Topic: neuromodulators

Andrew Huberman explains how key neuromodulators shape motivation, energy, focus and mood, with science-supported behavioral, nutritional and supplement strategies.

Retinal ganglion cell (RGC) types relay parallel streams of visual feature information. We hypothesized that neuromodulators might efficiently control which visual information streams reach the cortex, by selectively gating transmission from specific RGC axons in the thalamus. Using fiber photometry recordings, we found that optogenetic stimulation of serotonergic axons in primary visual thalamus of awake mice suppressed ongoing and visually evoked calcium activity and glutamate release from RGC boutons. Two-photon calcium imaging revealed that serotonin axon stimulation suppressed RGC boutons

With the rapid growth of optical imaging technologies, the simultaneous observation of brain-wide activity and behavior in small organisms offers a unique opportunity to study how distributed neuronal networks interact to process sensory information and generate behavior. While neuromodulators have been shown to grant flexible state-switching mechanisms to circuits, their influence on global network dynamics is poorly characterized. Using resonant-scanning two-photon microscopy, we measured brain-wide calcium dynamics in head-restrained larval zebrafish while monitoring tail movements to inves

Brain information processing is shaped by fluctuations in neuronal rhythmic activities, each defining distinctive brain states. Switches in brain states during wake-sleep cycle are described at the network level, by a neuronal population shift from active to oscillatory state. At the cellular level, neurons switch from tonic to burst. This switch is organized thanks to neuromodulators. They refer to signaling molecules that induce reversible changes in functional properties of neurons or synapses. Simultaneously, learning and memory are attributed to the ability of neurons to modify their conn

Neuromodulators adjust sensory processing and synapses are key control sites for such plasticity. Less clear is how neuromodulation alters the amount of information that synapses transmit through a circuit. To understand the operation of the “vesicle code” the experimenter needs to isolate signals from individual active zones with a resolution of single vesicles while observing or controlling the incoming signal. This has not been possible using electrophysiology but can now be achieved by multiphoton imaging of the glutamate reporter iGluSnFR in the retina of larval zebrafish. This approach

To survive in the wild small rodents evolved specialized retinas. To escape predators, looming shadows need to be detected with speed and precision. To evade starvation, small seeds, grass, nuts and insects need to also be detected quickly. Some of these succulent seeds and insects may be camouflaged offering only low contrast targets.Moreover, these challenging tasks need to be accomplished continuously at dusk, night, dawn and daytime. Crossover inhibition is thought to be involved in enhancing contrast detectionin the microcircuits of the inner plexiform layer of the mammalian retina. The A

Neuromodulators control information processing in cortical microcircuits by regulating the cellular and synaptic physiology of neurons. Computational models and detailed simulations of neocortical microcircuitry offer a unifying framework to analyze the role of neuromodulators on network activity. In the present study, to get a deeper insight in the organization of the cortical neuropil for modeling purposes, we quantify the fiber length per cortical volume and the density of varicosities for catecholaminergic, serotonergic and cholinergic systems using immunocytochemical staining and stereolo

Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We the

Behavioral states such as arousal and attention can have profound effects on sensory processing, determining how – sometimes whether – a stimulus is processed. This state-dependence is believed to arise, at least in part, as a result of inputs to cortex from subcortical structures that release neuromodulators such as acetylcholine, noradrenaline, and serotonin, often non-synaptically. The mechanisms that underlie the interaction between these “wireless” non-synaptic signals and the “wired” cortical circuit are not well understood. Furthermore, neuromodulatory signaling is traditionally conside

Recent progress in artificial intelligence (AI), particularly through the development of artificial neural networks (ANNs), has significantly benefited from insights gained from neuroscience. This progress has enhanced the replication of complex cognitive tasks such as vision and natural language processing (1,2). However, ANNs still struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency - capabilities that biological systems handle seamlessly (3–8). Neuromodulators, the chemical messengers that target neurons and synapses in specific brain regions,

Across species, neurons possess numerous ion channel types, with a single cell typically expressing tens of channel genes, which translate to hundreds or even thousands of channel protein types, each with different kinetics [1]. Moreover, maximal conductance densities across individuals are also highly variable and subject to neuromodulators which are essential for enabling the nervous system to switch between behaviorally relevant modes [2,3]. This raises the question of how neuromodulators can reliably induce changes in intrinsic neuronal properties across a heterogeneous population. At th

Recurrent Neural Networks (RNNs) are a popular computational tool in neuroscience. Their architecture is inspired by biology, namely the abundance of lateral connections in cortex. Because they are universal approximators, they can be trained to match behavior in common neuroscience tasks, and their hidden state can be compared to neural recordings. Despite the biological inspiration, many features of real neurons are abstracted away (e.g. morphology, neuromodulators, excitability dynamics, short term plasticity). While many of these properties were included as add-ons to standard RNN models,

Neuromodulators such as monoamines or neuropeptides play crucial roles in modulating diverse brain functions. They can be released phasically, i.e. in response to stimuli, or tonically, in a spontaneous and more discrete manner. Understanding how these spontaneous releases occur is challenging yet essential, as their disruption is associated with behavioral and psychiatric disorders. A paradigm shift in this domain has been the generation of fluorescent probes sensitive enough to detect, using 2-photons microscopy, discrete tonic release of specific neuromodulators including dopamine, noradren

Norepinephrine (NE) is one of the main neuromodulators in the brain influencing a plethora of behavioral outputs including both autonomic and cognitive functions, such as emotional learning and stress responses, through a brain-wide network of projections emanating from the Locus Coeruleus (LC). Genetically encoded indicators engineered from G-protein-coupled receptors (GPCRs) have been proven essential tools for neuroscience enabling in vivo imaging of neuromodulators with high spatiotemporal resolution. Current state-of-the-art NE sensors allow for in vivo optical detection of NE, however, t

Neuromodulators have a key role in mediating cognitive processes such as attention, learning and memory. Disorders of the neuromodulatory systems underlie certain degenerative neurological conditions like Alzheimer’s and Parkinson’s diseases. While the role of neuromodulators in explicit learning is increasingly in the focus of the research, their participation in implicit learning and memory processes is poorly understood.To examine this we have used a previously developed automated training setup in which animals must respond to sequential light flashes at different locations. Following the

To date, our understanding of how the human brain retrieves, processes, and stores information remains limited. A specific challenge lies in unravelling how local plasticity mechanisms shape the transfer of information between neurons. Traditional approaches to studying local plasticity in vitro, such as patch clamp or calcium imaging, are often limited by low throughput or the ability to record only from a subset of neurons within intricate networks. In response to these challenges, we adopt a bottom-up approach to engineer low-density neuronal networks with defined architectures. Human induc