Neuromodulation and cortical function: modeling the physiological basis of behavior

Switching between External and Internal Attention in Hippocampal Networks

Everyday experience requires processing external signals from the world around us and internal information retrieved from memory. To do both, the brain must fluctuate between states that are optimized for external versus internal attention. Here, we focus on the hippocampus as a region that may serve at the interface between these forms of attention and ask how it switches between prioritizing sensory signals from the external world versus internal signals related to memories and thoughts. Pharmacological, computational, and animal studies have identified input from the cholinergic basal forebrain as important for biasing the hippocampus toward processing external information, whereas complementary research suggests the dorsal attention network (DAN) may aid in allocating attentional resources toward accessing internal information. We therefore tested the hypothesis that the basal forebrain and DAN drive the hippocampus toward external and internal attention, respectively. We used data from 29 human participants (17 female) who completed two attention tasks during fMRI. One task (memory-guided) required proportionally more internal attention, and proportionally less external attention, than the other (explicitly instructed). We discovered that background functional connectivity between the basal forebrain and hippocampus was stronger during the explicitly instructed versus memory-guided task. In contrast, DAN-hippocampus background connectivity was stronger during the memory-guided versus explicitly instructed task. Finally, the strength of DAN-hippocampus background connectivity was correlated with performance on the memory-guided but not explicitly instructed task. Together, these results provide evidence that the basal forebrain and DAN may modulate the hippocampus to switch between external and internal attention.SIGNIFICANCE STATEMENT How does the brain balance the need to pay attention to internal thoughts and external sensations? We focused on the human hippocampus, a region that may serve at the interface between internal and external attention, and asked how its functional connectivity varies based on attentional states. The hippocampus was more strongly coupled with the cholinergic basal forebrain when attentional states were guided by the external world rather than retrieved memories. This pattern flipped for functional connectivity between the hippocampus and dorsal attention network, which was higher for attention tasks that were guided by memory rather than external cues. Together, these findings show that distinct networks in the brain may modulate the hippocampus to switch between external and internal attention.

Weaker situations: Uncertainty reveals individual differences in learning: Implications for PTSD

Few individuals who experience trauma develop posttraumatic stress disorder (PTSD). Therefore, the identification of individual differences that signal increased risk for PTSD is important. Lissek et al. (2006) proposed using a weak rather than a strong situation to identify individual differences. A weak situation involves less-salient cues as well as some degree of uncertainty, which reveal individual differences. A strong situation involves salient cues with little uncertainty, which produce consistently strong responses. Results from fear conditioning studies that support this hypothesis are discussed briefly. This review focuses on recent findings from three learning tasks: classical eyeblink conditioning, avoidance learning, and a computer-based task. These tasks are interpreted as weaker learning situations in that they involve some degree of uncertainty. Individual differences in learning based on behavioral inhibition, which is a risk factor for PTSD, are explored. Specifically, behaviorally inhibited individuals and rodents (i.e., Wistar Kyoto rats), as well as individuals expressing PTSD symptoms, exhibit enhanced eyeblink conditioning. Behaviorally inhibited rodents also demonstrate enhanced avoidance responding (i.e., lever pressing). Both enhanced eyeblink conditioning and avoidance are most evident with schedules of partial reinforcement. Behaviorally inhibited individuals also performed better on reward and punishment trials than noninhibited controls in a probabilistic category learning task. Overall, the use of weaker situations with uncertain relationships may be more ecologically valid than learning tasks in which the aversive event occurs on every trial and may provide more sensitivity for identifying individual differences in learning for those at risk for, or expressing, PTSD symptoms.

Genetically encoded fluorescent sensors for imaging neuronal dynamics in vivo

The brain relies on many forms of dynamic activities in individual neurons, from synaptic transmission to electrical activity and intracellular signaling events. Monitoring these neuronal activities with high spatiotemporal resolution in the context of animal behavior is a necessary step to achieve a mechanistic understanding of brain function. With the rapid development and dissemination of highly optimized genetically encoded fluorescent sensors, a growing number of brain activities can now be visualized in vivo. To date, cellular calcium imaging, which has been largely used as a proxy for electrical activity, has become a mainstay in systems neuroscience. While challenges remain, voltage imaging of neural populations is now possible. In addition, it is becoming increasingly practical to image over half a dozen neurotransmitters, as well as certain intracellular signaling and metabolic activities. These new capabilities enable neuroscientists to test previously unattainable hypotheses and questions. This review summarizes recent progress in the development and delivery of genetically encoded fluorescent sensors, and highlights example applications in the context of in vivo imaging.

Neurobiological reduction: From cellular explanations of behavior to interventions

Scientific reductionism, the view that higher level functions can be explained by properties at some lower-level or levels, has been an assumption of nervous system analyses since the acceptance of the neuron doctrine in the late 19th century, and became a dominant experimental approach with the development of intracellular recording techniques in the mid-20th century. Subsequent refinements of electrophysiological approaches and the continual development of molecular and genetic techniques have promoted a focus on molecular and cellular mechanisms in experimental analyses and explanations of sensory, motor, and cognitive functions. Reductionist assumptions have also influenced our views of the etiology and treatment of psychopathologies, and have more recently led to claims that we can, or even should, pharmacologically enhance the normal brain. Reductionism remains an area of active debate in the philosophy of science. In neuroscience and psychology, the debate typically focuses on the mind-brain question and the mechanisms of cognition, and how or if they can be explained in neurobiological terms. However, these debates are affected by the complexity of the phenomena being considered and the difficulty of obtaining the necessary neurobiological detail. We can instead ask whether features identified in neurobiological analyses of simpler aspects in simpler nervous systems support current molecular and cellular approaches to explaining systems or behaviors. While my view is that they do not, this does not invite the opposing view prevalent in dichotomous thinking that molecular and cellular detail is irrelevant and we should focus on computations or representations. We instead need to consider how to address the long-standing dilemma of how a nervous system that ostensibly functions through discrete cell to cell communication can generate population effects across multiple spatial and temporal scales to generate behavior.

External drivers of BOLD signal's non-stationarity

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.

Transcranial magnetic stimulation of the brain: What is stimulated? - A consensus and critical position paper

Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes^1-6, but it is not known whether these subtypes have correspondingly diverse patterns of activity in the living brain. Here we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, which are organized by a single factor: position along the main axis of transcriptomic variation. We combined in vivo two-photon calcium imaging of mouse V1 with a transcriptomic method to identify mRNA for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1-3 into a three-level hierarchy of 5 subclasses, 11 types and 35 subtypes using previously defined transcriptomic clusters³. Responses to visual stimuli differed significantly only between subclasses, with cells in the Sncg subclass uniformly suppressed, and cells in the other subclasses predominantly excited. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory subtypes that fired more in resting, oscillatory brain states had a smaller fraction of their axonal projections in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro⁷, and expressed more inhibitory cholinergic receptors. Subtypes that fired more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 subtypes shape state-dependent cortical processing.

A generalized cortical activity pattern at internally generated mental context boundaries during unguided narrative recall

Current theory and empirical studies suggest that humans segment continuous experiences into events based on the mismatch between predicted and actual sensory inputs; detection of these ‘event boundaries’ evokes transient neural responses. However, boundaries can also occur at transitions between internal mental states, without relevant external input changes. To what extent do such ‘internal boundaries’ share neural response properties with externally driven boundaries? We conducted an fMRI experiment where subjects watched a series of short movies and then verbally recalled the movies, unprompted, in the order of their choosing. During recall, transitions between movies thus constituted major boundaries between internal mental contexts, generated purely by subjects’ unguided thoughts. Following the offset of each recalled movie, we observed stereotyped spatial activation patterns in the default mode network, especially the posterior medial cortex, consistent across different movie contents and even across the different tasks of movie watching and recall. Surprisingly, the between-movie boundary patterns did not resemble patterns at boundaries between events within a movie. Thus, major transitions between mental contexts elicit neural phenomena shared across internal and external modes and distinct from within-context event boundary detection, potentially reflecting a cognitive state related to the flushing and reconfiguration of situation models.

A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

Recent evidence has demonstrated that during visual spatial attention sampling, neural activity and behavioral performance exhibit large fluctuations. To understand the origin of these fluctuations and their functional role, here, we introduce a mechanism based on the dynamical activity pattern (attention spotlight) emerging from neural circuit models in the transition regime between different dynamical states. This attention activity pattern with rich spatiotemporal dynamics flexibly samples from different stimulus locations, explaining many key aspects of temporal fluctuations such as variable theta oscillations of visual spatial attention. Moreover, the mechanism expands our understanding of how visual attention exploits spatially complex fluctuations characterized by superdiffusive motion in space and makes experimentally testable predictions. We further illustrate that attention sampling based on such spatiotemporal fluctuations provides profound functional advantages such as adaptive switching between exploitation and exploration activities and is particularly efficient at sampling natural scenes with multiple salient objects.

Somatosensory Cortex Repetitive Transcranial Magnetic Stimulation and Associative Sensory Stimulation of Peripheral Nerves Could Assist Motor and Sensory Recovery After Stroke

Background

We investigated whether transcranial magnetic stimulation (rTMS) over the primary somatosensory cortex (S1) and sensory stimulation (SS) could promote upper limb recovery in participants with subacute stroke.

Methods

Participants were randomized into four groups: rTMS/Sham SS, Sham rTMS/SS, rTMS/SS, and control group (Sham rTMS/Sham SS). Participants underwent ten sessions of sham or active rTMS over S1 (10 Hz, 1,500 pulses, 120% of resting motor threshold, 20 min), followed by sham or active SS. The SS involved active sensory training (exploring features of objects and graphesthesia, proprioception exercises), mirror therapy, and Transcutaneous electrical nerve stimulation (TENS) in the region of the median nerve in the wrist (stimulation intensity as the minimum intensity at which the participants reported paresthesia; five electrical pulses of 1 ms duration each at 10 Hz were delivered every second over 45 min). Sham stimulations occurred as follows: Sham rTMS, coil was held while disconnected from the stimulator, and rTMS noise was presented with computer loudspeakers with recorded sound from a real stimulation. The Sham SS received therapy in the unaffected upper limb, did not use the mirror and received TENS stimulation for only 60 seconds. The primary outcome was the Body Structure/Function: Fugl-Meyer Assessment (FMA) and Nottingham Sensory Assessment (NSA); the secondary outcome was the Activity/Participation domains, assessed with Box and Block Test, Motor Activity Log scale, Jebsen-Taylor Test, and Functional Independence Measure.

Results

Forty participants with stroke ischemic (n = 38) and hemorrhagic (n = 2), men (n = 19) and women (n = 21), in the subacute stage (10.6 ± 6 weeks) had a mean age of 62.2 ± 9.6 years, were equally divided into four groups (10 participants in each group). Significant somatosensory improvements were found in participants receiving active rTMS and active SS, compared with those in the control group (sham rTMS with sham SS). Motor function improved only in participants who received active rTMS, with greater effects when active rTMS was combined with active SS.

Conclusion

The combined use of SS with rTMS over S1 represents a more effective therapy for increasing sensory and motor recovery, as well as functional independence, in participants with subacute stroke.

Clinical Trial Registration

[clinicaltrials.gov], identifier [NCT03329807].

Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain

Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network's internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4-12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli-such as default mode, dorsal attentional, and frontoparietal networks-are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain's capability for continuous switching between two modes, which is crucial for the emergence of consciousness.

Theta-Burst Stimulation of Forearm Muscles in Patients With Complex Regional Pain Syndrome: Influence on Brain and Clinical Outcomes

Objective: Complex regional pain syndrome (CRPS) is a common pain condition characterized by the changes in the brain that are not yet addressed by conventional treatment regimens. Repetitive peripheral magnetic stimulation (rPMS) of muscles is painless and non-invasive and can influence these changes (the induction of brain plasticity) to reduce pain and improve motricity. In patients with CRPS, this open-label pilot study tested rPMS after-effects on the pain intensity and sensorimotor control of the upper limb along with the excitability changes of the primary motor cortex (M1).

Methods: Eight patients with CRPS were enrolled in a single-session program. Patients were tested at pre- and post-rPMS over the flexor digitorum superficialis (FDS) muscle. The clinical outcomes were pain intensity, proprioception, active range of motion (ROM), and grip strength. M1 excitability was tested using the single- and paired-pulse transcranial magnetic stimulation (TMS) of M1.

Results: In our small sample study, rPMS reduced instant and week pain, improved proprioception and ROM, and reduced the hemispheric imbalance of several TMS outcomes. The more M1 contralateral to the CRPS side was hyperactivated at baseline, the more pain was reduced.

Discussion: This open-label pilot study provided promising findings for the use of rPMS in CRPS with a focus on M1 plastic changes. Future randomized, placebo-controlled clinical trials should confirm the existence of a causal relationship between the TMS outcomes and post-rPMS decrease of pain. This will favor the development of personalized treatments of peripheral non-invasive neurostimulation in CRPS.

Complex Regional Pain Syndrome. A Comprehensive Review on Neuroplastic Changes Supporting the Use of Non-invasive Neurostimulation in Clinical Settings

Background: Complex regional pain syndrome (CRPS) is a rare debilitating disorder characterized by severe pain affecting one or more limbs. CRPS presents a complex multifactorial physiopathology. The peripheral and sensorimotor abnormalities reflect maladaptive changes of the central nervous system. These changes of volume, connectivity, activation, metabolism, etc., could be the keys to understand chronicization, refractoriness to conventional treatment, and developing more efficient treatments. Objective: This review discusses the use of non-pharmacological, non-invasive neurostimulation techniques in CRPS, with regard to the CRPS physiopathology, brain changes underlying chronicization, conventional approaches to treat CRPS, current evidence, and mechanisms of action of peripheral and brain stimulation. Conclusion: Future work is warranted to foster the evidence of the efficacy of non-invasive neurostimulation in CRPS. It seems that the approach has to be individualized owing to the integrity of the brain and corticospinal function. Non-invasive neurostimulation of the brain or of nerve/muscles/spinal roots, alone or in combination with conventional therapy, represents a fertile ground to develop more efficient approaches for pain management in CRPS.

AIM: A network model of attention in auditory cortex

Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.

Selective attention plays a key role in how we navigate our everyday lives. For example, at a cocktail party, we can attend to friend’s speech amidst other speakers, music, and background noise. In stark contrast, hundreds of millions of people with hearing impairment and other disorders find such environments overwhelming and debilitating. Understanding the mechanisms underlying selective attention may lead to breakthroughs in improving the quality of life for those negatively affected. Here, we propose a mechanistic network model of attention in primary auditory cortex based on attentional inhibitory modulation (AIM). In the AIM model, attention targets specific cortical inhibitory neurons, which then modulate local cortical circuits to emphasize a particular feature of sounds and suppress competing features. We show that the AIM model can account for experimental observations across different species and stimulus domains. We also demonstrate that the same mechanisms can enable listeners to flexibly switch between attending to specific targets sounds and monitoring the environment in complex acoustic scenes, such as a cocktail party. The AIM network provides a theoretical framework which can work in tandem with new experiments to help unravel cortical circuits underlying attention.

Effects of muscarinic and nicotinic receptors on contextual modulation in macaque area V1

Context affects the salience and visibility of image elements in visual scenes. Collinear flankers can enhance or decrease the perceptual and neuronal sensitivity to flanked stimuli. These effects are mediated through lateral interactions between neurons in the primary visual cortex (area V1), in conjunction with feedback from higher visual areas. The strength of lateral interactions is affected by cholinergic neuromodulation. Blockade of muscarinic receptors should increase the strength of lateral intracortical interactions, while nicotinic blockade should reduce thalamocortical feed-forward drive. Here we test this proposal through local iontophoretic application of the muscarinic receptor antagonist scopolamine and the nicotinic receptor antagonist mecamylamine, while recording single cells in parafoveal representations in awake fixating macaque V1. Collinear flankers generally reduced neuronal contrast sensitivity. Muscarinic and nicotinic receptor blockade equally reduced neuronal contrast sensitivity. Contrary to our hypothesis, flanker interactions were not systematically affected by either receptor blockade.

Statistical complexity is maximized close to criticality in cortical dynamics

Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity (with higher spiking variability) to desynchronized and disordered regimes (with lower spiking variability). It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here we use a symbolic information approach to show that, despite the monotonical increase of the Shannon entropy between ordered and disordered regimes, we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. More specifically, we show that statistical complexity is maximized close to criticality for cortical spiking data of urethane-anesthetized rats, as well as for a network model of excitable elements that presents a critical point of a nonequilibrium phase transition.

The environmental sculpting hypothesis of juvenile and adult hippocampal neurogenesis

We propose that a major contribution of juvenile and adult hippocampal neurogenesis is to allow behavioral experience to sculpt dentate gyrus connectivity such that sensory attributes that are relevant to the animal's environment are more strongly represented. This "specialized" dentate is then able to store a larger number of discriminable memory representations. Our hypothesis builds on accumulating evidence that neurogenesis declines to low levels prior to adulthood in many species. Rather than being necessary for ongoing hippocampal function, as several current theories posit, we argue that neurogenesis has primarily a prospective function, in that it allows experience to shape hippocampal circuits and optimize them for future learning in the particular environment in which the animal lives. Using an anatomically-based simulation of the hippocampus (BACON), we demonstrate that environmental sculpting of this kind would reduce overlap among hippocampal memory representations and provide representation cells with more information about an animal's current situation; consequently, it would allow more memories to be stored and accurately recalled without significant interference. We describe several new, testable predictions generated by the sculpting hypothesis and evaluate the hypothesis with respect to existing evidence. We argue that the sculpting hypothesis provides a strong rationale for why juvenile and adult neurogenesis occurs specifically in the dentate gyrus and why it declines significantly prior to adulthood.

Experience enhances certainty about olfactory stimuli under bulbar cholinergic control

We present evidence that experience and cholinergic modulation in an early sensory network interact to improve certainty about olfactory stimuli. The data we present are in agreement with existing theoretical ideas about the functional role of acetylcholine but highlight the importance of early sensory networks in addition to cortical networks. We use a simple behavioral paradigm in mice which allows us to measure certainty about a stimulus via the response amplitude to a condition and novel stimuli. We conclude that additional learning increases certainty and that the slope of this relationship can be modulated by activation of muscarinic cholinergic receptors in the olfactory bulb.

Sparse coding with a somato-dendritic rule

Cortical neurons are silent most of the time: sparse activity enables low-energy computation in the brain, and promises to do the same in neuromorphic hardware. Beyond power efficiency, sparse codes have favourable properties for associative learning, as they can store more information than local codes but are easier to read out than dense codes. Auto-encoders with a sparse constraint can learn sparse codes, and so can single-layer networks that combine recurrent inhibition with unsupervised Hebbian learning. But the latter usually require fast homeostatic plasticity, which could lead to catastrophic forgetting in embodied agents that learn continuously. Here we set out to explore whether plasticity at recurrent inhibitory synapses could take up that role instead, regulating both the population sparseness and the firing rates of individual neurons. We put the idea to the test in a network that employs compartmentalised inputs to solve the task: rate-based dendritic compartments integrate the feedforward input, while spiking integrate-and-fire somas compete through recurrent inhibition. A somato-dendritic learning rule allows somatic inhibition to modulate nonlinear Hebbian learning in the dendrites. Trained on MNIST digits and natural images, the network discovers independent components that form a sparse encoding of the input and support linear decoding. These findings confirm that intrinsic homeostatic plasticity is not strictly required for regulating sparseness: inhibitory synaptic plasticity can have the same effect. Our work illustrates the usefulness of compartmentalised inputs, and makes the case for moving beyond point neuron models in artificial spiking neural networks.

Vagus nerve stimulation (VNS)-induced layer-specific modulation of evoked responses in the sensory cortex of rats

Neuromodulation achieved by vagus nerve stimulation (VNS) induces various neuropsychiatric effects whose underlying mechanisms of action remain poorly understood. Innervation of neuromodulators and a microcircuit structure in the cerebral cortex informed the hypothesis that VNS exerts layer-specific modulation in the sensory cortex and alters the balance between feedforward and feedback pathways. To test this hypothesis, we characterized laminar profiles of auditory-evoked potentials (AEPs) in the primary auditory cortex (A1) of anesthetized rats with an array of microelectrodes and investigated the effects of VNS on AEPs and stimulus specific adaptation (SSA). VNS predominantly increased the amplitudes of AEPs in superficial layers, but this effect diminished with depth. In addition, VNS exerted a stronger modulation of the neural responses to repeated stimuli than to deviant stimuli, resulting in decreased SSA across all layers of the A1. These results may provide new insights that the VNS-induced neuropsychiatric effects may be attributable to a sensory gain mechanism: VNS strengthens the ascending input in the sensory cortex and creates an imbalance in the strength of activities between superficial and deep cortical layers, where the feedfoward and feedback pathways predominantly originate, respectively.

Noradrenergic Regulation of Hippocampus-Dependent Memory

Neuromodulation regulates critical functions of CNS synapses, ranging from neural circuit development to high-order cognitive processes, including learning and memory. This broad scope of action is generally mediated through alterations of the strength of synaptic transmission (i.e. synaptic plasticity). Changes in synaptic strength are widely considered to be a cellular representation of learned information. Noradrenaline is a neuromodulator that is secreted throughout the brain in response to novelty or increased arousal. Once released, noradrenaline activates metabotropic receptors, initiating intracellular signaling cascades that promote enduring changes in synaptic strength and facilitate memory storage. Here, we provide an overview of noradrenergic modulation of synaptic plasticity and memory formation within mammalian neural circuits, which has broad applicability within the neurotherapeutics community. Advances in our understanding of noradrenaline in the context of these processes may provide a foundation for refining treatment strategies for multiple brain diseases, ranging from post-traumatic stress disorder to Alzheimer's Disease.

The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation

The space of possible neural models is enormous and under-explored. Single cell computational neuroscience models account for a range of dynamical properties of membrane potential, but typically do not address network function. In contrast, most models focused on network function address the dimensions of excitatory weight matrices and firing thresholds without addressing the complexities of metabotropic receptor effects on intrinsic properties. There are many under-explored dimensions of neural parameter space, and the field needs a framework for representing what has been explored and what has not. Possible frameworks include maps of parameter spaces, or efforts to categorize the fundamental elements and molecules of neural circuit function. Here we review dimensions that are under-explored in network models that include the metabotropic modulation of synaptic plasticity and presynaptic inhibition, spike frequency adaptation due to calcium-dependent potassium currents, and afterdepolarization due to calcium-sensitive non-specific cation currents and hyperpolarization activated cation currents. Neuroscience research should more effectively explore possible functional models incorporating under-explored dimensions of neural function.

Preparation for upcoming attentional states in the hippocampus and medial prefrontal cortex

Goal-directed attention is usually studied by providing individuals with explicit instructions on what they should attend to. But in daily life, we often use past experiences to guide our attentional states. Given the importance of memory for predicting upcoming events, we hypothesized that memory-guided attention is supported by neural preparation for anticipated attentional states. We examined preparatory coding in the human hippocampus and mPFC, two regions that are important for memory-guided behaviors, in two tasks: one where attention was guided by memory and another in which attention was explicitly instructed. Hippocampus and mPFC exhibited higher activity for memory-guided vs. explicitly instructed attention. Furthermore, representations in both regions contained information about upcoming attentional states. In the hippocampus, this preparation was stronger for memory-guided attention, and occurred alongside stronger coupling with visual cortex during attentional guidance. These results highlight the mechanisms by which memories are used to prepare for upcoming attentional goals.

Muscarinic and Nicotinic Modulation of Memory but not Verbal Problem-solving

Aspects of cognitive flexibility are modulated by the noradrenergic system, which is important in arousal and attention. Acetylcholine also modulates arousal and attention, as well as working memory. Effects of muscarinic and nicotinic antagonism on memory are well established. Our purpose was to test whether muscarinic and nicotinic antagonism affect aspects of cognitive flexibility, specifically verbal problem-solving, as well as memory, given acetylcholine's role in attention and arousal. Eighteen participants attended three testing sessions. Two hours before testing, participants received either 0.6 mg scopolamine, 10 mg mecamylamine, or placebo. Then, participants were tested on three memory tasks (Buschke Selective Reminding Test [BSRT], California Verbal Learning Test [CVLT], Rey Complex Figure Test), two verbal problem-solving/cognitive flexibility tasks (Compound Remote Associates Test, a timed anagram test), and a spatial inductive reasoning task (Raven's Progressive Matrices). Task order and drug order were counterbalanced. Memory impairment was seen on one BSRT measure and multiple CVLT measures with scopolamine, and with one BSRT measure with mecamylamine. There were no effects of either drug on any of the tasks involving cognitive flexibility, including verbal problem-solving. Specific memory impairments were detected using muscarinic, and to a marginal extent, nicotinic antagonists, as expected, but no effect was seen on cognitive flexibility. Therefore, although both the noradrenergic and cholinergic systems play important roles in arousal and cortical signal-to-noise processing, the cholinergic system does not appear to have the same effect as the noradrenergic system on cognitive flexibility, including verbal problem-solving.

Diverse Spatiotemporal Scales of Cholinergic Signaling in the Neocortex

ACh is a signaling molecule in the mammalian CNS, with well-documented influence over cognition and behavior. However, the nature of cholinergic signaling in the brain remains controversial, with ongoing debates focused on the spatial and temporal resolution of ACh activity. Generally, opposing views have embraced a dichotomy between transmission as slow and volume-mediated versus fast and synaptic. Here, we provide the perspective that ACh, like most other neurotransmitters, exhibits both fast and slow modes that are strongly determined by the anatomy of cholinergic fibers, the distribution and the signaling mechanisms of receptor subtypes, and the dynamics of ACh hydrolysis. Current methodological approaches remain limited in their ability to provide detailed analyses of these underlying factors. However, we believe that the continued development of novel technologies in combination with a more nuanced view of cholinergic activity will open critical new avenues to a better understanding of ACh in the brain.Dual Perspectives Companion Paper: Forebrain Cholinergic Signaling: Wired and Phasic, Not Tonic, and Causing Behavior, by Martin Sarter and Cindy Lustig.

Priming of GABAergic Long-term Potentiation by Muscarinic Receptors

Growing evidence indicates that GABAergic interneurons play a pivotal role to generate brain oscillation patterns, which are fundamental for the mnemonic processing of the hippocampus. While acetylcholine (ACh) is a powerful modulator of synaptic plasticity and brain function, few studies have been focused on the role of cholinergic signaling in the regulation of GABAergic inhibitory synaptic plasticity. We have previously shown that co-activation of endocannabinoids (CB1R) and muscarinic receptor (mAChR) in hippocampal interneurons can induce activity-dependent GABAergic long-term depression in CA1 pyramidal neurons. Here, using electrophysiological and pharmacological approaches in acute rat hippocampal slices, we show that activation of cholinergic receptors followed by either high-frequency stimulation of Schaeffer collaterals or exogenous activation of metabotropic glutamate receptor (mGluR) induces a robust long-term potentiation at GABAergic synapses (iLTP). These forms of iLTP are blocked by the M1 type of mAChR (MR1) or by the group I of mGluR (mGluR1/5) antagonists. These results suggest the existence of spatiotemporal cooperativity between cholinergic and glutamatergic pathways where activation of mAChR serves as a metaplastic switch making glutamatergic synapses capable to induce long-term potentiation at inhibitory synapses, that may contribute to the modulation of brain mechanisms of learning and memory.

Mnemonic Similarity Task: A Tool for Assessing Hippocampal Integrity

The hippocampus is critical for learning and memory, relying in part on pattern separation processes supported by the dentate gyrus (DG) to prevent interference from overlapping memory representations. In 2007, we designed the Mnemonic Similarity Task (MST), a modified object recognition memory task, to be highly sensitive to hippocampal function by placing strong demands on pattern separation. The MST is now a widely used behavioral task, repeatedly shown to be sensitive to age-related memory decline, hippocampal connectivity, and hippocampal function, with specificity to the DG. Here, we review the utility of the MST, its relationship to hippocampal function, its utility in detecting hippocampal-based memory alterations across the lifespan, and impairments associated with clinical pathology from a variety of disorders.

Indicators and Criteria of Consciousness in Animals and Intelligent Machines: An Inside-Out Approach

In today's society, it becomes increasingly important to assess which non-human and non-verbal beings possess consciousness. This review article aims to delineate criteria for consciousness especially in animals, while also taking into account intelligent artifacts. First, we circumscribe what we mean with "consciousness" and describe key features of subjective experience: qualitative richness, situatedness, intentionality and interpretation, integration and the combination of dynamic and stabilizing properties. We argue that consciousness has a biological function, which is to present the subject with a multimodal, situational survey of the surrounding world and body, subserving complex decision-making and goal-directed behavior. This survey reflects the brain's capacity for internal modeling of external events underlying changes in sensory state. Next, we follow an inside-out approach: how can the features of conscious experience, correlating to mechanisms inside the brain, be logically coupled to externally observable ("outside") properties? Instead of proposing criteria that would each define a "hard" threshold for consciousness, we outline six indicators: (i) goal-directed behavior and model-based learning; (ii) anatomic and physiological substrates for generating integrative multimodal representations; (iii) psychometrics and meta-cognition; (iv) episodic memory; (v) susceptibility to illusions and multistable perception; and (vi) specific visuospatial behaviors. Rather than emphasizing a particular indicator as being decisive, we propose that the consistency amongst these indicators can serve to assess consciousness in particular species. The integration of scores on the various indicators yields an overall, graded criterion for consciousness, somewhat comparable to the Glasgow Coma Scale for unresponsive patients. When considering theoretically derived measures of consciousness, it is argued that their validity should not be assessed on the basis of a single quantifiable measure, but requires cross-examination across multiple pieces of evidence, including the indicators proposed here. Current intelligent machines, including deep learning neural networks (DLNNs) and agile robots, are not indicated to be conscious yet. Instead of assessing machine consciousness by a brief Turing-type of test, evidence for it may gradually accumulate when we study machines ethologically and across time, considering multiple behaviors that require flexibility, improvisation, spontaneous problem-solving and the situational conspectus typically associated with conscious experience.

Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer's Disease-Related Pathology: Computer Modeling and Simulations

The aim of this study was to evaluate the possibility of the gamma oscillation function (40–130 Hz) to reduce Alzheimer’s disease related pathology in a computer model of the hippocampal network dentate gyrus, CA3, and CA1 (DG-CA3-CA1) regions. Methods: Computer simulations were made for a pathological model in which Alzheimer’s disease was simulated by synaptic degradation in the hippocampus. Pathology modeling was based on sequentially turning off the connections with entorhinal cortex layer 2 (EC2) and the dentate gyrus on CA3 pyramidal neurons. Gamma induction modeling consisted of simulating the oscillation provided by the septo-hippocampal pathway with band frequencies from 40–130 Hz. Pathological models with and without gamma induction were compared with a control. Results: In the hippocampal regions of DG, CA3, and CA1, and jointly DG-CA3-CA1 and CA3-CA1, gamma induction resulted in a statistically significant improvement in terms of increased numbers of spikes, spikes per burst, and burst duration as compared with the model simulating Alzheimer’s disease (AD). The positive maximal Lyapunov exponent was negative in both the control model and the one with gamma induction as opposed to the pathological model where it was positive within the DG-CA3-CA1 region. Gamma induction resulted in decreased transfer entropy in accordance with the information flow in DG → CA3 and CA3 → CA1. Conclusions: The results of simulation studies show that inducing gamma oscillations in the hippocampus may reduce Alzheimer’s disease related pathology. Pathologically higher transfer entropy values after gamma induction returned to values comparable to the control model.

Criticality between Cortical States

Since the first measurements of neuronal avalanches, the critical brain hypothesis has gained traction. However, if the brain is critical, what is the phase transition? For several decades, it has been known that the cerebral cortex operates in a diversity of regimes, ranging from highly synchronous states (with higher spiking variability) to desynchronized states (with lower spiking variability). Here, using both new and publicly available data, we test independent signatures of criticality and show that a phase transition occurs in an intermediate value of spiking variability, in both anesthetized and freely moving animals. The critical exponents point to a universality class different from mean-field directed percolation. Importantly, as the cortex hovers around this critical point, the avalanche exponents follow a linear relation that encompasses previous experimental results from different setups and is reproduced by a model.

Computer Modeling of Alzheimer's Disease-Simulations of Synaptic Plasticity and Memory in the CA3-CA1 Hippocampal Formation Microcircuit

This paper aims to present computer modeling of synaptic plasticity and memory in the CA3-CA1 hippocampal formation microcircuit. The computer simulations showed a comparison of a pathological model in which Alzheimer's disease (AD) was simulated by synaptic degradation in the hippocampus and control model (healthy) of CA3-CA1 networks with modification of weights for the memory. There were statistically higher spike values of both CA1 and CA3 pyramidal cells in the control model than in the pathological model (p = 0.0042 for CA1 and p = 0.0033 for CA3). A similar outcome was achieved for frequency (p = 0.0002 for CA1 and p = 0.0001 for CA3). The entropy of pyramidal cells of the healthy CA3 network seemed to be significantly higher than that of AD (p = 0.0304). We need to study a lot of physiological parameters and their combinations of the CA3-CA1 hippocampal formation microcircuit to understand AD. High statistically correlations were obtained between memory, spikes and synaptic deletion in both CA1 and CA3 cells.

Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types

GABAergic interneurons play an important role in shaping the activity of excitatory pyramidal cells (PCs). How the various inhibitory cell types contribute to neuronal information processing, however, is not resolved. Here, we propose a functional role for a widespread network motif consisting of parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide (VIP)-expressing interneurons. Following the idea that PV and SOM interneurons control the distribution of somatic and dendritic inhibition onto PCs, we suggest that mutual inhibition between VIP and SOM cells translates weak inputs to VIP interneurons into large changes of somato-dendritic inhibition of PCs. Using a computational model, we show that the neuronal and synaptic properties of the circuit support this hypothesis. Moreover, we demonstrate that the SOM-VIP motif allows transient inputs to persistently switch the circuit between two processing modes, in which top-down inputs onto apical dendrites of PCs are either integrated or cancelled.

Neurons in the brain can be classified as excitatory or inhibitory based on whether they activate or deactivate the cells to whom they send signals. Compared to their excitatory counterpart, inhibitory neurons present themselves as a wild diversity of cell classes. It is broadly believed that these classes serve different purposes, but as of now, those are poorly understood. In this article, we suggest how an intricate interplay of three inhibitory cell classes can control whether internal signals—such as predictions, memory signals or motor commands—are taken into account when sensory signals are interpreted. Using a mathematical model and computer simulations, we show that such internal signals can be shut down by regulating which inhibitory cell types are active, and that the interaction of different cell classes allows weak control signals to do so.

Biomarker Predictors of Delirium in Acutely Ill Patients: A Systematic Review

Delirium is a serious and common disorder that affects up to 80% of acutely ill patients, mainly the aged. In recent years, several studies pointed out possible biomarkers that could be used alone or in combination with other resources in the diagnosis and follow-up of critically ill patients who develop delirium. In this context, a systematic review was conducted to determine the predictive value of several biomarkers in acutely (critically and noncritically) ill adult patients with delirium. Studies that used the confusion assessment method (CAM) and CAM-intensive care unit as the diagnostic method were considered. The most recent search was performed in November 2017. There was no language restriction. Initially, 626 articles were screened and 39 were included in the study. A comprehensive evaluation of the abstracts resulted in the exclusion of 202 studies, leaving 39 articles as potentially relevant. Inflammatory markers, S100β and cortisol, could predict delirium occurrence in a specific subgroup population of critically ill patients.

Computer Model of Synapse Loss During an Alzheimer's Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1-The Way to Chaos and Information Transfer

The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.

Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits

Experimental studies have begun revealing essential properties of the structural connectivity and the spatiotemporal activity dynamics of cortical circuits. To integrate these properties from anatomy and physiology, and to elucidate the links between them, we develop a novel cortical circuit model that captures a range of realistic features of synaptic connectivity. We show that the model accounts for the emergence of higher-order connectivity structures, including highly connected hub neurons that form an interconnected rich-club. The circuit model exhibits a rich repertoire of dynamical activity states, ranging from asynchronous to localized and global propagating wave states. We find that around the transition between asynchronous and localized propagating wave states, our model quantitatively reproduces a variety of major empirical findings regarding neural spatiotemporal dynamics, which otherwise remain disjointed in existing studies. These dynamics include diverse coupling (correlation) between spiking activity of individual neurons and the population, dynamical wave patterns with variable speeds and precise temporal structures of neural spikes. We further illustrate how these neural dynamics are related to the connectivity properties by analysing structural contributions to variable spiking dynamics and by showing that the rich-club structure is related to the diverse population coupling. These findings establish an integrated account of structural connectivity and activity dynamics of local cortical circuits, and provide new insights into understanding their working mechanisms.

To integrate essential anatomical and physiological properties of local cortical circuits and to elucidate mechanistic links between them, we develop a novel circuit model capturing key synaptic connectivity features. We show that the model explains the emergence of a range of connectivity patterns such as rich-club connectivity, and gives rise to a rich repertoire of cortical states. We identify both the anatomical and physiological mechanisms underlying the transition of these cortical states, and show that our model reconciles an otherwise disparate set of key physiological findings on neural activity dynamics. We further illustrate how these neural dynamics are related to the connectivity properties by analysing structural contributions to variable spiking dynamics and by showing that the rich-club structure is related to diverse neural population correlations as observed recently. Our model thus provides a framework for integrating and explaining a variety of neural connectivity properties and spatiotemporal activity dynamics observed in experimental studies, and provides novel experimentally testable predictions.

Targeted Vagus Nerve Stimulation for Rehabilitation After Stroke

Stroke is a leading cause of disability worldwide, and in approximately 60% of individuals, upper limb deficits persist 6 months after stroke. These deficits adversely affect the functional use of the upper limb and restrict participation in day to day activities. An important goal of stroke rehabilitation is to improve the quality of life by enhancing functional independence and participation in activities. Since upper limb deficits are one of the best predictors of quality of life after stroke, effective interventions targeting these deficits may represent a means to improve quality of life. An increased understanding of the neurobiological processes underlying stroke recovery has led to the development of targeted approaches to improve motor deficits. One such targeted strategy uses brief bursts of Vagus Nerve Stimulation (VNS) paired with rehabilitation to enhance plasticity and support recovery of upper limb function after chronic stroke. Stimulation of the vagus nerve triggers release of plasticity promoting neuromodulators, such as acetylcholine and norepinephrine, throughout the cortex. Timed engagement of neuromodulators concurrent with motor training drives task-specific plasticity in the motor cortex to improve function and provides the basis for paired VNS therapy. A number of studies in preclinical models of ischemic stroke demonstrated that VNS paired with rehabilitative training significantly improved the recovery of forelimb motor function compared to rehabilitative training without VNS. The improvements were associated with synaptic reorganization of cortical motor networks and recruitment of residual motor neurons controlling the impaired forelimb, demonstrating the putative neurobiological mechanisms underlying recovery of motor function. These preclinical studies provided the basis for conducting two multi-site, randomized controlled pilot trials in individuals with moderate to severe upper limb weakness after chronic ischemic stroke. In both studies, VNS paired with rehabilitation improved motor deficits compared to rehabilitation alone. The trials provided support for a 120-patient pivotal study designed to evaluate the efficacy of paired VNS therapy in individuals with chronic ischemic stroke. This manuscript will discuss the neurobiological rationale for VNS therapy, provide an in-depth discussion of both animal and human studies of VNS therapy for stroke, and outline the challenges and opportunities for the future use of VNS therapy.

Specific Basal Forebrain-Cortical Cholinergic Circuits Coordinate Cognitive Operations

Based on recent molecular genetics, as well as functional and quantitative anatomical studies, the basal forebrain (BF) cholinergic projections, once viewed as a diffuse system, are emerging as being remarkably specific in connectivity. Acetylcholine (ACh) can rapidly and selectively modulate activity of specific circuits and ACh release can be coordinated in multiple areas that are related to particular aspects of cognitive processing. This review discusses how a combination of multiple new approaches with more established techniques are being used to finally reveal how cholinergic neurons, together with other BF neurons, provide temporal structure for behavior, contribute to local cortical state regulation, and coordinate activity between different functionally related cortical circuits. ACh selectively modulates dynamics for encoding and attention within individual cortical circuits, allows for important transitions during sleep, and shapes the fidelity of sensory processing by changing the correlation structure of neural firing. The importance of this system for integrated and fluid behavioral function is underscored by its disease-modifying role; the demise of BF cholinergic neurons has long been established in Alzheimer's disease and recent studies have revealed the involvement of the cholinergic system in modulation of anxiety-related circuits. Therefore, the BF cholinergic system plays a pivotal role in modulating the dynamics of the brain during sleep and behavior, as foretold by the intricacies of its anatomical map.

Activity changes in neuron-astrocyte networks in culture under the effect of norepinephrine

The concerted activity of neuron-glia networks is responsible for the fascinating dynamics of brain functions. Although these networks have been extensively investigated using a variety of experimental (in vivo and in vitro) and theoretical models, the manner by which neuron-glia networks interact is not fully understood. In particular, how neuromodulators influence network-level signaling between neurons and astrocytes was poorly addressed. In this work, we investigated global effects of the neuromodulator norepinephrine (NE) on neuron-astrocyte network communication in co-cultures of neurons and astrocytes and in isolated astrocyte networks. Electrical stimulation was used to activate the neuron-astrocyte glutamate-mediated pathway. Our results showed dramatic changes in network activity under applied global perturbations. Under neuromodulation, there was a marked rise in calcium signaling in astrocytes, neuronal spontaneous activity was reduced, and the communication between neuron-astrocyte networks was perturbed. Moreover, in the presence of NE, we observed two astrocyte behaviors based on their coupling to neurons. There were also morphological changes in astrocytes upon application of NE, suggesting a physical cause underlies the change in signaling. Our results shed light on the role of NE in controlling sleep-wake cycles.

Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity

Neuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network activity. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the physiology of cholinergic modulation of neocortical cells and synapses. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to investigate the effects of levels of ACh on diverse neuron types and synapses, and consequently on emergent network activity. Preliminary simulations from the framework, which was not tuned to reproduce any specific ACh-induced network effects, not only corroborate the long-standing notion that ACh desynchronizes spontaneous network activity, but also predict that a dose-dependent activation of ACh gives rise to a spectrum of neocortical network activity. We show that low levels of ACh, such as during non-rapid eye movement (nREM) sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as during wakefulness and REM sleep, govern fast oscillations and network asynchrony. In addition, spontaneous network activity modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the regulation of neurons and synapses caused by increasing levels of ACh, which enhances cellular excitability and decreases the efficacy of local synaptic transmission. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulators in neural information processing.

Evolution in Neuromodulation-The Differential Roles of Acetylcholine in Higher Order Association vs. Primary Visual Cortices

This review contrasts the neuromodulatory influences of acetylcholine (ACh) on the relatively conserved primary visual cortex (V1), compared to the newly evolved dorsolateral prefrontal association cortex (dlPFC). ACh is critical both for proper circuit development and organization, and for optimal functioning of mature systems in both cortical regions. ACh acts through both nicotinic and muscarinic receptors, which show very different expression profiles in V1 vs. dlPFC, and differing effects on neuronal firing. Cholinergic effects mediate attentional influences in V1, enhancing representation of incoming sensory stimuli. In dlPFC ACh plays a permissive role for network communication. ACh receptor expression and ACh actions in higher visual areas have an intermediate profile between V1 and dlPFC. This changing role of ACh modulation across association cortices may help to illuminate the particular susceptibility of PFC in cognitive disorders, and provide therapeutic targets to strengthen cognition.

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) employ simulated evolution in-silico to breed plastic neural networks with the aim to autonomously design and create learning systems. EPANN experiments evolve networks that include both innate properties and the ability to change and learn in response to experiences in different environments and problem domains. EPANNs' aims include autonomously creating learning systems, bootstrapping learning from scratch, recovering performance in unseen conditions, testing the computational advantages of particular neural components, and deriving hypotheses on the emergence of biological learning. Thus, EPANNs may include a large variety of different neuron types and dynamics, network architectures, plasticity rules, and other factors. While EPANNs have seen considerable progress over the last two decades, current scientific and technological advances in artificial neural networks are setting the conditions for radically new approaches and results. Exploiting the increased availability of computational resources and of simulation environments, the often challenging task of hand-designing learning neural networks could be replaced by more autonomous and creative processes. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and possible developments are presented.

Noradrenergic and Cholinergic Modulation of Belief Updating

To make optimal predictions in a dynamic environment, the impact of new observations on existing beliefs-that is, the learning rate-should be guided by ongoing estimates of change and uncertainty. Theoretical work has proposed specific computational roles for various neuromodulatory systems in the control of learning rate, but empirical evidence is still sparse. The aim of the current research was to examine the role of the noradrenergic and cholinergic systems in learning rate regulation. First, we replicated our recent findings that the centroparietal P3 component of the EEG-an index of phasic catecholamine release in the cortex-predicts trial-to-trial variability in learning rate and mediates the effects of surprise and belief uncertainty on learning rate (Study 1, n = 17). Second, we found that pharmacological suppression of either norepinephrine or acetylcholine activity produced baseline-dependent effects on learning rate following nonobvious changes in an outcome-generating process (Study 1). Third, we identified two genes, coding for α2A receptor sensitivity (ADRA2A) and norepinephrine reuptake (NET), as promising targets for future research on the genetic basis of individual differences in learning rate (Study 2, n = 137). Our findings suggest a role for the noradrenergic and cholinergic systems in belief updating and underline the importance of studying interactions between different neuromodulatory systems.

Vocal learning in songbirds requires cholinergic signaling in a motor cortex-like nucleus

Cholinergic inputs to cortex modulate plasticity and sensory processing, yet little is known about their role in motor control. Here, we show that cholinergic signaling in a songbird vocal motor cortical area, the robust nucleus of the arcopallium (RA), is required for song learning. Reverse microdialysis of nicotinic and muscarinic receptor antagonists into RA in juvenile birds did not significantly affect syllable timing or acoustic structure during vocal babbling. However, chronic blockade over weeks reduced singing quantity and impaired learning, resulting in an impoverished song with excess variability, abnormal acoustic features, and reduced similarity to tutor song. The demonstration that cholinergic signaling in a motor cortical area is required for song learning motivates the songbird as a tractable model system to identify roles of the basal forebrain cholinergic system in motor control. NEW & NOTEWORTHY Cholinergic inputs to cortex are evolutionarily conserved and implicated in sensory processing and synaptic plasticity. However, functions of cholinergic signals in motor areas are understudied and poorly understood. Here, we show that cholinergic signaling in a songbird vocal motor cortical area is not required for normal vocal variability during babbling but is essential for developmental song learning. Cholinergic modulation of motor cortex is thus required for learning but not for the ability to sing.

Function of Selective Neuromodulatory Projections in the Mammalian Cerebral Cortex: Comparison Between Cholinergic and Noradrenergic Systems

Cortical processing is dynamically modulated by different neuromodulators. Neuromodulation of the cerebral cortex is crucial for maintaining cognitive brain functions such as perception, attention and learning. However, we do not fully understand how neuromodulatory projections are organized in the cerebral cortex to exert various functions. The basal forebrain (BF) cholinergic projection and the locus coeruleus (LC) noradrenergic projection are well-known neuromodulatory projections to the cortex. Decades of studies have identified anatomical and physiological characteristics of these circuits. While both cholinergic and noradrenergic neurons widely project to the cortex, they exhibit different levels of selectivity. Here, we summarize their anatomical and physiological features, highlighting selectivity and specificity of these circuits to different cortical regions. We discuss the importance of selective modulation by comparing their functions in the cortex. We highlight key features in the input-output circuits and target selectivity of these neuromodulatory projections and their roles in controlling four major brain functions: attention, reinforcement, learning and memory, sleep and wakefulness.