Cholinergic Modulation Promotes Attentional Modulation in Primary Visual Cortex- A Modeling Study

Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

REDOX Balance in Oligodendrocytes Is Important for Zebrafish Visual System Regeneration

Zebrafish (Danio rerio) present continuous growth and regenerate many parts of their body after an injury. Fish oligodendrocytes, microglia and astrocytes support the formation of new connections producing effective regeneration of the central nervous system after a lesion. To understand the role of oligodendrocytes and the signals that mediate regeneration, we use the well-established optic nerve (ON) crush model. We also used sox10 fluorescent transgenic lines to label fully differentiated oligodendrocytes. To quench the effect of reactive oxygen species (ROS), we used the endogenous antioxidant melatonin. Using these tools, we measured ROS production by flow cytometry and explored the regeneration of the optic tectum (OT), the response of oligodendrocytes and their mitochondria by confocal microscopy and Western blot. ROS are produced by oligodendrocytes 3 h after injury and JNK activity is triggered. Concomitantly, there is a decrease in the number of fully differentiated oligodendrocytes in the OT and in their mitochondrial population. By 24 h, oligodendrocytes partially recover. Exposure to melatonin blocks the changes observed in these oligodendrocytes at 3 h and increases their number and their mitochondrial populations after 24 h. Melatonin also blocks JNK upregulation and induces aberrant neuronal differentiation in the OT. In conclusion, a proper balance of ROS is necessary during visual system regeneration and exposure to melatonin has a detrimental impact.

Maternal diabetes decreases the expression of α2-adrenergic and M2 muscarinic receptors in the visual cortex of male rat neonates

AIMS

This study investigates the impact of maternal diabetes on the expression of α2-adrenergic and M2 muscarinic receptors in the primary visual cortex of male offspring born to diabetic rats.

MAIN METHODS

In adult female rats, a single dose of intraperitoneal streptozotocin (STZ) was used to induce diabetes (Diabetic group). Diabetes was controlled with insulin in the Insulin-treated group. Female rats in the control group received normal saline instead of STZ. Male newborns were euthanized at P0, P7, and P14, and the expression of α2-adrenergic and M2 muscarinic receptors in the primary visual cortex was determined using immunohistochemistry (IHC).

KEY FINDINGS

The study showed that α2-adrenergic and M2 muscarinic receptors were significantly suppressed in all layers of the primary visual cortex of male neonates born to diabetic rats at P0, P7, and P14 compared to the control group. The highest expression was for the Con group at P14 and the lowest one was in the Dia group at P0 for both receptors. The insulin treatment in diabetic mothers modulated the expression of these receptors to normal levels in their newborns.

SIGNIFICANCE

The results demonstrate maternal diabetes decreases the expression of α2-adrenergic and M2 muscarinic receptors in the primary visual cortex of male offspring born to diabetic rats. Insulin treatment can offset these effects of diabetes.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.

Multiple states in ongoing neural activity in the rat visual cortex

The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive. Using a custom-made electrode array, we recorded 32-site electrocorticograms in the primary and secondary visual cortex of freely behaving rats and determined the propagation patterns of spontaneous neural activity. Nonlinear dimensionality reduction and unsupervised clustering revealed multiple discrete states of the activity patterns. The activity remained stable in one state and suddenly jumped to another state. The diversity and dynamics of the internally switching cortical states would imply flexibility of neural responses to various external inputs.

Neurochemistry of Visual Attention

Visual attention is the cognitive process that mediates the selection of important information from the environment. This selection is usually controlled by bottom-up and top-down attentional biasing. Since for most humans vision is the dominant sense, visual attention is critically important for higher-order cognitive functions and related deficits are a core symptom of many neuropsychiatric and neurological disorders. Here, we summarize the importance and relative contributions of different neuromodulators and neurotransmitters to the neural mechanisms of top-down and bottom-up attentional control. We will not only review the roles of widely accepted neuromodulators, such as acetylcholine, dopamine and noradrenaline, but also the contributions of other modulatory substances. In doing so, we hope to shed some light on the current understanding of the role of neurochemistry in shaping neuron properties contributing to the allocation of attention in the visual field.

Role of Choline in Ocular Diseases

Choline is essential for maintaining the structure and function of cells in humans. Choline plays an important role in eye health and disease. It is a precursor of acetylcholine, a neurotransmitter of the parasympathetic nervous system, and it is involved in the production and secretion of tears by the lacrimal glands. It also contributes to the stability of the cells and tears on the ocular surface and is involved in retinal development and differentiation. Choline deficiency is associated with retinal hemorrhage, glaucoma, and dry eye syndrome. Choline supplementation may be effective for treating these diseases.

How Outcome Uncertainty Mediates Attention, Learning, and Decision-Making

Animals and humans evolved sophisticated nervous systems that endowed them with the ability to form internal-models or beliefs and make predictions about the future to survive and flourish in a world in which future outcomes are often uncertain. Crucial to this capacity is the ability to adjust behavioral and learning policies in response to the level of uncertainty. Until recently, the neuronal mechanisms that could underlie such uncertainty-guided control have been largely unknown. In this review, I discuss newly discovered neuronal circuits in primates that represent uncertainty about future rewards and propose how they guide information-seeking, attention, decision-making, and learning to help us survive in an uncertain world. Lastly, I discuss the possible relevance of these findings to learning in artificial systems.

Attention in Psychology, Neuroscience, and Machine Learning

Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.

Dynamic coupling of whole-brain neuronal and neurotransmitter systems

Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the functional repertoire associated with human brain function, which is impossible to explain through long-term plasticity. Neuromodulation evolved to allow for such flexibility by dynamically updating the effectivity of the fixed anatomical connectivity. Here, we introduce a theoretical framework modeling the dynamical mutual coupling between the neuronal and neurotransmitter systems. We demonstrate that this framework is crucial to advance our understanding of whole-brain dynamics by bidirectional coupling of the two systems through combining multimodal neuroimaging data (diffusion magnetic resonance imaging [dMRI], functional magnetic resonance imaging [fMRI], and positron electron tomography [PET]) to explain the functional effects of specific serotoninergic receptor (5-HT_2AR) stimulation with psilocybin in healthy humans. This advance provides an understanding of why psilocybin is showing considerable promise as a therapeutic intervention for neuropsychiatric disorders including depression, anxiety, and addiction. Overall, these insights demonstrate that the whole-brain mutual coupling between the neuronal and the neurotransmission systems is essential for understanding the remarkable flexibility of human brain function despite having to rely on fixed anatomical connectivity.