrelevant activity in the prefrontal cortex (PFC) of primates during delay periods of delayedresponse tasks, thus implying that PFC is able to maintain sensory information and so function as a WM module. For retrieval of sensory information from network activity after the sensory stimulus is no longer present it is imperative that the state of the network at the time of retrieval be correlated with its state at the time of stimulus offset. One extreme, prominent in computational models of WM, is the co-existence of multiple attractors. In this approach the network dynamics has a multitude of possible steady states, which correspond to different memory states, and a stimulus can force the network to shift to one such steady state. Alternatively, even in the absence of multiple attractors, if the dynamics of the network is chaotic then information about past events can be extracted from the state of the network, provided that the typical time scale of the autocorrelation (AC) of neuronal dynamics is large enough. In the first part of this thesis I study an attractor-based model of memory of a spatial location to investigate the role of non-linearities of neuronal f-I curves in WM mechanisms. I provide an analytic theory and simulation results showing that these nonlinearities, rather than synaptic or neuronal time constants, can be the basis of WM network mechanisms. In the second part I explore factors controlling the time scale of neuronal ACs in a large balanced network displaying chaotic dynamics. I develop a mean-field (MF) theory describing the ACs in terms of several order parameters. Then, I show that apart from the proximity to the transition-to-chaos point, which can increase the width of the AC curve, the existence of connectivity motifs can cause long-time correlations in the state of the network.
