A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons

We propose a model for the neuronal implementation of selective visual attention based on temporal correlation among groups of neurons. Neurons in primary visual cortex respond to visual stimuli with a Poisson distributed spike train with an appropriate, stimulus-dependent mean firing rate. The spike trains of neurons whose receptive fields donot overlap with the focus of attention are distributed according to homogeneous (time-independent) Poisson process with no correlation between action potentials of different neurons. In contrast, spike trains of neurons with receptive fields within the focus of attention are distributed according to non-homogeneous (time-dependent) Poisson processes. Since the short-term average spike rates of all neurons with receptive fields in the focus of attention covary, correlations between these spike trains are introduced which are detected by inhibitory interneurons in V4. These cells, modeled as modified integrate-and-fire neurons, function as coincidence detectors and suppress the response of V4 cells associated with non-attended visual stimuli. The model reproduces quantitatively experimental data obtained in cortical area V4 of monkey by Moran and Desimone (1985).     

A common, high-dimensional model of the representational space in human ventral temporal cortex

We present a high-dimensional model of the representational space in human ventral temporal (VT) cortex in which dimensions are response-tuning functions that are common across individuals and patterns of response are modeled as weighted sums of basis patterns associated with these response tunings. We map response-pattern vectors, measured with fMRI, from individual subjects' voxel spaces into this common model space using a new method, "hyperalignment." Hyperalignment parameters based on responses during one experiment--movie viewing--identified 35 common response-tuning functions that captured fine-grained distinctions among a wide range of stimuli in the movie and in two category perception experiments. Between-subject classification (BSC, multivariate pattern classification based on other subjects' data) of response-pattern vectors in common model space greatly exceeded BSC of anatomically aligned responses and matched within-subject classification. Results indicate that population codes for complex visual stimuli in VT cortex are based on response-tuning functions that are common across individuals.     

Simulation studies of a temporal sequence memory model

Models of circuit action in the mammalian hippocampus have led us to a study of habituation circuits. In order to help model the process of habituation we consider here a memory network designed to learn sequences of inputs separated by various time intervals and to repeat these sequences when cued by their initial portions. The structure of the memory is based on the anatomy of the dentate gyrus region of the mammalian hippocampus. The model consists of a number of arrays of cells called lamellae. Each array consists of four lines of model cells coupled uniformly to neighbors within the array and with some randomness to cells in other lamellae. All model cells operate according to first-order differential equations. Two of the lines of cells in each lamella are coupled such that sufficient excitation by a system input generates a wave of activity that travels down the lamella. Such waves effect dynamic storage of the representation of each input, allowing association connections to form that code both the set of cells stimulated by each input and the time interval between successive inputs. Results of simulation of two networks are presented illustrating the model's operating characteristics and memory capacity.     

A model of cortical memory processing based on columnar organization

Recent neurophysiological experiments using mammalian brains indicated that some cortical neurons exhibit oscillatory activities which can be of functional importance in visual perception. These findings suggest that the oscillation is an ubiquitous feature of cortical information processing carried out by columns which are receiving growing attention as functional subdivisions of cortical circuitry. On the assumption that a basic functional unit is a column comprising excitatory and inhibitory neurons, a network model of cortical memory processing which can account for these oscillations is proposed. Numerical simulations revealed that for appropriately determined parameters the network can attain memory-pattern retrieval resulting from fixed-point behaviour despite the fact that columns have the characteristic of oscillators. Received: 19 March 1993/Accepted in revised form: 23 September 1993     

A model for processing of movement in the visual system

Processing of spatio-temporal information in the human visual system has been investigated thoroughly during the past decade, but is still far from being properly understood. Moreover, the theory of separation of information by means of sustained and transient channels already at the retinal level is not satisfactory, as experimental results indicate that these two types of channels span a continuum of temporal characteristics. It is however obvious, that the process of pattern recognition and velocity perception calls for their separation at some level of the hierarchy. In this communication, we extend our model of three-dimensional spatio-temporal frequency expansion in the visual system (Gafni and Zeevi, 1977) to show how velocity-information extraction channels, sensitive to direction and velocity exclusively, can be formed by simple summation of signals from well-defined sets of channels representing points in the frequency space. Correspondence of these channels to characteristics of the cortical neurons is discussed.     

Plasticity and stability of the visual system in human achiasma

The absence of the optic chiasm is an extraordinary and extreme abnormality in the nervous system. The abnormality produces highly atypical functional responses in the cortex, including overlapping hemifield representations and bilateral population receptive fields in both striate and extrastriate visual cortex. Even in the presence of these large functional abnormalities, the effect on visual perception and daily life is not easily detected. Here, we demonstrate that in two achiasmic humans the gross topography of the geniculostriate and occipital callosal connections remains largely unaltered. We conclude that visual function is preserved by reorganization of intracortical connections instead of large-scale reorganizations of the visual cortex. Thus, developmental mechanisms of local wiring within cortical maps compensate for the improper gross wiring to preserve function in human achiasma.     

Global stability, local stability and permanence in model food webs

The dynamical theory of food webs has been based typically on local stability analysis. The relevance of local stability to food web properties has been questioned because local stability holds only in the immediate vicinity of the equilibrium and provides no information about the size of the basin of attraction. Local stability does not guarantee persistence of food webs in stochastic environments. Moreover, local stability excludes more complex dynamics such as periodic and chaotic behaviors, which may allow persistence. Global stability and permanence could be better criteria of community persistence. Our simulation analysis suggests that these three stability measures are qualitatively consistent in that all three predict decreasing stability with increasing complexity. Some new predictions on how stability depends on food web configurations are generated here: a consumer-victim link has a smaller effect on the probabilities of stability, as measured by all three stability criteria, than a pair of recipient-controlled and donor-controlled links; a recipient-controlled link has a larger effect on the probabilities of local stability and permanence than a donor-controlled link, while they have the same effect on the probability of global stability; food webs with equal proportions of donor-controlled and recipient-controlled links are less stable than those with different proportions.     

The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition

To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.     

Home range criteria based on temporal stability of areal occupation

Home range is often defined as the area normally occupied by an animal. Such definitions fail to identify temporal stability of areal occupation as the basis for determining presence of home range. Several ambiguities in the most widely quoted definition are noted and criteria are developed for specifying presence or absence of home range under various conditions of areal change. Application of the criteria requires considerable data, but such data are also required to increase the accuracy of home range measurement.     

Associative memory model with temporal spike coding and active dendrite

We propose a neuron model whose internal state is integrated on a two dimensional phase space composed of time and dendritic space. Here, the postsynaptic potential is given as a curved surface on the phase space. Using the proposed neuron model, we introduce a continuous-time associative memory model with spike propagation delay and multiple synaptic sites on the dendrite. We show by numerical simulation that the memory capacity is doubled due to the effects of temporal spike coding and active dendrite compared to the sparse coding associative memory model.     

A model for the estimate of local image velocity by cells in the visual cortex

Some computational theories of motion perception assume that the first stage en route to this perception is the local estimate of image velocity. However, this assumption is not supported by data from the primary visual cortex. Its motion sensitive cells are not selective to velocity, but rather are directionally selective and tuned to spatio-temporal frequencies. Accordingly, physiologically based theories start with filters selective to oriented spatio-temporal frequencies. This paper shows that computational and physiological theories do not necessarily conflict, because such filters may, as a population, compute velocity locally. To prove this point, we show how to combine the outputs of a class of frequency tuned filters to detect local image velocity. Furthermore, we show that the combination of filters may simulate 'Pattern' cells in the middle temporal area (MT), whereas each filter simulates primary visual cortex cells. These simulations include three properties of the primary cortex. First, the spatio-temporal frequency tuning curves of the individual filters display approximate space-time separability. Secondly, their direction-of-motion tuning curves depend on the distribution of orientations of the components of the Fourier decomposition and speed of the stimulus. Thirdly, the filters show facilitation and suppression for responses to apparent motions in the preferred and null directions, respectively. It is suggested that the MT's role is not to solve the aperture problem, but to estimate velocities from primary cortex information. The spatial integration that accounts for motion coherence may be postponed to a later cortical stage.     

A ratio model of perceived speed in the human visual system

The perceived speed of moving images changes over time. Prolonged viewing of a pattern (adaptation) leads to an exponential decrease in its perceived speed. Similarly, responses of neurones tuned to motion reduce exponentially over time. It is tempting to link these phenomena. However, under certain conditions, perceived speed increases after adaptation and the time course of these perceptual effects varies widely. We propose a model that comprises two temporally tuned mechanisms whose sensitivities reduce exponentially over time. Perceived speed is taken as the ratio of these filters' outputs. The model captures increases and decreases in perceived speed following adaptation and describes our data well with just four free parameters. Whilst the model captures perceptual time courses that vary widely, parameter estimates for the time constants of the underlying filters are in good agreement with estimates of the time course of adaptation of direction selective neurones in the mammalian visual system.     

A model for size- and rotation-invariant pattern processing in the visual system

The mapping of retinal space onto the striate cortex of some mammals can be approximated by a log-polar function. It has been proposed that this mapping is of functional importance for scale-and rotation-invariant pattern recognition in the visual system. An exact log-polar transform converts centered scaling and rotation into translations. A subsequent translation-invariant transform, such as the absolute value of the Fourier transform, thus generates overall size-and rotation-invariance. In our model, the translation-invariance is realized via the R-transform. This transform can be executed by simple neural networks, and it does not require the complex computations of the Fourier transform, used in Mellin-transform size-invariance models. The logarithmic space distortion and differentiation in the first processing stage of the model is realized via Mexican hat filters whose diameter increases linearly with eccentricity, similar to the characteristics of the receptive fields of retinal ganglion cells. Except for some special cases, the model can explain object recognition independent of size, orientation and position. Some general problems of Mellin-type size-invariance models-that also apply to our model-are discussed.     

A recurrent system incorporating characteristics of the visual system: a model for the function of backward neural connections in the visual system

A recurrent system is constructed in order to investigate the role of the backward neural connections found in the primate visual system. The system incorporates a layer to perform localized spatial frequency analysis of input images, a function which has been assumed to take place in the primary visual cortex. The function of the system is examined by simulation. The results show that the system can separate an object pattern from its background, irrespective of its precise position. The acceptable displacement range for input images is determined from the width of the window function used to calculate the local Fourier transform. A multilayer version of the above recurrent system is also constructed.     

Interaction between the amygdala and the medial temporal lobe memory system predicts better memory for emotional events

Emotional events are remembered better than neutral events possibly because the amygdala enhances the function of medial temporal lobe (MTL) memory system (modulation hypothesis). Although this hypothesis has been supported by much animal research, evidence from humans has been scarce and indirect. We investigated this issue using event-related fMRI during encoding of emotional and neutral pictures. Memory performance after scanning showed a retention advantage for emotional pictures. Successful encoding activity in the amygdala and MTL memory structures was greater and more strongly correlated for emotional than for neutral pictures. Moreover, a double dissociation was found along the longitudinal axis of the MTL memory system: activity in anterior regions predicted memory for emotional items, whereas activity in posterior regions predicted memory for neutral items. These results provide direct evidence for the modulation hypothesis in humans and reveal a functional specialization within the MTL regarding the effects of emotion on memory formation.     

Directing the mind's eye: prefrontal, inferior and medial temporal mechanisms for visual working memory

Human and nonhuman primates have a remarkable ability to recall, maintain and manipulate visual images in the absence of external sensory stimulation. Evidence from lesion, single-unit neurophysiological and neuroimaging studies shows that these visual working memory processes are consistently associated with sustained activity in object-selective inferior temporal neurons. Furthermore, results from these studies suggest that mnemonic activity in the inferior temporal cortex is, in turn, supported by top-down inputs from multimodal regions in prefrontal and medial temporal cortex, and under some circumstances, from the hippocampus.