Neuronal dendrites are electrically excitable: they can generate regenerative events such as dendritic spikes in response to sufficiently strong synaptic input. Although such events have been observed in many neuronal types, it is not well understood how active dendrites contribute to the tuning of neuronal output in vivo. Here we show that dendritic spikes increase the selectivity of neuronal responses to the orientation of a visual stimulus (orientation tuning). We performed direct patch-clamp recordings from the dendrites of pyramidal neurons in the primary visual cortex of lightly anaesthetized and awake mice, during sensory processing. Visual stimulation triggered regenerative local dendritic spikes that were distinct from back-propagating action potentials. These events were orientation tuned and were suppressed by either hyperpolarization of membrane potential or intracellular blockade of NMDA (N-methyl-d-aspartate) receptors. Both of these manipulations also decreased the selectivity of subthreshold orientation tuning measured at the soma, thus linking dendritic regenerative events to somatic orientation tuning. Together, our results suggest that dendritic spikes that are triggered by visual input contribute to a fundamental cortical computation: enhancing orientation selectivity in the visual cortex. Thus, dendritic excitability is an essential component of behaviourally relevant computations in neurons.

The detection and discrimination of temporal sequences is fundamental to brain function and underlies perception, cognition, and motor output. By applying patterned, two-photon glutamate uncaging, we found that single dendrites of cortical pyramidal neurons exhibit sensitivity to the sequence of synaptic activation. This sensitivity is encoded by both local dendritic calcium signals and somatic depolarization, leading to sequence-selective spike output. The mechanism involves dendritic impedance gradients and nonlinear synaptic N-methyl-D-aspartate receptor activation and is generalizable to dendrites in different neuronal types. This enables discrimination of patterns delivered to a single dendrite, as well as patterns distributed randomly across the dendritic tree. Pyramidal cell dendrites can thus act as processing compartments for the detection of synaptic sequences, thereby implementing a fundamental cortical computation.

Cortical pyramidal neurons receive thousands of synaptic inputs arriving at different dendritic locations with varying degrees of temporal synchrony. It is not known if different locations along single cortical dendrites integrate excitatory inputs in different ways. Here we have used two-photon glutamate uncaging and compartmental modeling to reveal a gradient of nonlinear synaptic integration in basal and apical oblique dendrites of cortical pyramidal neurons. Excitatory inputs to the proximal dendrite sum linearly and require precise temporal coincidence for effective summation, whereas distal inputs are amplified with high gain and integrated over broader time windows. This allows distal inputs to overcome their electrotonic disadvantage, and become surprisingly more effective than proximal inputs at influencing action potential output. Thus, single dendritic branches can already exhibit nonuniform synaptic integration, with the computational strategy shifting from temporal coding to rate coding along the dendrite.

Information transfer at chemical synapses occurs when vesicles fuse with the plasma membrane and release neurotransmitter. This process is stochastic and its likelihood of occurrence is a crucial factor in the regulation of signal propagation in neuronal networks. The reliability of neurotransmitter release can be highly variable: experimental data from electrophysiological, molecular and imaging studies have demonstrated that synaptic terminals can individually set their neurotransmitter release probability dynamically through local feedback regulation. This local tuning of transmission has important implications for current models of single-neuron computation.

Escaping from imminent danger is an instinctive behaviour that is fundamental for survival, and requires the classification of sensory stimuli as harmless or threatening. The absence of threat enables animals to forage for essential resources, but as the level of threat and potential for harm increases, they have to decide whether or not to seek safety . Despite previous work on instinctive defensive behaviours in rodents, little is known about how the brain computes the threat level for initiating  escape. Here we show that the probability and vigour of escape in mice scale with the saliency of innate threats, and are well described by a model that computes the distance between the threat level and an escape threshold. Calcium imaging and optogenetics in the midbrain of freely behaving mice show that the activity of excitatory neurons in the deep layers of the medial superior colliculus (mSC) represents the saliency of the threat stimulus and is predictive of escape, whereas glutamatergic neurons of the dorsal periaqueductal grey (dPAG) encode exclusively the choice to escape and control escape vigour. We demonstrate a feed-forward monosynaptic excitatory connection from mSC to dPAG neurons, which is weak and unreliable-yet required for escape behaviour-and provides a synaptic threshold for dPAG activation and the initiation of escape. This threshold can be overcome by high mSC network activity because of short-term synaptic facilitation and recurrent excitation within the mSC, which amplifies and sustains synaptic drive to the dPAG. Therefore, dPAG glutamatergic neurons compute escape decisions and escape vigour using a synaptic mechanism to  threshold threat information received from the mSC, and provide a biophysical model of how the brain performs a critical behavioural computation.

No abstract

The arrival of an action potential at a synapse triggers neurotransmitter release with a limited probability, p(r). Although p(r) is a fundamental parameter in defining synaptic efficacy, it is not uniform across all synapses, and the mechanisms by which a given synapse sets its basal release probability are unknown. By measuring p(r) at single presynaptic terminals in connected pairs of hippocampal neurons, we show that neighboring synapses on the same dendritic branch have very similar release probabilities, and p(r) is negatively correlated with the number of synapses on the branch. Increasing dendritic depolarization elicits a homeostatic decrease in p(r), and equalizing activity in the dendrite significantly reduces its variability. Our results indicate that local dendritic activity is the major determinant of basal release probability, and we suggest that this feedback regulation might be required to maintain synapses in their operational range.

Molecular and cellular processes in neurons are critical for sensing and responding to energy deficit states, such as during weight-loss. Agouti related protein (AGRP)-expressing neurons are a key hypothalamic population that is activated during energy deficit and increases appetite and weight-gain. Cell type-specific transcriptomics can be used to identify pathways that counteract weight-loss, and here we report high-quality gene expression profiles of AGRP neurons from well-fed and food-deprived young adult mice. For comparison, we also analyzed Proopiomelanocortin (POMC)-expressing neurons, an intermingled population that suppresses appetite and body weight. We find that AGRP neurons are considerably more sensitive to energy deficit than POMC neurons. Furthermore, we identify cell type-specific pathways involving endoplasmic reticulum-stress, circadian signaling, ion channels, neuropeptides, and receptors. Combined with methods to validate and manipulate these pathways, this resource greatly expands molecular insight into neuronal regulation of body weight, and may be useful for devising therapeutic strategies for obesity and eating disorders.DOI: http://dx.doi.org/10.7554/eLife.09800.001