Dendritic discrimination of temporal input sequences in cortical neurons

Can NMDA Spikes Dictate Computations of Local Networks and Behavior?

Intelligence is the ability to learn appropriate responses to stimuli and the capacity to master new skills. Synaptic integration at the dendritic level is thought to be essential for this ability through linear and non-linear processing, by allowing neurons to be tuned to relevant information and to maximize adaptive behavior. Showing that dendrites are able to generate local computations that influence how animals perceive the world, form a new memory or learn a new skill was a break-through in neuroscience, since in the past they were seen as passive elements of the neurons, just funneling information to the soma. Here, we provide an overview of the role of dendritic integration in improving the neuronal network and behavioral performance. We focus on how NMDA spikes are generated and their role in neuronal computation for optimal behavioral output based on recent in vivo studies on rodents.

Adrenergic Modulation Regulates the Dendritic Excitability of Layer 5 Pyramidal Neurons In Vivo

The excitability of the apical tuft of layer 5 pyramidal neurons is thought to play a crucial role in behavioral performance and synaptic plasticity. We show that the excitability of the apical tuft is sensitive to adrenergic neuromodulation. Using two-photon dendritic Ca²⁺ imaging and in vivo whole-cell and extracellular recordings in awake mice, we show that application of the α2A-adrenoceptor agonist guanfacine increases the probability of dendritic Ca²⁺ events in the tuft and lowers the threshold for dendritic Ca²⁺ spikes. We further show that these effects are likely to be mediated by the dendritic current I_h. Modulation of I_h in a realistic compartmental model controlled both the generation and magnitude of dendritic calcium spikes in the apical tuft. These findings suggest that adrenergic neuromodulation may affect cognitive processes such as sensory integration, attention, and working memory by regulating the sensitivity of layer 5 pyramidal neurons to top-down inputs.

Dendritic Spikes Expand the Range of Well Tolerated Population Noise Structures

The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest in identifying noise structures that can be integrated linearly in a way that preserves reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a complementary denoising approach that is mediated by focal dendritic spikes. Dendritic spikes might seem to be unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation increases neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of conditions that can be processed by neuronal populations.SIGNIFICANCE STATEMENT Noise, or random variability, is a prominent feature of the neuronal code and poses a fundamental challenge for information processing. To reconcile the surprisingly accurate output of the brain with the inherent noisiness of biological systems, previous work examined signal integration in idealized neurons. The notion that emerged from this body of work is that accurate signal representation relies largely on input averaging in neuronal dendrites. In contrast to the prevailing view, I show that denoising in simulated neurons with realistic morphology and biophysical properties follows a different strategy: dendritic spikes act as classifiers that assist in extracting information from a variety of noise structures that have been considered before to be particularly disruptive for reliable brain function.

Functional Compartmentalization within Starburst Amacrine Cell Dendrites in the Retina

Dendrites in many neurons actively compute information. In retinal starburst amacrine cells, transformations from synaptic input to output occur within individual dendrites and mediate direction selectivity, but directional signal fidelity at individual synaptic outputs and correlated activity among neighboring outputs on starburst dendrites have not been examined systematically. Here, we record visually evoked calcium signals simultaneously at many individual synaptic outputs within single starburst amacrine cells in mouse retina. We measure visual receptive fields of individual output synapses and show that small groups of outputs are functionally compartmentalized within starburst dendrites, creating distinct computational units. Inhibition enhances compartmentalization and directional tuning of individual outputs but also decreases the signal-to-noise ratio. Simulations suggest, however, that the noise underlying output signal variability is well tolerated by postsynaptic direction-selective ganglion cells, which integrate convergent inputs to acquire reliable directional information.

Intrinsic temporal tuning of neurons in the optic tectum is shaped by multisensory experience

For a biological neural network to be functional, its neurons need to be connected with synapses of appropriate strength, and each neuron needs to appropriately respond to its synaptic inputs. This second aspect of network tuning is maintained by intrinsic plasticity; yet it is often considered secondary to changes in connectivity and mostly limited to adjustments of overall excitability of each neuron. Here we argue that even nonoscillatory neurons can be tuned to inputs of different temporal dynamics and that they can routinely adjust this tuning to match the statistics of their synaptic activation. Using the dynamic clamp technique, we show that, in the tectum of Xenopus tadpole, neurons become selective for faster inputs when animals are exposed to fast visual stimuli but remain responsive to longer inputs in animals exposed to slower, looming, or multisensory stimulation. We also report a homeostatic cotuning between synaptic and intrinsic temporal properties of individual tectal cells. These results expand our understanding of intrinsic plasticity in the brain and suggest that there may exist an additional dimension of network tuning that has been so far overlooked.NEW & NOTEWORTHY We use dynamic clamp to show that individual neurons in the tectum of Xenopus tadpoles are selectively tuned to either shorter (more synchronous) or longer (less synchronous) synaptic inputs. We also demonstrate that this intrinsic temporal tuning is strongly shaped by sensory experiences. This new phenomenon, which is likely to be mediated by changes in sodium channel inactivation, is bound to have important consequences for signal processing and the development of local recurrent connections.

Global and Multiplexed Dendritic Computations under In Vivo-like Conditions

Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions.

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences

From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.

Dendritic computations captured by an effective point neuron model

Modeling single-neuron dynamics is the first step to quantitatively understand brain computation. Yet, the existing point neuron models fail to capture dendritic effects, which are crucial for neuronal information processing. We derive an effective point neuron model, which incorporates an additional synaptic integration current arising from the nonlinear interaction between synaptic currents across spatial dendrites. Our model captures the somatic voltage response of a neuron with complex dendrites and is capable of performing rich dendritic computations. Besides its computational efficiency in simulations, our model suggests reexamination of previous studies involving the decomposition of excitatory and inhibitory synaptic inputs based on the existing point neuron framework, e.g., the inhibition is often underestimated in experiment.

Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework.

Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve discrimination of input synchrony

How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing. Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances g_H and g_M, mediated respectively by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and by muscarine-sensitive M-type K⁺ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration.NEW & NOTEWORTHY Neuronal filtering and integration of synaptic input patterns depend on the electrochemical properties of dendrites. We used an identified collision detection neuron in grasshoppers to examine how its morphology and two conductances affect its membrane impedance in relation to the computations it performs. The neuronal properties examined are ubiquitous and therefore promote a general understanding of neuronal computations, including those in the human brain.

Dendritic spines: Revisiting the physiological role

Dendritic spines are small, thin, specialized protrusions from neuronal dendrites, primarily localized in the excitatory synapses. Sophisticated imaging techniques revealed that dendritic spines are complex structures consisting of a dense network of cytoskeletal, transmembrane and scaffolding molecules, and numerous surface receptors. Molecular signaling pathways, mainly Rho and Ras family small GTPases pathways that converge on actin cytoskeleton, regulate the spine morphology and dynamics bi-directionally during synaptic activity. During synaptic plasticity the number and shapes of dendritic spines undergo radical reorganizations. Long-term potentiation (LTP) induction promote spine head enlargement and the formation and stabilization of new spines. Long-term depression (LTD) results in their shrinkage and retraction. Reports indicate increased spine density in the pyramidal neurons of autism and Fragile X syndrome patients and reduced density in the temporal gyrus loci of schizophrenic patients. Post-mortem reports of Alzheimer's brains showed reduced spine number in the hippocampus and cortex. This review highlights the spine morphogenesis process, the activity-dependent structural plasticity and mechanisms by which synaptic activity sculpts the dendritic spines, the structural and functional changes in spines during learning and memory using LTP and LTD processes. It also discusses on spine status in neurodegenerative diseases and the impact of nootropics and neuroprotective agents on the functional restoration of dendritic spines.

Brpf1 Haploinsufficiency Impairs Dendritic Arborization and Spine Formation, Leading to Cognitive Deficits

Haploinsufficiency of the bromodomain and PHD finger-containing protein 1 (BRPF1) gene causes intellectual disability (ID), which is characterized by impaired intellectual and cognitive function; however, the neurological basis for ID and the neurological function of BRPF1 dosage in the brain remain unclear. Here, by crossing Emx1-cre mice with Brpf1^fl/fl mice, we generated Brpf1 heterozygous mice to model BRPF1-related ID. Brpf1 heterozygotes showed reduced dendritic complexity in both hippocampal granule cells and cortical pyramidal neurons, accompanied by reduced spine density and altered spine and synapse morphology. An in vitro study of Brpf1 haploinsufficiency also demonstrated decreased frequency and amplitude of miniature EPSCs that may subsequently contribute to abnormal behaviors, including decreased anxiety levels and defective learning and memory. Our results demonstrate a critical role for Brpf1 dosage in neuron dendrite arborization, spine morphogenesis and behavior and provide insight into the pathogenesis of BRPF1-related ID.

How Dendrites Affect Online Recognition Memory

In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. However, little is known about the computational strategies or neural mechanisms that underlie the brain's ability to perform this type of "online" learning. Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic, network, pattern, and task-related parameters. We used the model to determine what dendrite size maximizes storage capacity under varying assumptions about pattern density and noise level. We show that over a several-fold range of both of these parameters, and over multiple orders-of-magnitude of memory size, capacity is maximized when dendrites contain a few hundred synapses-roughly the natural number found in memory-related areas of the brain. Thus, in comparison to entire neurons, dendrites increase storage capacity by providing a larger number of better-sized learning units. Our model provides the first normative theory that explains how dendrites increase the brain's capacity for online learning; predicts which combinations of parameter settings we should expect to find in the brain under normal operating conditions; leads to novel interpretations of an array of existing experimental results; and provides a tool for understanding which changes associated with neurological disorders, aging, or stress are most likely to produce memory deficits-knowledge that could eventually help in the design of improved clinical treatments for memory loss.

Spatiotemporal Information Processing Emulated by Multiterminal Neuro-Transistor Networks

All external sensory stimuli produce a spatiotemporal pattern of action potentials, which is transmitted to the biological neural system to be processed. The relative timing of synaptic spikes from different presynaptic neurons represents the features of the stimuli. A fundamental prerequisite in cortical information processing is the discrimination of different spatiotemporal input sequences. Here, capacitively coupled multiterminal oxide-based neuro-transistors are proposed for spatiotemporal information processing, mimicking the dendritic discriminability of different spatiotemporal input sequences. The experimental results demonstrate that such multiterminal neuromorphic devices can act as spatiotemporal information processing compartments for fundamental cortical computation. Also, as an example of spatiotemporal information processing, sound location functionality of the human brain is also emulated by constructing a simple artificial neural network based on such oxide-based multiterminal neuro-transistors.

Tuft dendrites of pyramidal neurons operate as feedback-modulated functional subunits

Dendrites of pyramidal cells exhibit complex morphologies and contain a variety of ionic conductances, which generate non-trivial integrative properties. Basal and proximal apical dendrites have been shown to function as independent computational subunits within a two-layer feedforward processing scheme. The outputs of the subunits are linearly summed and passed through a final non-linearity. It is an open question whether this mathematical abstraction can be applied to apical tuft dendrites as well. Using a detailed compartmental model of CA1 pyramidal neurons and a novel theoretical framework based on iso-response methods, we first show that somatic sub-threshold responses to brief synaptic inputs cannot be described by a two-layer feedforward model. Then, we relax the core assumption of subunit independence and introduce non-linear feedback from the output layer to the subunit inputs. We find that additive feedback alone explains the somatic responses to synaptic inputs to most of the branches in the apical tuft. Individual dendritic branches bidirectionally modulate the thresholds of their input-output curves without significantly changing the gains. In contrast to these findings for precisely timed inputs, we show that neuronal computations based on firing rates can be accurately described by purely feedforward two-layer models. Our findings support the view that dendrites of pyramidal neurons possess non-linear analog processing capabilities that critically depend on the location of synaptic inputs. The iso-response framework proposed in this computational study is highly efficient and could be directly applied to biological neurons.

Pyramidal neurons are the principal cell type in the cerebral cortex. Revealing how these cells operate is key to understanding the dynamics and computations of cortical circuits. However, it is still a matter of debate how pyramidal neurons transform their synaptic inputs into spike outputs. Recent studies have proposed that individual dendritic branches or subtrees may function as independent computational subunits. Although experimental work consolidated this abstraction for basal and proximal apical dendrites, a rigorous test for tuft dendrites is still missing. By carrying out a computational study we demonstrate that dendritic branches in the tuft do not form independent subunits, however, their integrative properties can be captured by a model that incorporates modulatory feedback between these subunits. This conclusion has been reached using a novel theoretical framework that can be directly integrated into multi-electrode or photo-stimulation paradigms to reveal the dendritic computations of biological neurons.

Fundamentals of the modern theory of the phenomenon of "pain" from the perspective of a systematic approach. Neurophysiological basis. Part 1: A brief presentation of key subcellular and cellular ctructural elements of the central nervous system

The phenomenon of “pain” is a psychophysiological phenomenon that is actualized in the mind of a person as a result of the systemic response of his body to certain external and internal stimuli. The heart of the corresponding mental processes is certain neurophysiological processes, which in turn are caused by a certain form of the systemic structural and functional organization of the central nervous system (CNS). Thus, the systemic structural and functional organization of the central nervous system of a person, determining the corresponding psychophysiological state in a specific time interval, determines its psycho-emotional states or reactions manifested by the pain phenomenon. The nervous system of the human body has a hierarchical structure and is a morphologically and functionally complete set of different, interconnected, nervous and structural formations. The basis of the structural formations of the nervous system is nervous tissue. It is a system of interconnected differentials of nerve cells, neuroglia and glial macrophages, providing specific functions of perception of stimulation, excitation, generation of nerve impulses and its transmission. The neuron and each of its compartments (spines, dendrites, catfish, axon) is an autonomous, plastic, active, structural formation with complex computational properties. One of them – dendrites – plays a key role in the integration and processing of information. Dendrites, due to their morphology, provide neurons with unique electrical and plastic properties and cause variations in their computational properties. The morphology of dendrites: 1) determines – a) the number and type of contacts that a particular neuron can form with other neurons; b) the complexity, diversity of its functions; c) its computational operations; 2) determines – a) variations in the computational properties of a neuron (variations of the discharges between bursts and regular forms of pulsation); b) back distribution of action potentials. Dendritic spines can form synaptic connection – one of the main factors for increasing the diversity of forms of synaptic connections of neurons. Their volume and shape can change over a short period of time, and they can rotate in space, appear and disappear by themselves. Spines play a key role in selectively changing the strength of synaptic connections during the memorization and learning process. Glial cells are active participants in diffuse transmission of nerve impulses in the brain. Astrocytes form a three-dimensional, functionally “syncytia-like” formation, inside of which there are neurons, thus causing their specific microenvironment. They and neurons are structurally and functionally interconnected, based on which their permanent interaction occurs. Oligodendrocytes provide conditions for the generation and transmission of nerve impulses along the processes of neurons and play a significant role in the processes of their excitation and inhibition. Microglial cells play an important role in the formation of the brain, especially in the formation and maintenance of synapses. Thus, the CNS should be considered as a single, functionally “syncytia-like”, structural entity. Because the three-dimensional distribution of dendritic branches in space is important for determining the type of information that goes to a neuron, it is necessary to consider the three-dimensionality of their structure when analyzing the implementation of their functions.

A compact holographic projector module for high-resolution 3D multi-site two-photon photostimulation

Patterned two-photon (2P) photolysis via holographic illumination is a powerful method to investigate neuronal function because of its capability to emulate multiple synaptic inputs in three dimensions (3D) simultaneously. However, like any optical system, holographic projectors have a finite space-bandwidth product that restricts the spatial range of patterned illumination or field-of-view (FOV) for a desired resolution. Such trade-off between holographic FOV and resolution restricts the coverage within a limited domain of the neuron's dendritic tree to perform highly resolved patterned 2P photolysis on individual spines. Here, we integrate a holographic projector into a commercial 2P galvanometer-based 2D scanning microscope with an uncaging unit and extend the accessible holographic FOV by using the galvanometer scanning mirrors to reposition the holographic FOV arbitrarily across the imaging FOV. The projector system utilizes the microscope's built-in imaging functions. Stimulation positions can be selected from within an acquired 3D image stack (the volume-of-interest, VOI) and the holographic projector then generates 3D illumination patterns with multiple uncaging foci. The imaging FOV of our system is 800×800 μm2 within which a holographic VOI of 70×70×70 μm3 can be chosen at arbitrary positions and also moved during experiments without moving the sample. We describe the design and alignment protocol as well as the custom software plugin that controls the 3D positioning of stimulation sites. We demonstrate the neurobiological application of the system by simultaneously uncaging glutamate at multiple spines within dendritic domains and consequently observing summation of postsynaptic potentials at the soma, eventually resulting in action potentials. At the same time, it is possible to perform two-photon Ca2+ imaging in 2D in the dendrite and thus to monitor synaptic Ca2+ entry in selected spines and also local regenerative events such as dendritic action potentials.

Neuronal morphologies built for reliable physiology in a rhythmic motor circuit

It is often assumed that highly-branched neuronal structures perform compartmentalized computations. However, previously we showed that the Gastric Mill (GM) neuron in the crustacean stomatogastric ganglion (STG) operates like a single electrotonic compartment, despite having thousands of branch points and total cable length >10 mm (Otopalik et al., 2017a; 2017b). Here we show that compact electrotonic architecture is generalizable to other STG neuron types, and that these neurons present direction-insensitive, linear voltage integration, suggesting they pool synaptic inputs across their neuronal structures. We also show, using simulations of 720 cable models spanning a broad range of geometries and passive properties, that compact electrotonus, linear integration, and directional insensitivity in STG neurons arise from their neurite geometries (diameters tapering from 10-20 µm to < 2 µm at their terminal tips). A broad parameter search reveals multiple morphological and biophysical solutions for achieving different degrees of passive electrotonic decrement and computational strategies in the absence of active properties.

Functional Synaptic Architecture of Callosal Inputs in Mouse Primary Visual Cortex

Callosal projections are thought to play a critical role in coordinating neural activity between the cerebral hemispheres in placental mammals, but the rules that govern the arrangement of callosal synapses on the dendrites of their target neurons remain poorly understood. Here we describe a high-throughput method to map the functional organization of callosal connectivity by combining in vivo 3D random-access two-photon calcium imaging of the dendritic spines of single V1 neurons with optogenetic stimulation of the presynaptic neural population in the contralateral hemisphere. We find that callosal-recipient spines are more likely to cluster with non-callosal-recipient spines with similar orientation preference. These observations, based on optogenetic stimulation, were confirmed by direct anatomical visualization of callosal synaptic connections using post hoc expansion microscopy. Our results demonstrate, for the first time, that functional synaptic clustering in a short dendritic segment could play a role in integrating distinct neuronal circuits.

Two-Photon Uncaging of Glutamate

Two-photon microscopy produces the excited singlet state of a chromophore with wavelengths approximately double that used for normal excitation. Two photons are absorbed almost simultaneously, via a virtual state, and this makes the excitation technique inherently non-linear. It requires ultra-fast lasers to deliver the high flux density needed to access intrinsically very short lived intermediates, and in combination with lenses of high numerical aperture, this confines axial excitation highly. Since the two-photon excitation volume is similar to a large spine head, the technique has been widely used to study glutamatergic transmission in brain slices. Here I describe the principles of two-photon uncaging of glutamate and provide a practical guide to its application.

Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity

Many neurons possess dendrites enriched with sodium channels and are capable of generating action potentials. However, the role of dendritic sodium spikes remain unclear. Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons.

Synaptic topography - Converging connections and emerging function

Brain circuits are constituted of individual neurons that are interconnected with a vast array of synapses. In order to understand how brain function emerges from this complex synaptic network, immense efforts have been made to trace the synaptic topography, i.e. arrangement of synaptic connections, of the network. In addition to anatomically elaborating the synaptic layout at multiple levels across brain regions, recent studies have attempted to elucidate the fundamental wiring principles that govern neural information processing in the brain, establishing a link between anatomy and function. In this review, I will discuss recent discoveries on the topographical organization of synaptic connections at the cell-to-cell and subcellular levels in the cortex and hippocampus. Accumulating evidence leads us to acknowledge the highly structured, non-random synaptic connectivity that emerges together with sensory feature preferences of neurons and synchronous neuronal activity.

Sequential Nonlinear Filtering of Local Motion Cues by Global Motion Circuits

Many animals guide their movements using optic flow, the displacement of stationary objects across the retina caused by self-motion. How do animals selectively synthesize a global motion pattern from its local motion components? To what extent does this feature selectivity rely on circuit mechanisms versus dendritic processing? Here we used in vivo calcium imaging to identify pre- and postsynaptic mechanisms for processing local motion signals in global motion detection circuits in Drosophila. Lobula plate tangential cells (LPTCs) detect global motion by pooling input from local motion detectors, T4/T5 neurons. We show that T4/T5 neurons suppress responses to adjacent local motion signals whereas LPTC dendrites selectively amplify spatiotemporal sequences of local motion signals consistent with preferred global patterns. We propose that sequential nonlinear suppression and amplification operations allow optic flow circuitry to simultaneously prevent saturating responses to local signals while creating selectivity for global motion patterns critical to behavior.

Dendritic solutions to the credit assignment problem

Guaranteeing that synaptic plasticity leads to effective learning requires a means for assigning credit to each neuron for its contribution to behavior. The 'credit assignment problem' refers to the fact that credit assignment is non-trivial in hierarchical networks with multiple stages of processing. One difficulty is that if credit signals are integrated with other inputs, then it is hard for synaptic plasticity rules to distinguish credit-related activity from non-credit-related activity. A potential solution is to use the spatial layout and non-linear properties of dendrites to distinguish credit signals from other inputs. In cortical pyramidal neurons, evidence hints that top-down feedback signals are integrated in the distal apical dendrites and have a distinct impact on spike-firing and synaptic plasticity. This suggests that the distal apical dendrites of pyramidal neurons help the brain to solve the credit assignment problem.

Glutamate Activity Regulates and Dendritic Development of J-RGCs

Retinal ganglion cells (RGCs) have a wide variety of dendritic architectures, which are critical for the formation of their function-specific synaptic circuitry. The developmental regulation of the dendrites of RGCs is thought to be subtype dependent. The purpose of this study is to characterize the dendritic development of a genetically identified RGC subtype, JamB RGCs (J-RGCs), and the roles of glutamate receptor activity on the dendritic development of these cells. We show that the dendrites of J-RGCs are strictly ramified in the outer portion of the inner plexiform layer (IPL) of the retina at the age of postnatal day 8 (P8), mimicking the ramification pattern of adults. However, several other important features of dendrites undergo substantial developmental refinement after P8. From P8 to P13, the dendritic development of J-RGCs is characterized by a dramatic increase of dendritic length and the size of the dendritic field. After eye opening, the dendritic development of J-RGCs is characterized by a tremendous decrease of the number of dendritic protrusions (spine-like structures) and a consolidation of the size of the dendritic field. To determine whether the dendritic development of J-RGCs is regulated by glutamatergic activity, we conditionally knocked out the expression of an obligatory subunit of N-methyl-D-aspartate receptors (NMDARs), NR1 (Grin1), in J-RGCs. We found that J-RGCs with the NMDAR mutation have decreased dendrite outgrowth and dendritic field expansion but increased number of dendritic protrusions before eye opening. To determine if visual experience regulates the development of J-RGC dendrites, we raised the mice in complete darkness after birth. Light deprivation prevented the decrease in the number of dendritic protrusions and the consolidation of the dendritic field of wild type (WT) mice after eye opening. However, light deprivation has no additional effect on the number of dendritic protrusions or the size of the dendritic field of J-RGCs with NMDAR mutation. Together, these results revealed the roles of light stimulation and NMDAR activity on the dendritic development of J-RGCs.

Selective effects of Δ9-tetrahydrocannabinol on medium spiny neurons in the striatum

Derivatives from the Cannabis plant are the most commonly abused illegal substances in the world. The main psychoactive component found in the plant, Δ-9-tetrahydrocannabinol (THC), exerts its effects through the endocannabinoid system. Manipulations of this system affect some types of learning that seem to be dependent on dorsal striatum synaptic plasticity. Dendritic spines exhibit important synaptic functional attributes and a potential for plasticity, which is thought to mediate long-lasting changes in behaviour. To study the possible structural plasticity changes that prolonged THC administration might exert in the dorsal striatum, adult, male C57BL6/J mice were intraperitoneally injected with THC (10mg/kg) or vehicle for 15 days followed by a 7-day drug-free period. Using single cell intracellular injections of Lucifer Yellow, confocal microscopy, and 3D reconstruction of labelled neurons, we studied dendritic spine density and spine size in medium spiny neurons (MSNs) of the anterior dorsolateral striatum (aDLS) and posterior dorsomedial striatum (pDMS). We found that the THC treatment increased dendritic spine density in the distal part of the dendrites of MSNs in the pDMS, but no changes were found in the rest of the parameters analysed in either region studied. We also observed that dendritic spines of MSNs of pDMS presented lower volume and surface area values than MSNs of the aDLS. These results seem to indicate that THC could induce structural plasticity alterations in the circuits involving pDMS MSNs.

A-type K+ channels impede supralinear summation of clustered glutamatergic inputs in layer 3 neocortical pyramidal neurons

A-type K⁺ channels restrain the spread of incoming signals in tufted and apical dendrites of pyramidal neurons resulting in strong compartmentalization. However, the exact subunit composition and functional significance of K⁺ channels expressed in small diameter proximal dendrites remain poorly understood. We focus on A-type K⁺ channels expressed in basal and oblique dendrites of cortical layer 3 pyramidal neurons, in ex vivo brain slices from young adult mice. Blocking putative Kv4 subunits with phrixotoxin-2 enhances depolarizing potentials elicited by uncaging RuBi-glutamate at single dendritic spines. A concentration of 4-aminopyridine reported to block Kv1 has no effect on such responses. 4-aminopyridine and phrixotoxin-2 increase supralinear summation of glutamatergic potentials evoked by synchronous activation of clustered spines. The effect of 4-aminopyridine on glutamate responses is simulated in a computational model where the dendritic A-type conductance is distributed homogeneously or in a linear density gradient. Thus, putative Kv4-containing channels depress excitatory inputs at single synapses. The additional recruitment of Kv1 subunits might require the synchronous activation of multiple inputs to regulate the gain of signal integration.

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We focus on A-type K+ channels expressed in oblique and basal dendrites.

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Putative Kv4 subunits depress excitatory signals generated by single spine excitation.

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Kv4 and Kv1 regulate supralinear signal integration at clustered dendritic spines.

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A computational model simulates Kv-mediated modulation of dendritic integration.

Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons

The NMDA spike is a long-lasting nonlinear phenomenon initiated locally in the dendritic branches of a variety of cortical neurons. It plays a key role in synaptic plasticity and in single-neuron computations. Combining dynamic system theory and computational approaches, we now explore how the timing of synaptic inhibition affects the NMDA spike and its associated membrane current. When impinging on its early phase, individual inhibitory synapses strongly, but transiently, dampen the NMDA spike; later inhibition prematurely terminates it. A single inhibitory synapse reduces the NMDA-mediated Ca²⁺ current, a key player in plasticity, by up to 45%. NMDA spikes in distal dendritic branches/spines are longer-lasting and more resilient to inhibition, enhancing synaptic plasticity at these branches. We conclude that NMDA spikes are highly sensitive to dendritic inhibition; sparse weak inhibition can finely tune synaptic plasticity both locally at the dendritic branch level and globally at the level of the neuron's output.

Studying Axon-Astrocyte Functional Interactions by 3D Two-Photon Ca2+ Imaging: A Practical Guide to Experiments and "Big Data" Analysis

Recent advances in fast volumetric imaging have enabled rapid generation of large amounts of multi-dimensional functional data. While many computer frameworks exist for data storage and analysis of the multi-gigabyte Ca²⁺ imaging experiments in neurons, they are less useful for analyzing Ca²⁺ dynamics in astrocytes, where transients do not follow a predictable spatio-temporal distribution pattern. In this manuscript, we provide a detailed protocol and commentary for recording and analyzing three-dimensional (3D) Ca²⁺ transients through time in GCaMP6f-expressing astrocytes of adult brain slices in response to axonal stimulation, using our recently developed tools to perform interactive exploration, filtering, and time-correlation analysis of the transients. In addition to the protocol, we release our in-house software tools and discuss parameters pertinent to conducting axonal stimulation/response experiments across various brain regions and conditions. Our software tools are available from the Volterra Lab webpage at https://wwwfbm.unil.ch/dnf/group/glia-an-active-synaptic-partner/member/volterra-andrea-volterra in the form of software plugins for Image J (NIH)—a de facto standard in scientific image analysis. Three programs are available: MultiROI_TZ_profiler for interactive graphing of several movable ROIs simultaneously, Gaussian_Filter5D for Gaussian filtering in several dimensions, and Correlation_Calculator for computing various cross-correlation parameters on voxel collections through time.

Neuronal oscillations: unavoidable and useful?

Neuronal systems have a high propensity to engage in oscillatory activity because both the properties of individual neurons and canonical circuit motifs favour rhythmic activity. In addition, coupled oscillators can engage in a large variety of dynamical regimes, ranging from synchronization with different phase offsets to chaotic behaviour. Which regime prevails depends on differences between preferred oscillation frequencies, coupling strength and coupling delays. The ability of delay coupled oscillator networks to generate a rich repertoire of temporally structured activation sequences is exploited by central pattern generator networks for the control of movements. However, it is less clear whether temporal patterning of neuronal discharges also plays a role in cognitive processes. Here, it will be argued that the temporal patterning of neuronal discharges emerging from delay coupled oscillator networks plays a pivotal role in all instances in which selective relations have to be established between the responses of distributed assemblies of neurons. Examples are the dynamic formation of functional networks, the selective routing of activity in densely interconnected networks, the attention-dependent selection of sensory signals, the fast and context-dependent binding of responses for further joint processing in pattern recognition and the formation of associations by learning. Special consideration is given to arguments that challenge a functional role of oscillations and synchrony in cognition because of the volatile nature of these phenomena and recent evidence will be reviewed suggesting that this volatility is functionally advantageous.

New Types of Experiments Reveal that a Neuron Functions as Multiple Independent Threshold Units

Neurons are the computational elements that compose the brain and their fundamental principles of activity are known for decades. According to the long-lasting computational scheme, each neuron sums the incoming electrical signals via its dendrites and when the membrane potential reaches a certain threshold the neuron typically generates a spike to its axon. Here we present three types of experiments, using neuronal cultures, indicating that each neuron functions as a collection of independent threshold units. The neuron is anisotropically activated following the origin of the arriving signals to the membrane, via its dendritic trees. The first type of experiments demonstrates that a single neuron’s spike waveform typically varies as a function of the stimulation location. The second type reveals that spatial summation is absent for extracellular stimulations from different directions. The third type indicates that spatial summation and subtraction are not achieved when combining intra- and extra- cellular stimulations, as well as for nonlocal time interference, where the precise timings of the stimulations are irrelevant. Results call to re-examine neuronal functionalities beyond the traditional framework, and the advanced computational capabilities and dynamical properties of such complex systems.

Local Signals in Mouse Horizontal Cell Dendrites

The mouse retina contains a single type of horizontal cell, a GABAergic interneuron that samples from all cone photoreceptors within reach and modulates their glutamatergic output via parallel feedback mechanisms. Because horizontal cells form an electrically coupled network, they have been implicated in global signal processing, such as large-scale contrast enhancement. Recently, it has been proposed that horizontal cells can also act locally at the level of individual cone photoreceptors. To test this possibility physiologically, we used two-photon microscopy to record light stimulus-evoked Ca²⁺ signals in cone axon terminals and horizontal cell dendrites as well as glutamate release in the outer plexiform layer. By selectively stimulating the two mouse cone opsins with green and UV light, we assessed whether signals from individual cones remain isolated within horizontal cell dendritic tips or whether they spread across the dendritic arbor. Consistent with the mouse's opsin expression gradient, we found that the Ca²⁺ signals recorded from dendrites of dorsal horizontal cells were dominated by M-opsin and those of ventral horizontal cells by S-opsin activation. The signals measured in neighboring horizontal cell dendritic tips varied markedly in their chromatic preference, arguing against global processing. Rather, our experimental data and results from biophysically realistic modeling support the idea that horizontal cells can process cone input locally, extending the classical view of horizontal cell function. Pharmacologically removing horizontal cells from the circuitry reduced the sensitivity of the cone signal to low frequencies, suggesting that local horizontal cell feedback shapes the temporal properties of cone output.

Local Order within Global Disorder: Synaptic Architecture of Visual Space

Substantial evidence at the subcellular level indicates that the spatial arrangement of synaptic inputs onto dendrites could play a significant role in cortical computations, but how synapses of functionally defined cortical networks are arranged within the dendrites of individual neurons remains unclear. Here we assessed one-dimensional spatial receptive fields of individual dendritic spines within individual layer 2/3 neuron dendrites. Spatial receptive field properties of dendritic spines were strikingly diverse, with no evidence of large-scale topographic organization. At a fine scale, organization was evident: neighboring spines separated by less than 10 μm shared similar spatial receptive field properties and exhibited a distance-dependent correlation in sensory-driven and spontaneous activity patterns. Fine-scale dendritic organization was supported by the fact that functional groups of spines defined by dimensionality reduction of receptive field properties exhibited non-random dendritic clustering. Our results demonstrate that functional synaptic clustering is a robust feature existing at a local spatial scale. VIDEO ABSTRACT.

Detailed Dendritic Excitatory/Inhibitory Balance through Heterosynaptic Spike-Timing-Dependent Plasticity

The balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such a balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. In this study, we developed computational models of heterosynaptic spike-timing-dependent plasticity (STDP) to show that the excitatory/inhibitory balance in dendritic branches is robustly achieved through heterosynaptic interactions between excitatory and inhibitory synapses. The model reproduces key features of experimental heterosynaptic STDP well, and provides analytical insights. Furthermore, heterosynaptic STDP explains how the maturation of inhibitory neurons modulates the selectivity of excitatory neurons for binocular matching in the critical period plasticity. The model also provides an alternative explanation for the potential mechanism underlying the somatic detailed balance that is commonly associated with inhibitory STDP. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and the resultant dendritic computation.SIGNIFICANCE STATEMENT Recent experimental studies reveal that relative differences in spike timings experienced among neighboring glutamatergic and GABAergic synapses on a dendritic branch significantly influences changes in the efficiency of these synapses. This heterosynaptic form of spike-timing-dependent plasticity (STDP) is potentially important for shaping the synaptic organization and computation of neurons, but its functional role remains elusive. Through computational modeling at the parameter regime where previous experimental results are well reproduced, we show that heterosynaptic plasticity serves to finely balance excitatory and inhibitory inputs on the dendrite. Our results suggest a principle of GABA-driven neural circuit formation.

Dendrites, deep learning, and sequences in the hippocampus

The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.

Synaptic integrative mechanisms for spatial cognition

Synaptic integrative mechanisms have profound effects on electrical signaling in the brain that, although largely hidden from recording methods that observe the spiking activity of neurons, may be critical for the encoding, storage and retrieval of information. Here we review roles for synaptic integrative mechanisms in the selection, generation and plasticity of place and grid fields, and in related temporal codes for the representation of space. We outline outstanding questions and challenges in the testing of hypothesized models for spatial computation and memory.

Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level

Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the local non-linear processing of synaptic inputs allowed for by dendrites. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. We also explore how the connectivity between two cells is affected by these plasticity rules and by different synaptic distributions. Finally, we show that how memory retention during associative learning can be prolonged in networks of neurons by including dendrites.

Synaptic plasticity is the neuronal mechanism underlying learning. Here the authors construct biophysical models of pyramidal neurons that reproduce observed plasticity gradients along the dendrite and show that dendritic spike dependent LTP which is predominant in distal sections can prolong memory retention.

Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer

The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. Patterned mossy fiber activity induces rhythmic Golgi cell activity that is synchronized by shared parallel fiber input and by gap junctions. This leads to long distance synchrony of Golgi cells along the transverse axis, powerfully regulating granule cell firing by imposing inhibition during a specific time window. The essential network mechanisms, including tunable Golgi cell oscillations, on-beam inhibition and NMDA receptors causing first winner keeps winning of granule cells, illustrate how fundamental properties of the granule layer operate in tandem to produce (1) well timed and spatially bound output, (2) a wide dynamic range of granule cell firing and (3) transient and coherent gating oscillations. These results substantially enrich our understanding of granule cell layer processing, which seems to promote spatial group selection of granule cell activity as a function of timing of mossy fiber input.

The cerebellum is an organ of peculiar geometrical properties, and has been attributed the function of applying spatiotemporal transforms to sensorimotor data since Eccles. In this work we have analyzed the spatiotemporal response properties of the first part of the cerebellar circuit, the granule layer. On the basis of a biophysically plausible and large-scale model of the cerebellum, constrained by a wealth of anatomical data, we study the network dynamics and firing properties of individual cell populations in response to 'realistic' input patterns. We make specific predictions about the spatiotemporal features of granule layer processing regarding the effects of the gap junction coupled network of Golgi cells on a spatially restricted input, in an effect we denominate first-takes-all. Furthermore, we calculate that the granule cell layer has a wide dynamic range, indicating that this is a system that can transmit large variations of input intensities.

Learning with three factors: modulating Hebbian plasticity with errors

Synaptic plasticity is a central theme in neuroscience. A framework of three-factor learning rules provides a powerful abstraction, helping to navigate through the abundance of models of synaptic plasticity. It is well-known that the dopamine modulation of learning is related to reward, but theoretical models predict other functional roles of the modulatory third factor; it may encode errors for supervised learning, summary statistics of the population activity for unsupervised learning or attentional feedback. Specialized structures may be needed in order to generate and propagate third factors in the neural network.

Learning and recognition of tactile temporal sequences by mice and humans

The world around us is replete with stimuli that unfold over time. When we hear an auditory stream like music or speech or scan a texture with our fingertip, physical features in the stimulus are concatenated in a particular order. This temporal patterning is critical to interpreting the stimulus. To explore the capacity of mice and humans to learn tactile sequences, we developed a task in which subjects had to recognise a continuous modulated noise sequence delivered to whiskers or fingertips, defined by its temporal patterning over hundreds of milliseconds. GO and NO-GO sequences differed only in that the order of their constituent noise modulation segments was temporally scrambled. Both mice and humans efficiently learned tactile sequences. Mouse sequence recognition depended on detecting transitions in noise amplitude; animals could base their decision on the earliest information available. Humans appeared to use additional cues, including the duration of noise modulation segments.

DOI:http://dx.doi.org/10.7554/eLife.27333.001

Oligophrenin-1 regulates number, morphology and synaptic properties of adult-born inhibitory interneurons in the olfactory bulb

Among the X-linked genes associated with intellectual disability, Oligophrenin-1 (OPHN1) encodes for a Rho GTPase-activating protein, a key regulator of several developmental processes, such as dendrite and spine formation and synaptic activity. Inhibitory interneurons play a key role in the development and function of neuronal circuits. Whether a mutation of OPHN1 can affect morphology and synaptic properties of inhibitory interneurons remains poorly understood. To address these open questions, we studied in a well-established mouse model of X-linked intellectual disability, i.e. a line of mice carrying a null mutation of OPHN1, the development and function of adult generated inhibitory interneurons in the olfactory bulb. Combining quantitative morphological analysis and electrophysiological recordings we found that the adult generated inhibitory interneurons were dramatically reduced in number and exhibited a higher proportion of filopodia-like spines, with the consequences on their synaptic function, in OPHN1 ko mice. Furthermore, we found that olfactory behaviour was perturbed in OPHN1 ko mice. Chronic treatment with a Rho kinase inhibitor rescued most of the defects of the newly generated neurons. Altogether, our data indicated that OPHN1 plays a key role in regulating the number, morphology and function of adult-born inhibitory interneurons and contributed to identify potential therapeutic targets.

Variable Temporal Integration of Stimulus Patterns in the Mouse Barrel Cortex

Making sense of the world requires distinguishing temporal patterns and sequences lasting hundreds of milliseconds or more. How cortical circuits integrate over time to represent specific sensory sequences remains elusive. Here we assessed whether neurons in the barrel cortex (BC) integrate information about temporal patterns of whisker movements. We performed cell-attached recordings in anesthetized mice while delivering whisker deflections at variable intervals and compared the information carried by neurons about the latest interstimulus interval (reflecting sensitivity to instantaneous frequency) and earlier intervals (reflecting integration over timescales up to several hundred milliseconds). Neurons carried more information about the latest interval than earlier ones. The amount of temporal integration varied with neuronal responsiveness and with the cortical depth of the recording site, that is, with laminar location. A subset of neurons in the upper layers displayed the strongest integration. Highly responsive neurons in the deeper layers encoded the latest interval but integrated particularly weakly. Under these conditions, BC neurons act primarily as encoders of current stimulation parameters; however, our results suggest that temporal integration over hundreds of milliseconds can emerge in some neurons within BC.

Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells

The simple cells of the visual cortex respond over a narrow range of stimulus orientations, and this tuning is invariant to the contrast at which the stimulus is presented. The inputs to a single cell derive from a population of thalamic cells that provide a bell-shaped tuning width and offset that increases with stimulus contrast. Synaptic depression, noise and inhibition have been proposed as feedforward mechanisms to explain why these increases do not appear in simple cells. The extent to which these three mechanisms contribute to contrast-invariant orientation tuning is unknown. Consequently, the aim was to test the hypothesis that these mechanisms do not contribute equally. Unlike previous studies, all mechanisms were examined using the same network model based on Banitt et al. (2007). The results showed that thalamocortical synaptic noise was essential and sufficient to widen tuning widths at low contrasts to that of higher contrasts but could not counteract the offset at higher contrasts. Thalamocortical synaptic depression could only be used to counteract a small fraction of the offset otherwise the relationship between contrast and response rate was disrupted. Only broadly tuned simple and complex cell inhibition could counteract the remaining offset for all stimulus contrasts but complex cell inhibition reduced the gain of the response. These results suggest unequal contributions of these feedforward mechanisms with thalamic synaptic noise widening tuning widths for low contrasts, synaptic depression counteracting a small component of the offset and synaptic inhibition completely removing the remaining offset to produce contrast-invariant orientation tuning.

Synaptic input sequence discrimination on behavioral timescales mediated by reaction-diffusion chemistry in dendrites

Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here, we show that synaptically-driven reaction-diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data. Again, sequences were recognized, and local channel modulation downstream of putative sequence-triggered signaling could elicit changes in neuronal firing. We predict that dendritic sequence-recognition zones occupy 5 to 30 microns and recognize time-intervals of 0.2 to 5 s. We suggest that this mechanism provides highly parallel and selective neural computation in a functionally important time range.

Action potential initiation in a two-compartment model of pyramidal neuron mediated by dendritic Ca2+ spike

Dendritic Ca²⁺ spike endows cortical pyramidal cell with powerful ability of synaptic integration, which is critical for neuronal computation. Here we propose a two-compartment conductance-based model to investigate how the Ca²⁺ activity of apical dendrite participates in the action potential (AP) initiation to affect the firing properties of pyramidal neurons. We have shown that the apical input with sufficient intensity triggers a dendritic Ca²⁺ spike, which significantly boosts dendritic inputs as it propagates to soma. Such event instantaneously shifts the limit cycle attractor of the neuron and results in a burst of APs, which makes its firing rate reach a plateau steady-state level. Delivering current to two chambers simultaneously increases the level of neuronal excitability and decreases the threshold of input-output relation. Here the back-propagating APs facilitate the initiation of dendritic Ca²⁺ spike and evoke BAC firing. These findings indicate that the proposed model is capable of reproducing in vitro experimental observations. By determining spike initiating dynamics, we have provided a fundamental link between dendritic Ca²⁺ spike and output APs, which could contribute to mechanically interpreting how dendritic Ca²⁺ activity participates in the simple computations of pyramidal neuron.

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”.

Systems-based analysis of dendritic nonlinearities reveals temporal feature extraction in mouse L5 cortical neurons

What do dendritic nonlinearities tell a neuron about signals injected into the dendrite? Linear and nonlinear dendritic components affect how time-varying inputs are transformed into action potentials (APs), but the relative contribution of each component is unclear. We developed a novel systems-identification approach to isolate the nonlinear response of layer 5 pyramidal neuron dendrites in mouse prefrontal cortex in response to dendritic current injections. We then quantified the nonlinear component and its effect on the soma, using functional models composed of linear filters and static nonlinearities. Both noise and waveform current injections revealed linear and nonlinear components in the dendritic response. The nonlinear component consisted of fast Na⁺ spikes that varied in amplitude 10-fold in a single neuron. A functional model reproduced the timing and amplitude of the dendritic spikes and revealed that they were selective to a preferred input dynamic (~4.5 ms rise time). The selectivity of the dendritic spikes became wider in the presence of additive noise, which was also predicted by the functional model. A second functional model revealed that the dendritic spikes were weakly boosted before being linearly integrated at the soma. For both our noise and waveform dendritic input, somatic APs were dependent on the somatic integration of the stimulus, followed a subset of large dendritic spikes, and were selective to the same input dynamics preferred by the dendrites. Our results suggest that the amplitude of fast dendritic spikes conveys information about high-frequency features in the dendritic input, which is then combined with low-frequency somatic integration.NEW & NOTEWORTHY The nonlinear response of layer 5 mouse pyramidal dendrites was isolated with a novel systems-based approach. In response to dendritic current injections, the nonlinear component contained mostly fast, variable-amplitude, Na⁺ spikes. A functional model accounted for the timing and amplitude of the dendritic spikes and revealed that dendritic spikes are selective to a preferred input dynamic, which was verified experimentally. Thus, fast dendritic nonlinearities behave as high-frequency feature detectors that influence somatic action potentials.

Feedforward motor information enhances somatosensory responses and sharpens angular tuning of rat S1 barrel cortex neurons

The primary vibrissae motor cortex (vM1) is responsible for generating whisking movements. In parallel, vM1 also sends information directly to the sensory barrel cortex (vS1). In this study, we investigated the effects of vM1 activation on processing of vibrissae sensory information in vS1 of the rat. To dissociate the vibrissae sensory-motor loop, we optogenetically activated vM1 and independently passively stimulated principal vibrissae. Optogenetic activation of vM1 supra-linearly amplified the response of vS1 neurons to passive vibrissa stimulation in all cortical layers measured. Maximal amplification occurred when onset of vM1 optogenetic activation preceded vibrissa stimulation by 20 ms. In addition to amplification, vM1 activation also sharpened angular tuning of vS1 neurons in all cortical layers measured. Our findings indicated that in addition to output motor signals, vM1 also sends preparatory signals to vS1 that serve to amplify and sharpen the response of neurons in the barrel cortex to incoming sensory input signals.

DOI:http://dx.doi.org/10.7554/eLife.21843.001

Two identified looming detectors in the locust: ubiquitous lateral connections among their inputs contribute to selective responses to looming objects

In locusts, two lobula giant movement detector neurons (LGMDs) act as looming object detectors. Their reproducible responses to looming and their ethological significance makes them models for single neuron computation. But there is no comprehensive picture of the neurons that connect directly to each LGMD. We used high-through-put serial block-face scanning-electron-microscopy to reconstruct the network of input-synapses onto the LGMDs over spatial scales ranging from single synapses and small circuits, up to dendritic branches and total excitatory input. Reconstructions reveal that many trans-medullary-afferents (TmAs) connect the eye with each LGMD, one TmA per facet per LGMD. But when a TmA synapses with an LGMD it also connects laterally with another TmA. These inter-TmA synapses are always reciprocal. Total excitatory input to the LGMD 1 and 2 comes from 131,000 and 186,000 synapses reaching densities of 3.1 and 2.6 synapses per μm² respectively. We explored the computational consequences of reciprocal synapses between each TmA and 6 others from neighbouring columns. Since any lateral interactions between LGMD inputs have always been inhibitory we may assume these reciprocal lateral connections are most likely inhibitory. Such reciprocal inhibitory synapses increased the LGMD’s selectivity for looming over passing objects, particularly at the beginning of object approach.