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Cortical Synchronization and Perceptual Framing
, 1996
"... How does the brain group together different parts of an object into a coherent visual object representation? Different parts of an object may be processed by the brain at different rates and may thus become desynchronized. Perceptual framing is a process that resynchronizes cortical activities corre ..."
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Cited by 35 (20 self)
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How does the brain group together different parts of an object into a coherent visual object representation? Different parts of an object may be processed by the brain at different rates and may thus become desynchronized. Perceptual framing is a process that resynchronizes cortical activities corresponding to the same retinal object. A neural network model is presented that is able to rapidly resynchronize desynchronized neural activities. The model provides a link between perceptual and brain data. Model properties quantitatively simulate perceptual framing data, including psychophysical data about temporal order judgments and the reduction of threshold contrast as a function of stimulus length. Such a model has earlier been used to explain data about illusory contour formation, texture segregation, shapefromshading, 3D vision, and cortical receptive fields. The model hereby shows how many data may be understood as manifestations of a cortical grouping process that can rapidly res...
A neurobiological theory of meaning in perception. Part 1. Information and meaning in nonconvergent and nonlocal brain dynamics
 Int. J. Bifurc. Chaos
, 2003
"... Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integrati ..."
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Cited by 28 (14 self)
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Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integration. However, propagation delays are obligatory. Delays vary with the lengths and conduction velocities of the axons carrying the information, causing phase dispersion. In order to determine how synchrony is achieved despite dispersion, we recorded EEG signals from multiple electrode arrays on five cortical areas in cats and rabbits, that had been trained to discriminate visual or auditory conditioned stimuli. Analysis by timelagged correlation, multiple correlation and PCA, showed that maximal correlation was at zero lag and averaged.7, indicating that 50 % of the power in the gamma range among the five areas was at zero lag irrespective of phase or frequency. There were no stimulusrelated episodes of transiently increased phase locking among the areas, nor EEG "bursts " of transiently increased amplitude above the sustained level of synchrony. Three operations were identified to account for the sustained correlation. Cortices broadcast their outputs over divergentconvergent axonal
The Wave Packet: An Action Potential For The 21st Century
, 2003
"... prediction is made for clinical testing that wave packets are precursor to states of awareness. They are not by themselves accessible to experience, as may be the macroscopic states initiated by global state transitions. Keywords: EEG; meaning; mesoscopic neurodynamics; phase cone; state transiti ..."
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Cited by 11 (0 self)
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prediction is made for clinical testing that wave packets are precursor to states of awareness. They are not by themselves accessible to experience, as may be the macroscopic states initiated by global state transitions. Keywords: EEG; meaning; mesoscopic neurodynamics; phase cone; state transition; wave packet. 1. Introduction Brain systems operate on many levels of organization, each with its own scales of time and space. Dynamics applies to every level from the atomic to the molecular, and from macromolecular organelles to the neurons that incorporate them. In turn neurons form populations, these form the subassemblies in brains, and so on to embodied brains interacting intentionally with material, interpersonal, and social environments. Each level is macroscopic to that below it and microscopic to that above it. Among the most di#cult tasks scientists face are those of conceiving and describing the exchanges between levels, seeing that the measures of time 3 and distance ar
A Normal Form Projection Algorithm for Associative Memory
 Associative Neural Memories: Theory and Implementation
, 1993
"... this paper is contained in the projection theorem, which details the associative memory capabilities of networks utilizing the normal form projection algorithm for storage of periodic attractors. The algorithm was originally designed, using dynamical systems theory, to allow learning and pattern re ..."
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Cited by 6 (1 self)
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this paper is contained in the projection theorem, which details the associative memory capabilities of networks utilizing the normal form projection algorithm for storage of periodic attractors. The algorithm was originally designed, using dynamical systems theory, to allow learning and pattern recognition with oscillatory attractors in models of olfactory cortex. Here we concentrate on mathematical analysis and engineering oriented applications of the algorithm, and briefly discuss biological models at the end. We focus attention on the storage of periodic attractors, since that is the best understood unusual capability of this system. The storage of static and chaotic attractors are discussed as variations on this theme. We hope to give intuitive discussion and geometric perspectives to compliment and clarify the formal analysis. Other approaches to oscillatory memory may be found in [26, 17, 45, 33, 37]. The normal form projection algorithm provides one solution to the problem of storing analog attractors in a recurrent neural network. Associative memory storage of analog patterns and continuous periodic sequences in the same network is analytically guaranteed. For a network with N nodes, the capacity is N
Attentional Network Streams of Synchronized 40Hz Activity in a Cortical Architecture of Coupled Oscillatory Associative Memories
 In
, 1997
"... We have developed a neural network architecture that implements a theory of attention, learning, and transcortical communication based on adaptive synchronization of 520 Hz and 3080 Hz oscillations between cortical areas. It assigns functional significance to EEG, ERP, and neuroimaging data. Usin ..."
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Cited by 5 (1 self)
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We have developed a neural network architecture that implements a theory of attention, learning, and transcortical communication based on adaptive synchronization of 520 Hz and 3080 Hz oscillations between cortical areas. It assigns functional significance to EEG, ERP, and neuroimaging data. Using dynamical systems theory, the architecture is constructed from recurrently interconnected oscillatory associative memory modules that model higher order sensory and motor areas of cortex. The modules learn connection weights between themselves which cause the system to evolve under a 520 Hz clocked sensory/motor processing cycle by a sequence of transitions of synchronized 3080 Hz oscillatory attractors within the modules. The architecture employs selective"attentional" control of the synchronization of the 3080 Hz oscillations between modules to direct the flow of communication and computation to recognize and generate sequences. The 3080 Hz attractor amplitude patterns code the infor...
Spatial Eigenmodes and Synchronous Oscillation: CoIncidence Detection in Simulated Cerebral Cortex
"... Zerolag synchrony arises between two points on the cerebral cortex when these receive concurrent independent inputs and has generally been ascribed to nonlinear mechanisms. We report results obtained by Principal Component Analysis (PCA) applied to simulations of cerebral cortex which exhibit zero ..."
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Cited by 5 (2 self)
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Zerolag synchrony arises between two points on the cerebral cortex when these receive concurrent independent inputs and has generally been ascribed to nonlinear mechanisms. We report results obtained by Principal Component Analysis (PCA) applied to simulations of cerebral cortex which exhibit zerolag synchrony and realistic spectral content, and show that synchrony can arise by distinct and separable linear and nonlinear mechanisms. For lower levels of cortical activation synchrony between the sites of input can be accounted for by the eigenmodes associated with the wave activity generated by the inputs. The first spatial eigenmode arises from even 2 Clare L. Chapman et al. components in the independent input signals and the second spatial eigenmode arises from odd components in the inputs. Together these account for most of the signal variance, while the predominance of the first mode over the second within the nearfield of the inputs accounts for zerolag synchrony in the ne...
Computing with Dynamic Attractors in Neural Networks
, 1998
"... ing from the details of the design, construction, operation and training method, we view a network as a dynamical system, to be described mathematically by difference equations or differential equations; we concentrate on differential equations. The dynamics are determined by the network design, inp ..."
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Cited by 4 (0 self)
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ing from the details of the design, construction, operation and training method, we view a network as a dynamical system, to be described mathematically by difference equations or differential equations; we concentrate on differential equations. The dynamics are determined by the network design, input patterns (data vectors), and training algorithm. For an N unit network the state space is the space of all activation vectors, identified with Euclidean space R N . After some scheme of training has been completed, an input vector presented for classification determines an initial activation state (perhaps after preprocessing, according to the design of the network). Under the dynamics of the network this state evolves and in the long run settles down on an attractor. This attractor may be an equilibrium (stationary) state, which we call a static attractor, or a dynamic attractor, such as a limit cycle or fractal. In the simplest cases, and in the overwhelming majority of networks studi...
A Hierarchical SensoryMotor Architecture of Oscillating Cortical Area Subnetworks
 Analysis and Modeling of Neural Systems II
, 1993
"... We show how hierarchical networks may be constructed of interconnected oscillatory network modules developed previously as models of olfactory cortex, or caricatures of "patches"of neocortex. The architecture is such that the larger system is itself a special case of the type of network of the submo ..."
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Cited by 3 (3 self)
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We show how hierarchical networks may be constructed of interconnected oscillatory network modules developed previously as models of olfactory cortex, or caricatures of "patches"of neocortex. The architecture is such that the larger system is itself a special case of the type of network of the submodules, and can be analysed with the same tools used to design the subnetwork modules. A particular subnetwork is formed by a set of neural populations whose interconnections also contain higher order synapses. These synapses determine attractors for that subnetwork independent of other subnetworks. Each subnetwork module assumes only minimal coupling justified by known anatomy. An N node module can be shown to function as an associative memory for up to N=2 oscillatory and N=3 chaotic memory attractors. The modules can learn connection weights between themselves which will cause the system to evolve under a clocked "machine cycle" by a sequence of transitions of attractors within the modules...
Spatially Extended Chaos and the Perception of Form  Computer and biological modeling of shape perception, emergent attention foci, and reversible depth percepts
, 1995
"... depiction of phase regime path for Freeman model; new perceptions cause parameter changes resulting in new chaotic attractor and new limit cycles. 2.7.2. Binding simple patterns with intermittency dynamics I. Tsuda and colleagues [1992] developed a model which also shows nonequilibrium behavior dur ..."
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depiction of phase regime path for Freeman model; new perceptions cause parameter changes resulting in new chaotic attractor and new limit cycles. 2.7.2. Binding simple patterns with intermittency dynamics I. Tsuda and colleagues [1992] developed a model which also shows nonequilibrium behavior during the recognition or memory recall dynamics, and they review many possible cognitive functions for chaotic dynamics. They establish several attractors through a Hebbian learning stage, then apply the dynamics outlined below. In this situation, presenting the network with one learned attractor causes it to cycle through the previous attractors in a pseudorandom order, with spurious transitional states between visits. (This mode of behavior, also described by Kaneko, is called chaotic itinerancy.) They conjecture that this dynamical behavior serves to semantically link previously memorized attracters into more complex combinations. The network formulation uses two node types which are here ...