Mechanisms underlying the basal forebrain enhancement of top-down and bottom-up attention.

Both attentional signals from frontal cortex and neuromodulatory signals from basal forebrain (BF) have been shown to influence information processing in the primary visual cortex (V1). These two systems exert complementary effects on their targets, including increasing firing rates and decreasing interneuronal correlations. Interestingly, experimental research suggests that the cholinergic system is important for increasing V1's sensitivity to both sensory and attentional information. To see how the BF and top-down attention act together to modulate sensory input, we developed a spiking neural network model of V1 and thalamus that incorporated cholinergic neuromodulation and top-down attention. In our model, activation of the BF had a broad effect that decreases the efficacy of top-down projections and increased the reliance of bottom-up sensory input. In contrast, we demonstrated how local release of acetylcholine in the visual cortex, which was triggered through top-down gluatmatergic projections, could enhance top-down attention with high spatial specificity. Our model matched experimental data showing that the BF and top-down attention decrease interneuronal correlations and increase between-trial reliability. We found that decreases in correlations were primarily between excitatory-inhibitory pairs rather than excitatory-excitatory pairs and suggest that excitatory-inhibitory decorrelation is necessary for maintaining low levels of excitatory-excitatory correlations. Increased inhibitory drive via release of acetylcholine in V1 may then act as a buffer, absorbing increases in excitatory-excitatory correlations that occur with attention and BF stimulation. These findings will lead to a better understanding of the mechanisms underyling the BF's interactions with attention signals and influences on correlations.

Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments.

Neuromodulatory systems, including the noradrenergic, serotonergic, dopaminergic, and cholinergic systems, track environmental signals, such as risks, rewards, novelty, effort, and social cooperation. These systems provide a foundation for cognitive function in higher organisms; attention, emotion, goal-directed behavior, and decision-making derive from the interaction between the neuromodulatory systems and brain areas, such as the amygdala, frontal cortex, hippocampus, and sensory cortices. Given their strong influence on behavior and cognition, these systems also play a key role in disease states and are the primary target of many current treatment strategies. The fact that these systems interact with each other either directly or indirectly, however, makes it difficult to understand how a failure in one or more systems can lead to a particular symptom or pathology. In this review, we explore experimental evidence, as well as focus on computational and theoretical models of neuromodulation. Better understanding of neuromodulatory systems may lead to the development of novel treatment strategies for a number of brain disorders.

A viral strategy for targeting and manipulating interneurons across vertebrate species.

A fundamental impediment to understanding the brain is the availability of inexpensive and robust methods for targeting and manipulating specific neuronal populations. The need to overcome this barrier is pressing because there are considerable anatomical, physiological, cognitive and behavioral differences between mice and higher mammalian species in which it is difficult to specifically target and manipulate genetically defined functional cell types. In particular, it is unclear the degree to which insights from mouse models can shed light on the neural mechanisms that mediate cognitive functions in higher species, including humans. Here we describe a novel recombinant adeno-associated virus that restricts gene expression to GABAergic interneurons within the telencephalon. We demonstrate that the viral expression is specific and robust, allowing for morphological visualization, activity monitoring and functional manipulation of interneurons in both mice and non-genetically tractable species, thus opening the possibility to study GABAergic function in virtually any vertebrate species.

Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex.

The dopaminergic system has been shown to control the amount of noise in the prefrontal cortex (PFC) and likely plays an important role in working memory and the pathophysiology of schizophrenia. We developed a model that takes into account the known receptor distributions of D1 and D2 receptors, the changes these receptors have on neuron response properties, as well as identified circuitry involved in working memory. Our model suggests that D1 receptor under-stimulation in supragranular layers gates internal noise into the PFC leading to cognitive symptoms as has been proposed in attention disorders, while D2 over-stimulation gates noise into the PFC by over-activation of cortico-striatal projecting neurons in infragranular layers. We apply this model in the context of a memory-guided saccade paradigm and show deficits similar to those observed in schizophrenic patients. We also show set-shifting impairments similar to those observed in rodents with D1 and D2 receptor manipulations. We discuss how the introduction of noise through changes in D1 and D2 receptor activation may account for many of the symptoms of schizophrenia depending on where this dysfunction occurs in the PFC.

Optogenetic Activation of Normalization in Alert Macaque Visual Cortex.

Normalization has been proposed as a canonical computation that accounts for a variety of nonlinear neuronal response properties associated with sensory processing and higher cognitive functions. A key premise of normalization is that the excitability of a neuron is inversely proportional to the overall activity level of the network. We tested this by optogenetically activating excitatory neurons in alert macaque primary visual cortex and measuring changes in neuronal activity as a function of stimulation intensity, with or without variable-contrast visual stimulation. Optogenetic depolarization of excitatory neurons either facilitated or suppressed baseline activity, consistent with indirect recruitment of inhibitory networks. As predicted by the normalization model, neurons exhibited sub-additive responses to optogenetic and visual stimulation, which depended lawfully on stimulation intensity and luminance contrast. We conclude that the normalization computation persists even under the artificial conditions of optogenetic stimulation, underscoring the canonical nature of this form of neural computation.

A large-scale neural network model of the influence of neuromodulatory levels on working memory and behavior.

The dorsolateral prefrontal cortex (dlPFC), which is regarded as the primary site for visuospatial working memory in the brain, is significantly modulated by dopamine (DA) and norepinephrine (NE). DA and NE originate in the ventral tegmental area (VTA) and locus coeruleus (LC), respectively, and have been shown to have an "inverted-U" dose-response profile in dlPFC, where the level of arousal and decision-making performance is a function of DA and NE concentrations. Moreover, there appears to be a sweet spot, in terms of the level of DA and NE activation, which allows for optimal working memory and behavioral performance. When either DA or NE is too high, input to the PFC is essentially blocked. When either DA or NE is too low, PFC network dynamics become noisy and activity levels diminish. Mechanisms for how this is occurring have been suggested, however, they have not been tested in a large-scale model with neurobiologically plausible network dynamics. Also, DA and NE levels have not been simultaneously manipulated experimentally, which is not realistic in vivo due to strong bi-directional connections between the VTA and LC. To address these issues, we built a spiking neural network model that includes D1, α2A, and α1 receptors. The model was able to match the inverted-U profiles that have been shown experimentally for differing levels of DA and NE. Furthermore, we were able to make predictions about what working memory and behavioral deficits may occur during simultaneous manipulation of DA and NE outside of their optimal levels. Specifically, when DA levels were low and NE levels were high, cues could not be held in working memory due to increased noise. On the other hand, when DA levels were high and NE levels were low, incorrect decisions were made due to weak overall network activity. We also show that lateral inhibition in working memory may play a more important role in increasing signal-to-noise ratio than increasing recurrent excitatory input.

Simulation of cholinergic and noradrenergic modulation of behavior in uncertain environments.

Attention is a complex neurobiological process that involves rapidly and flexibly balancing sensory input and goal-directed predictions in response to environmental changes. The cholinergic and noradrenergic systems, which have been proposed to respond to expected and unexpected environmental uncertainty, respectively, play an important role in attention by differentially modulating activity in a multitude of cortical targets. Here we develop a model of an attention task that involves expected and unexpected uncertainty. The cholinergic and noradrenergic systems track this uncertainty and, in turn, influence cortical processing in five different, experimentally verified ways: (1) nicotinic enhancement of thalamocortical input, (2) muscarinic regulation of corticocortical feedback, (3) noradrenergic mediation of a network reset, (4) locus coeruleus (LC) activation of the basal forebrain (BF), and (5) cholinergic and noradrenergic balance between sensory input and frontal cortex predictions. Our results shed light on how the noradrenergic and cholinergic systems interact with each other and a distributed set of neural areas, and how this could lead to behavioral adaptation in the face of uncertainty.

Two stimuli are better than one: combination rules for prepulse inhibition revisited.

Stimuli that precede elicitation of the startle reflex can inhibit that reflex. J. R. Ison, M. Zuckerman, and J. M. Russo (1975), measuring the startle reflex in the rat, showed that the effect of combining two inhibiting stimuli was greater than that of either alone. They also demonstrated that this combination effect could be described by using the rule for adding the probability of independent events, expressing the effect of each prestimulus as the proportion of the response inhibited by that stimulus. The purpose of this research was to extend those findings using the startle blink in humans. In two experiments, participants encountered trials in which two stimuli (p1 and p2) were given in succession, ending with a blink-eliciting noise (S). In Experiment 1 (N=18) p1 was a synchronous tone and a word (appearing on a computer screen), and p2 was a blink-eliciting noise identical to S. In Experiment 2 (N=30), p1 was a soft tone and p2 was a word. For both experiments, two stimuli were more inhibiting than one, and the effect of combining p1 and p2 was accurately described by the Ison et al. combination rule.