& What happens in the brain when you conjure up a mental image in your mind’s eye? We tested whether the particular regions of extrastriate cortex activated during mental imagery depend on the content of the image. Using functional magnetic resonance imaging (fMRI), we demonstrated selective activation within a region of cortex specialized for face perception during mental imagery of faces, and selective activation within a place-selective cortical region during imagery of places. In a further study, we compared the activation for imagery and perception in these regions, and found greater response magnitudes for perception than for imagery of the same items. Finally, we found that it is possible to determine the content of single cognitive events from an inspection of the fMRI data from individual imagery trials. These findings strengthen evidence that imagery and perception share common processing mechanisms, and demonstrate that the specific brain regions activated during mental imagery depend on the content of the visual image. &

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We study the synthesis of neural coding, selective attention and perceptual decision making. A hierarchical neural architecture is proposed, which implements Bayesian integration of noisy sensory input and topdown attentional priors, leading to sound perceptual discrimination. The model offers an explicit explanation for the experimentally observed modulation that prior information in one stimulus feature (location) can have on an independent feature (orientation). The network’s intermediate levels of representation instantiate known physiological properties of visual cortical neurons. The model also illustrates a possible reconciliation of cortical and neuromodulatory representations of uncertainty. 1

Physiological investigations into the neural basis of sensory attention have led to puzzling and contradictory results. Attention can seemingly lead to increased, decreased and unchanged neural activities, according to features of attentional experiments that are not well understood. We take one particular case in which activities increase as a result of attention, model its possible statistical underpinning, and relate our model to other attentional suggestions. Increased activities in population codes are associated with increased certainty about the encoded quantities. This increased certainty has to come from somewhere { in our model it emerges from particular changes in the model 's processing strategy. 1 Introduction Although hard to dene very precisely, attention has been a highly seductive target for experiments and models alike. The core idea, that some stimuli or stimulus features are treated dierently from others (in particular, more eectively) for the purposes of repr...

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.

Visual attention is attracted by salient stimuli that ‘pop out ’ from their surroundings. Attention can also be voluntarily directed to objects of current importance to the observer. What happens in the brain when these two processes interact? To mangle a well-known Wordsworth line, the world is too much for us — it contains far too much information for us to perceive at once. We typically pay attention to individual items, one after another. But which items? That depends on two distinct types of attentional mechanism. Bottom-up mechanisms are thought to operate on raw sensory input, rapidly and involuntarily shifting attention to salient visual features of potential importance — the spot of red against a field of green that could be a piece of fruit, the sudden

The synchrony of neurons in extrastriate visual cortex is modulated by selective attention even when there are only small changes in firing rate (Fries, Reynolds, Rorie, & Desimone, 2001). We used Hodgkin-Huxley type models of cortical neurons to investigate the mechanism by which the degree of synchrony can be modulated independently of changes in firing rates. The synchrony of local networks of model cortical interneurons interacting through GABAA synapses was modulated on a fast timescale by selectively activating a fraction of the interneurons. The activated interneurons became rapidly synchronized and suppressed the activity of the other neurons in the network but only if the network was in a restricted range of balanced synaptic background activity. During stronger background activity, the network did not synchronize, and for weaker background activity, the network synchronized but did not return to an asynchronous state after synchronizing. The inhibitory output of the network

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

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processing