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Stimulus-Dependent Assembly Formation of Oscillatory Responses: II. Desynchronization
Abstract
Recent theoretical and experimental work suggests a temporal structure of neuronal spike activity as a potential mechanism for solving the binding problem in the brain. In particular, recordings from cat visual cortex demonstrate the possibility that stimulus coherency is coded by synchronization of oscillatory neuronal responses. Coding by synchronized oscillatory activity has to avoid bulk synchronization within entire cortical areas. Recent experimental evidence indicates that incoherent stimuli can activate coherently oscillating assemblies of cells that are not synchronized among one another.
In this paper we show that appropriately designed excitatory delay connections can support the desynchronization of two-dimensional layers of delayed nonlinear oscillators. Closely following experimental observations, we then present two examples of stimulus-dependent assembly formation in oscillatory layers that employ both synchronizing and desynchronizing delay connections: First, we demonstrate the segregation of oscillatory responses to two overlapping but incoherently moving stimuli. Second, we show that the coherence of movement and location of two stimulus bar segments can be coded by the correlation of oscillatory activity.
... In these papers, information is transmitted along the network instantaneously, as in the classical model. However, networked Wilson-Cowan models have been developed with intra-node delays by König and Schillen (1991) and Schillen and König (1991) . In particular, in their work, information between spatial populations was instantaneous, with delays present between individual excitatory and inhibitory populations. ...
... On the other hand, when network effects are included, there have been a variety of studies for the form of the model without delays, resulting in ODE rather than DDE systems; see Wang (1995) , Campbell and Wang (1996) Malagarriga et al. (2015) . Networked Wilson-Cowan oscillators have previously been considered with intra-node delays but in the absence of inter-node delays by König and Schillen (1991) and Schillen and König (1991) . On the other hand, networked Wilson-Cowan oscillators with inter-node delays between individual excited populations (with all other delays neglected) are studied in Deco et al. (2009) . ...
We study the dynamics of a network Wilson–Cowan model (a system of connected Wilson–Cowan oscil- lators) for interacting excitatory and inhibitory neuron populations with time delays. We account for the time delay in information propagation be- tween individual excitatory and inhibitory subpopulations at each node via intra-node time delays, and we account for time delay in information propagation between neuron populations at different nodes with inter-node time delays. We find that network structure can regularize or deregularize the dynamics, with networks of higher mean degree permitting stable limit cycles and networks with smaller mean degree yielding less regular dynamics (which may range from chaotic so- lutions, to solutions for which limit cycles collapse into steady states, which are biologically undesirable compared with the preferred stable limit cycles). Furthermore, heterogeneity in the degree distribution of the network (resulting from networks with nodes of varying degree) can result in asynchronous dynam- ics, even if at each node the local dynamics are that of a limit cycle, in contrast to the synchronization of dynamics between nodes seen when the degree of all nodes is equal.
... As discharge rates of all neurons were elevated to the same extent, this segregation of neurons into two distinct groups would not have been detectable by decoding only rate responses. Simulations of recurrent networks with enhanced coupling of nodes tuned to groupable features have reproduced such context-dependent synchronization phenomena (73)(74)(75). The results of these and related studies eventually led to the proposal that transient and context-sensitive synchronization of discharges could serve as a mechanism for perceptual grouping and inspired the "binding by synchrony" (BBS) hypothesis (76). ...
Significance
This review attempts to unite three hitherto rather unconnected concepts of basic functions of the cerebral cortex, taking the visual system as an example: 1) feed-forward processing in multilayer hierarchies (labeled line coding), 2) dynamic association of features (assembly coding), and 3) matching of sensory evidence with stored priors (predictive coding). The latter two functions are supposed to rely on the high-dimensional dynamics of delay-coupled recurrent networks. Discharge rates of neurons (rate code) and temporal relations among discharges (temporal code) are identified as conveying complementary information. Thus, the new concept accounts for the coexistence of feed-forward and recurrent processing, accommodates both rate and temporal codes, and assigns crucial functions to the complex dynamics emerging from recurrent interactions.
... First models simulating the olfactory dynamics [4], [5], [34], [66] or visual cortex dynamics [31], [51], [32], [3], [29], [58], [57], [41] provide ideas, how the emergent properties of biological brains are related to complex dynamics, and how computational abilities may be generated by oscillatory networks. A collection of recent results can also be found in [52]. ...
After a short introduction to neural networks modelling basic concepts of neurodynamics are outlined. Discrete neurodynamics, more convenient for computer simulations, is then introduced. The value of parameter studies for comprehension of nonconvergent, i.e. oscillatory and chaotic, network behavior is discussed, and a modular approach to recurrent network dynamics is suggested. Results on attractor structures and bifurca-tion sets, obtained for dynamics of single units with self-connection, two neuron networks and ring networks, are presented.
This chapter is a continuation and an extension of the previous chapter. After the brief introduction of selective visual attention in the previous chapter, we present more details on the related knowledge, theory and experiments for visual attention. Section 2.1 describes the relevant basic properties and structures of the human visual system (HVS). In Sections 2.2 and 2.3, the widely used feature integration theory (FIT), as well as its extension (i.e. the guided search (GS) theory), is to be discussed, with the experimental confirmation available. FIT deals with bottom-up attention, while GS enables a combination of bottom-up and top-down attention. Section 2.4 further discusses the time binding theory for multi-feature integration at the neuronal level. Section 2.5 gives insight into some important issues in visual attention modelling, such as competition, normalization and frequency whitening. The final section covers statistical signal processing, which can be used for modelling visual attention alone or jointly with other principles in biology. The content in this chapter is the source of inspiration for many computational models of the human visual attention. It will help the reader not only to understand the existing computational attention models to be presented throughout this book, but also to build new systems because some crucial aspects of visual attention have not been incorporated in computational and engineering models yet, due to the difficulties in modelling, as well as application scenarios not being explored.
I constructed a cortical neural network model and investigated possible roles of coherent ongoing oscillations in membrane potentials of neurons in object perception. The model has a hierarchical structure consisting of two lower networks and one higher network that are reciprocally connected via divergent/convergent projections. Information about features and their relationships (or objects) is encoded by the population activities of neurons (or dynamic cell assemblies) of the lower networks and the higher network, respectively. The ongoing state of the network is expressed by ‘random itinerancy’ among these dynamic cell assemblies. Under the ongoing state, the dynamic cell assemblies belonging to the same object are transiently linked across the networks and coherently oscillate at lower frequencies (∼15 Hz). When the model perceives a presented object, the dynamic cell assemblies corresponding to the object are persistently linked together across the networks and coherently oscillate at higher frequencies (∼40 Hz). When the feedback pathways are impaired, the dynamic phase transition from the slow- to fast-oscillations is not induced by the object presentation, keeping the lower frequency oscillations (∼15 Hz) where the activated dynamic cell assemblies oscillate incoherently. Reaction times to the object presentation are greatly reduced if the ongoing oscillation frequencies fall within a specific range (∼20–30 Hz). I suggest that coherent ongoing slow-oscillations in cortical activity may serve as a ready state for sensory input, whereby the brain can respond effectively to sensory stimulation. Top-down processing via feedback pathways may give an essential contribution to the induction of coherent fast-oscillations across multiple cortical areas, by which relevant features are effectively integrated into a unified percept when stimulated with a sensory object.
The present study analyses the problem of binding and segmentation of a visual scene by means of a network of neural oscillators, laying emphasis on the problems of fragmentation, perception of details at different scales and spatial attention. The work is based on a two-layer model: a second layer of Wilson–Cowan oscillators is inhibited by information from the first layer. Moreover, the model uses a global inhibitor (GI) to segment objects. Spatial attention consists of an excitatory input, surrounded by an inhibitory annulus. A single object is identified by synchronous oscillatory activity of neural groups. The main idea of this work is that segmentation of objects at different detail levels can be achieved by linking parameters of the GI (i.e. the threshold and the inhibition strength) with the dimension of the zone selected by attention and with the dimension of the smaller objects to be detected. Simulations show that three possible kinds of behavior can be attained with the model, through proper choice of the GI parameters and attention input: (i) large objects in the visual scene are perceived, while small details are suppressed; (ii) large objects are perceived, while details are assembled together to constitute a single ‘noise term’; (iii) if attention is focused on a smaller area and the GI parameters modulated accordingly (i.e. the threshold and attention strength are reduced) details are individually perceived as separate objects. These results suggest that the GI and attention may represent two concurrent aspects of the same attentive mechanism, i.e. they should work together to provide flexible management of a visual scene at different levels of detail.
Current concepts in neurobiology of vision assume that local object features are represented by distributed neuronal populations in the brain. Such representations can lead to ambiguities if several distinct objects are simultaneously present in the visual field. Temporal characteristics of the neuronal activity have been proposed as a possible solution to this problem and have been found in various cortical areas.
In this paper we introduce a delayed nonlinear oscillator to investigate temporal coding in neuronal networks. We show synchronization within two-dimensional layers consisting of oscillatory elements coupled by excitatory delay connections. The observed correlation length is large compared to coupling length. Following the experimental situation, we then demonstrate the response of such layers to two short stimulus bars of varying gap distance. Coherency of stimuli is reflected by the temporal correlation of the responses, which closely resembles the experimental observations.
An oscillator neural network model is presented that is capable of processing local and global attributes of sensory input. Local features in the input are encoded in the average firing rate of the neurons while the relationships between these features can modulate the temporal structure of the neuronal output. Neurons that share the same receptive field interact via relatively strong feedback connections, while neurons with different fields interact via specific, relatively weak connections. This pattern of connectivity mimics that of primary visual cortex. The model is studied in the context of processing visual stimuli that are coded for orientation. We compare our theoretical results with recent experimental evidence on coherent oscillatory activity in the cat visual cortex. The computational capabilities of the model for performing discrimination and segmentation tasks are demonstrated.
A fundamental step in visual pattern recognition is the establishment of relations between spatially separate features. Recently, we have shown that neurons in the cat visual cortex have oscillatory responses in the range 40-60 Hz (refs 1, 2) which occur in synchrony for cells in a functional column and are tightly correlated with a local oscillatory field potential. This led us to hypothesize that the synchronization of oscillatory responses of spatially distributed, feature selective cells might be a way to establish relations between features in different parts of the visual field. In support of this hypothesis, we demonstrate here that neurons in spatially separate columns can synchronize their oscillatory responses. The synchronization has, on average, no phase difference, depends on the spatial separation and the orientation preference of the cells and is influenced by global stimulus properties.
Primary visual coding can be characterized by the receptive field (RF) properties of single neurons. Subject of this paper is our search for a global, second coding step beyond the RF-concept that links related features in a visual scene. In recent models of visual coding, oscillatory activities have been proposed to constitute such linking signals. We tested the neurophysiological relevance of this hypothesis for the visual system. Single and multiple spikes as well as local field potentials were recorded simultaneously from several locations in the primary visual cortex (A17 and A18) using 7 or 19 individually advanceable fiber-microelectrodes (250 or 330 microns apart). Stimulus-evoked (SE)-resonances of 35-85 Hz were found in these three types of signals throughout the visual cortex when the primary coding channels were activated by their specific stimuli. Stimulus position, orientation, movement direction and velocity, ocularity and stationary flicker caused specific SE-resonances. Coherent SE-resonances were found at distant cortical positions when at least one of the primary coding properties was similar. Coherence was found 1) within a vertical cortex column, 2) between neighbouring hypercolumns, and 3) between two different cortical areas. We assume that the coherence of SE-resonances is mediated by recurrent excitatory intra- and inter-areal connections via phase locking between assemblies that represent the linking features of the actual visual scene. Visually related activities are, thus, transiently labelled by a temporal code that signalizes their momentary association.
Sensory segmentation is an outstanding unsolved problem of theoretical, practical and technical importance. The basic idea of a solution is described in the form of a model. The response of "neurons" within the sensory field is temporally unstable. Segmentation is expressed by synchronization within segments and desynchronization between segments. Correlations are generated by an autonomous pattern formation process. Neuronal coupling is the result both of peripheral evidence (similarity of local quality) and of central evidence (common membership in a stored pattern). The model is consistent with known anatomy and physiology. However, a new physiological function, synaptic modulation, has to be postulated. The present paper restricts explicit treatment to the peripheral evidence represented by amplitude modulations globally present in all components of a sound spectrum. Generalization to arbitrary sensory qualities will be the subject of a later paper. The model is an application and illustration of the Correlation Theory of brain function.
The experience of reality, in both perception and recall, is spatially and temporally coherent and in-register. Features are bound in entities, and entities are bound in events. The properties of these entities and events, however, are represented in many different regions of the brain that are widely separated. The degree of neural parcellation is even greater when we consider that the perception of most entities and events also requires a motor interaction on the part of the perceiver (such as eye movements and hand movements) and often includes a recordable modification of the perceiver's somatic state. The question of how the brain achieves integration starting with the bits and pieces it has to work with, is the binding problem. Here we propose a new solution for this problem, at the level of neural systems that integrate functional regions of the telencephalon.
In areas 17 and 18 of the cat visual cortex the firing probability of neurons, in response to the presentation of optimally aligned light bars within their receptive field, oscillates with a peak frequency near 40 Hz. The neuronal firing pattern is tightly correlated with the phase and amplitude of an oscillatory local field potential recorded through the same electrode. The amplitude of the local field-potential oscillations are maximal in response to stimuli that match the orientation and direction preference of the local cluster of neurons. Single and multiunit recordings from the dorsal lateral geniculate nucleus of the thalamus showed no evidence of oscillations of the neuronal firing probability in the range of 20-70 Hz. The results demonstrate that local neuronal populations in the visual cortex engage in stimulus-specific synchronous oscillations resulting from an intracortical mechanism. The oscillatory responses may provide a general mechanism by which activity patterns in spatially separate regions of the cortex are temporally coordinated.
We have demonstrated previously that neurons in cat striate cortex, in response to their preferred stimuli, exhibit oscillatory responses in a frequency range of 40 - 60 Hz. Recently, we obtained evidence that such oscillatory responses can synchronize across columns. We have now performed an extensive analysis of this phenomenon for both unit and field potential responses. In addition, we studied the stimulus conditions leading to intercolumnar synchronization. We recorded both multi-unit activity and local field potentials from area 17 of adult cats with arrays of several electrodes. Interelectrode distances ranged from 0.4 to 12 mm. For all pairs of unit (n=200) and field potential (n=174) recordings, we computed auto- and cross-correlation functions. The modulation of the correlograms was quantified by fitting a damped sine wave (Gabor) function to the data. Cross-correlation analysis of the unit data revealed that in 90 out of 200 cases the recorded cells established a constant phase-relationship of their oscillatory responses. This occurred, on average, with no phase difference. If the receptive fields were nonoverlapping, we observed a synchronization primarily between cells with similar orientation preferences. Cells with overlapping receptive fields also showed a high incidence of synchronization if their orientation preferences were different. In this latter group, synchronization occurred even in cases where the stimulus was optimal for only one of the recording sites. Under conditions of monocular instead of binocular stimulation the oscillatory modulation of the responses was attenuated, but the cross-correlogram still indicated a significant interaction. Similar effects were seen with the application of stationary instead of moving stimuli. A synchronization of oscillatory field potential responses was observed in 136 out of 174 paired recordings. At all distances investigated, the probability of synchronization of field potential responses was independent of the orientation preferences of the cells. However, the strength of interaction decreased with increasing spatial separation. Control experiments showed that the synchronization of field potential responses was not due to volume conduction. The results demonstrate that oscillatory responses at separate cortical sites can transiently synchronize. The probability and strength of synchronization are dependent on the spatial separation of the recorded cells and their orientation preferences. In addition, the cross-columnar synchronization is influenced by features of the visual stimulus. It is suggested that this synchronization provides a mechanism for the formation of neuronal assemblies in the visual cortex.
The morphology and postsynaptic targets of GABA-containing boutons were determined in the striate cortex of cat, using a postembedding immunocytochemical technique at the electron microscopic level. Two types of terminals, both making symmetrical synaptic contacts, were GABA-positive. The first type (95% of all GABA-positive boutons) contained small pleomorphic vesicles, the second type (5%) contained larger ovoid vesicles. Furthermore, 99% of all cortical boutons containing pleomorphic vesicles were GABA positive, and all boutons with pleomorphic vesicles made symmetrical synaptic contacts. These results together with previously published stereological data (Beaulieu and Colonnier, 1985, 1987) were used to estimate the density of GABA-containing synapses, which is about 48 million/mm3 in the striate cortex. The postsynaptic targets of GABA positive boutons were also identified and the distribution was calculated to be as follows: 58% dendritic shafts, 26.4% dendritic spines, 13.1% somata and 2.5% axon initial segments. A total of 11% of the postsynaptic targets were GABA immunoreactive and therefore originated from GABAergic neurons. The results demonstrate that the majority of GABAergic synapses exert their action on the membrane of dendrites and spines rather than on the somata and axons of neurons.