A monocular, unconscious form of visual attention

Sudden changes in our visual field capture our attention so that we are faster and more accurate in our responses to that region of space. The underlying mechanisms by which these behavioral improvements occur are unknown. Here we investigate the level of the visual system at which attentional capture first occurs by presenting cues to one eye and then a target to either the same or the opposite eye. We show that monocular cues initially only shorten response time if the target is presented in the same eye as the cue suggesting that the initial capture of attention occurs at monocular levels of the visual system. We use dual-cues that cannot be distinguished by binocular parts of the visual system but are detectable at monocular levels to show that performance enhancements occur entirely unconsciously and are not due to local sensory interactions. Furthermore, we show that the spatial and temporal properties of the new monocular cueing effect differ from standard binocular cueing. Our results inspire a monocular competition model where visual stimuli compete to generate a salience map at monocular levels of representation. © ARVO

Attention lights up new object representations before the old ones fade away

We investigated how attention shifts from one object to another by recording neuronal activity in the primary visual cortex. Monkeys performed a contour-grouping task in which they had to select a target curve and ignore a distractor curve. Some trials required a shift of attention, because the target and distractor curves were switched during the course of the trial. We monitored the dynamics of this attention shift in area V1, in which neuronal responses evoked by the target curve are stronger than those evoked by the distractor. The reallocation of attention was associated with a rapid and strong enhancement of responses to the newly attended curve, followed, after ∼60 ms, by a weaker suppression of responses to the curve from which attention was removed. We conclude that attention can be rapidly allocated to a new object before it disengages from the previously attended one. Copyright © 2006 Society for Neuroscience

The effects of pair-wise and higher order correlations on the firing rate of a post-synaptic neuron

Coincident firing of neurons projecting to a common target cell is likely to raise the probability of firing of this post-synaptic cell. Therefore synchronized firing constitutes a significant event for post-synaptic neurons and is likely to play a role in neuronal information processing. Physiological data on synchronized firing in cortical networks is primarily based on paired recordings and cross-correlation analysis. However, pair-wise correlations among all inputs onto a post-synaptic neuron do not uniquely determine the distribution of simultaneous post-synaptic events. We develop a framework in order to calculate the amount of synchronous firing that, based on maximum entropy, should exist in a homogeneous neural network in which the neurons have known pair-wise correlations and higher order structure is absent. According to the distribution of maximal entropy, synchronous events in which a large proportion of the neurons participates should exist, even in the case of weak pair-wise correlations. Network simulations also exhibit these highly synchronous events in the case of weak pair-wise correlations. If such a group of neurons provides input to a common post-synaptic target, these network bursts may enhance the impact of this input, especially in the case of a high post-synaptic threshold. Unfortunately, the proportion of neurons participating in synchronous bursts can be approximated by our method under restricted conditions. When these conditions are not fulfilled, the spike trains have less than maximal entropy, which is indicative of the presence of higher order structure. In this situation, the degree of synchronicity cannot be derived from the pair-wise correlations

A biologically plausible learning rule for deep learning in the brain

Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain. However, the methods used for deep learning by artificial neural networks are biologically unrealistic and would need to be replaced by biologically realistic counterparts. Previous biologically plausible reinforcement learning rules, like AGREL and AuGMEnT, showed promising results but focused on shallow networks with three layers. Will these learning rules also generalize to networks with more layers and can they handle tasks of higher complexity? Here, we demonstrate that these learning schemes indeed generalize to deep networks, if we include an attention network that propagates information about the selected action to lower network levels. The resulting learning rule, called Q-AGREL, is equivalent to a particular form of error-backpropagation that trains one output unit at any one time. To demonstrate the utility of the learning scheme for larger problems, we trained networks with two hidden layers on the MNIST dataset, a standard and interesting Machine Learning task. Our results demonstrate that the capability of Q-AGREL is comparable to that of error backpropagation, although the learning rate is 1.5-2 times slower because the network has to learn by trial-and-error and updates the action value of only one output unit at a time. Our results provide new insights into how deep learning can be implemented in the brain

Continuous-time on-policy neural reinforcement learning of working memory tasks

As living organisms, one of our primary characteristics is the ability to rapidly process and react to unknown and unexpected events. To this end, we are able to recognize an event or a sequence of events and learn to respond properly. Despite advances in machine learning, current cognitive robotic systems are not able to rapidly and efficiently respond in the real world: the challenge is to learn to recognize both what is important, and also when to act. Reinforcement Learning (RL) is typically used to solve complex tasks: to learn the how. To respond quickly - to learn when - the environment has to be sampled often enough. For “enough”, a programmer has to decide on the step-size as a time-representation, choosing between a fine-grained representation of time (many state-transitions; difficult to learn with RL) or to a coarse temporal resolution (easier to learn with RL but lacking precise timing). Here, we derive a continuous-time version of on-policy SARSA-learning in a working-memory neural network model, AuGMEnT. Using a neural working memory network resolves the what problem, our when solution is built on the notion that in the real world, instantaneous actions of duration dt are actually impossible. We demonstrate how we can decouple action duration from the internal time-steps in the neural RL model using an action selection system. The resultant CT-AuGMEnT successfully learns to react to the events of a continuous-time task, without any pre-imposed specifications about the duration of the events or the delays between them