Recent neurophysiological experiments have led to a promising “biased competition hypothesis” of the neural basis of attention. According to this hypothesis, attention appears as a sometimes non-linear property that results from a top-down biassing effect that influences the competitive and cooperative interactions that work both within cortical areas and between cortical areas. In this paper we describe a detailed dynamical analysis of the synaptic and neuronal spiking mechanisms underlying biased competition. We perform a detailed analysis of the dynamical capabilities of the system by exploring the stationary attractors in the parameter space via a mean field reduction consistent with the underlying synaptic and spiking dynamics. The nonstationary dynamical behaviour, as measured in neuronal recording experiments, is studied via an integrate-and-fire model with realistic dynamics. This elucidates the role of cooperation and competition in the dynamics of biased competition; and shows why feedback connections between cortical areas need optimally to be weaker by a factor of approximately 2.5 than the feedforward connections in an attentional network. We modelled the interaction between top-down attention and bottom up stimulus contrast effects

A detailed neural model is presented of how the laminar circuits of visual cortical areas V1 and V2 implement context-sensitive binding processes such as perceptual grouping and attention. The model proposes how specific laminar circuits allow the responses of visual cortical neurons to be determined not only by the stimuli within their classical receptive fields, but also to be strongly influenced by stimuli in the extra-classical surround. This context-sensitive visual processing can greatly enhance the analysis of visual scenes, especially those containing targets that are low contrast, partially occluded, or crowded by distractors. We show how interactions of feedforward, feedback and horizontal circuitry can implement several types of contextual processing simultaneously, using shared laminar circuits. In particular, we present computer simulations which suggest how top-down attention and preattentive perceptual grouping, two processes that are fundamental for visual binding, can interact, with attentional enhancement selectively propagating along groupings of both real and illusory contours, thereby showing how attention can selectively enhance object representations. These simulations also illustrate how attention may have a stronger facilitatory

Scene analysis in the mammalian visual system, conceived as a distributed and parallel process, faces the so-called binding problem. As a possible solution, the temporal correlation hypothesis has been suggested and implemented in phase-coding models.

switching Cognitive behaviour requires complex context-dependent mapping between sensory stimuli and actions. The same stimulus can lead to different behaviours depending on the situation, or the same behaviour may be elicited by different cueing stimuli. Neurons in the primate prefrontal cortex show task-speci®c ®ring activity during working memory delay periods. These neurons provide a neural substrate for mapping stimulus and response in a ¯exible, context- or rule-dependent, fashion. We describe here an integrate-and-®re network model to explain and investigate the different types of working-memory-related neuronal activity observed. The model contains different populations (or pools) of neurons (as found neurophysiologically) in attractor networks which respond in the delay period to the stimulus object, the stimulus position (`sensory pools'), to combinations of the stimulus sensory properties (e.g. the object identity or object location) and the response (`intermediate pools'), and to the response required (left or right) (`premotor pools'). The pools are arranged hierarchically, are linked by associative synaptic connections, and have global inhibition through inhibitory interneurons to implement competition. It is shown that a biasing attentional input to de®ne the current rule applied to the intermediate pools enables the system to select the correct response in what is a biased competition model of attention. The integrate-and-®re model not only produces realistic spiking dynamicals very similar to the neuronal data but also shows how dopamine could weaken and shorten the persistent neuronal activity in the delay period; and allows us to predict more response errors when dopamine is elevated because there

To investigate scene segmentation in the visual system we present a model of two reciprocally connected visual areas using spiking neurons. Area P corresponds to the orientation selective subsystem of the primary visual cortex, while the central visual area C is modeled as associative memory representing stimulus objects according to Hebbian learning. Without feedback from area C, a single stimulus results in relatively slow and irregular activity, synchronized only for neighboring patches (slow state), while in the complete model activity is faster with enlarged synchronization range (fast state). Presenting a superposition of several stimulus objects, scene segmentation happens on a time scale of hundreds of milliseconds by alternating epochs of the slow and fast state, where neurons representing the same object are simultaneously in the fast state. Correlation analysis reveals synchronization on different time scales as found in experiments (T,C,H peaks). On the fast time scale (T peaks, gamma frequency range), recordings from two sites coding either different or the same object lead to correlograms that are either at or exhibit oscillatory modulations with a central peak. This is in agreement with experimental findings while standard phase coding models would predict shifted peaks in the case of different objects.

Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.

Feedback connections are a prominent feature of cortical anatomy and are likely to have significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research.

Tiesinga – Stimulus competition by inhibitory interference 1 Stimulus competition by inhibitory interference When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience 19:1736-1753). When attention is directed towards the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). When attention is directed to either of the two stimuli presented alone, the firing rate remains the same or increases slightly. These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific asynchronous excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by a projection from the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays it approached the firing rate of the poor stimulus. When either stimulus was presented alone the neuron’s response was not altered by the change in delay. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons by changing the relative timing of inhibition rather than by changes in the degree of synchrony of interneuron networks. The mechanism proposed here for attentional modulation of firing rate – gain modulation by inhibitory interference – is likely to have more general applicability to cortical information processing. Tiesinga – Stimulus competition by inhibitory interference 2

For the recognition of objects there are a number of computational requirements that go beyond the detection of simple geometric features like oriented lines. When there are several partially occluded objects present in a visual scene one has to have an internal knowledge about the object to be identified, e.g. using associative memories.

Many existing independent component analysis algorithms include a preprocessing stage where the inputs are sphered. This amounts to normalising the data such that all correlations between the variables are removed. In this work, I show that sphering allows very weak contextual modulation to steer the development of meaningful features. Contextbiased competition has been proposed as a model of covert attention and I propose that sphering-like normalisation also allows weaker top-down bias to guide attention.