Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons

Unilateral Optogenetic Inhibition and Excitation of Basal Ganglia Output Affect Directional Lick Choices and Movement Initiation in Mice

Models of basal ganglia (BG) function predict that tonic inhibitory output to motor thalamus (MT) suppresses unwanted movements, and that a decrease in such activity leads to action selection. Further, for unilateral activity changes in the BG, a lateralized effect on contralateral movements can be expected due to ipsilateral thalamocortical connectivity. However, a direct test of these outcomes of thalamic inhibition has not been performed. To conduct such a direct test, we utilized rapid optogenetic activation and inactivation of the GABAergic output of the substantia nigra pars reticulata (SNr) to MT in male and female mice that were trained in a sensory cued left/right licking task. Directional licking tasks have previously been shown to depend on a thalamocortical feedback loop between ventromedial MT and antero-lateral premotor cortex. In confirmation of model predictions, we found that unilateral optogenetic inhibition of GABAergic output from the SNr, during ipsilaterally cued trials, biased decision making towards a contralateral lick without affecting motor performance. In contrast, optogenetic excitation of SNr terminals in MT resulted in an opposite bias towards the ipsilateral direction confirming a bidirectional effect of tonic nigral output on directional decision making. However, direct optogenetic excitation of neurons in the SNr resulted in bilateral movement suppression, which is in agreement with previous results that show such suppression for nigral terminals in the superior colliculus (SC), which receives a bilateral projection from SNr.

Axonal Computations

Axons functionally link the somato-dendritic compartment to synaptic terminals. Structurally and functionally diverse, they accomplish a central role in determining the delays and reliability with which neuronal ensembles communicate. By combining their active and passive biophysical properties, they ensure a plethora of physiological computations. In this review, we revisit the biophysics of generation and propagation of electrical signals in the axon and their dynamics. We further place the computational abilities of axons in the context of intracellular and intercellular coupling. We discuss how, by means of sophisticated biophysical mechanisms, axons expand the repertoire of axonal computation, and thereby, of neural computation.

The contribution of ion channels in input-output plasticity

Persistent changes that occur in brain circuits are classically thought to be mediated by long-term modifications in synaptic efficacy. Yet, many studies have shown that voltage-gated ion channels located at the input and output side of the neurons are also the subject to persistent modifications. These channels are thus responsible for intrinsic plasticity that is expressed in many different neuronal types including glutamatergic principal neurons and GABAergic interneurons. As for synaptic plasticity, activation of synaptic glutamate receptors initiate persistent modification in neuronal excitability. We review here how synaptic input can be efficiently altered by activity-dependent modulation of ion channels that control EPSP amplification, spike threshold or resting membrane potential. We discuss the nature of the learning rules shared by intrinsic and synaptic plasticity, the mechanisms of ion channel regulation and the impact of intrinsic plasticity on induction of synaptic modifications.

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.

Enhanced Dendritic Compartmentalization in Human Cortical Neurons

The biophysical features of neurons shape information processing in the brain. Cortical neurons are larger in humans than in other species, but it is unclear how their size affects synaptic integration. Here, we perform direct electrical recordings from human dendrites and report enhanced electrical compartmentalization in layer 5 pyramidal neurons. Compared to rat dendrites, distal human dendrites provide limited excitation to the soma, even in the presence of dendritic spikes. Human somas also exhibit less bursting due to reduced recruitment of dendritic electrogenesis. Finally, we find that decreased ion channel densities result in higher input resistance and underlie the lower coupling of human dendrites. We conclude that the increased length of human neurons alters their input-output properties, which will impact cortical computation. VIDEO ABSTRACT.

Prolonged post-stimulation response induced by 980-nm infrared neural stimulation in the rat primary motor cortex

The post-stimulation response of neural activities plays an important role to evaluate the effectiveness and safety of neural modulation techniques. Previous studies have established the capability of infrared neural modulation (INM) on neural firing regulation in the central nervous system (CNS); however, the dynamic neural activity after the laser offset has not been well characterized yet. We applied 980-nm infrared diode laser light to irradiate the primary motor cortex of rats, and tungsten electrode was inserted to record the single-unit activity of neurons at the depth of 800-1000 μm (layer V of primary motor cortex). The neural activities were assessed through the change of neural firing rate and firing pattern pre- and post-stimulation with various radiant exposures. The results showed that the 980-nm laser could modulate the firing properties of neurons in the deep layer of the cortex. More neurons with post-stimulation response (78% vs. 83%) were observed at higher stimulation intensity (0.803 J/cm² vs. 1.071 J/cm², respectively). The change of firing rate also increased with radiant exposures increasing, and the response lasted up to 4.5 s at 1.071 J/cm², which was significantly longer than the theoretical thermal relaxation time. Moreover, the increasing Fano factors indicated the irregularity firing pattern of post-stimulation response. Our results confirmed that neural activity maintained a prolonged post-stimulation response after INM, which may provide necessary measurable data for optimization of INM applications in CNS.

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.

Dynamic Communications Between GABAA Switch, Local Connectivity, and Synapses During Cortical Development: A Computational Study

Several factors regulate cortical development, such as changes in local connectivity and the influences of dynamical synapses. In this study, we simulated various factors affecting the regulation of neural network activity during cortical development. Previous studies have shown that during early cortical development, the reversal potential of GABA_A shifts from depolarizing to hyperpolarizing. Here we provide the first integrative computational model to simulate the combined effects of these factors in a unified framework (building on our prior work: Khalil et al., 2017a,b). In the current study, we extend our model to monitor firing activity in response to the excitatory action of GABA_A. Precisely, we created a Spiking Neural Network model that included certain biophysical parameters for lateral connectivity (distance between adjacent neurons) and nearby local connectivity (complex connections involving those between neuronal groups). We simulated different network scenarios (for immature and mature conditions) based on these biophysical parameters. Then, we implemented two forms of Short-term synaptic plasticity (depression and facilitation). Each form has two distinct kinds according to its synaptic time constant value. Finally, in both sets of networks, we compared firing rate activity responses before and after simulating dynamical synapses. Based on simulation results, we found that the modulation effect of dynamical synapses for evaluating and shaping the firing activity of the neural network is strongly dependent on the physiological state of GABA_A. Moreover, the STP mechanism acts differently in every network scenario, mirroring the crucial modulating roles of these critical parameters during cortical development. Clinical implications for pathological alterations of GABAergic signaling in neurological and psychiatric disorders are discussed.

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.

A simplified morphological classification scheme for pyramidal cells in six layers of primary somatosensory cortex of juvenile rats

The majority of neurons in the neocortex are excitatory pyramidal cells (PCs). Many systematic classification schemes have been proposed based the neuronal morphology, the chemical composition, and the synaptic connectivity, etc. Recently, a cortical column of primary somatosensory cortex (SSC) has been reconstruction and functionally simulated (Markram et al., 2015). Putting forward from this study, here we proposed a simplified classification scheme for PCs in all layers of the SSC by mainly identifying apical dendritic morphology based on a large data set of 3D neuron reconstructions. We used this scheme to classify three types in layer 2, two in layer 3, three in layer 4, four in layer 5, and six types in layer 6. These PC types were visually distinguished and confirmed by quantitative differences in their morphometric properties. The classes yielded using this scheme largely corresponded with PC classes that were defined previously based on other neuronal and synaptic properties such as long-range projects and synaptic innervations, further validating its applicability. Therefore, the morphology information of apical dendrites is sufficient for a simple scheme to classify a spectrum of anatomical types of PCs in the SSC.

Modulations of dendritic Ca2+ spike with weak electric fields in layer 5 pyramidal cells

Weak electric fields (EFs) modulate input/output function of pyramidal cells. Dendritic Ca²⁺ spike is an important cellular mechanism for coupling synaptic inputs from different cortical layers, which plays a critical role in neuronal computation. This study aims to understand the effects of weak EFs on Ca²⁺ spikes initiated in the distal dendrites. We use a computational model to simulate dendritic Ca²⁺ spikes and backpropagating action potentials (APs) in layer 5 pyramidal cells. We apply uniform EFs (less than 20 mV/mm) to the model and examine how they affect the threshold for activation of Ca²⁺ spikes. We show that the effects of weak field on synaptically evoked Ca²⁺ spikes depend on the timing of synaptic inputs. When distal inputs coincide with the onset of EFs within a time window of several milliseconds, field-induced depolarization facilitates the initiation of Ca²⁺ spikes, while field-induced hyperpolarization suppresses dendritic APs. Sustained field-induced depolarization leads to the inactivation of Ca²⁺ channels and increases the threshold of Ca²⁺ spike. Sustained field-induced hyperpolarization de-inactivates Ca²⁺ channels and reduces the threshold of Ca²⁺ spike. By altering the threshold of backpropagation activated Ca²⁺ firing, field-induced depolarization increases the degree of coupling between inputs of the soma and distal dendrites, while field-induced hyperpolarization results in a decrease of coupling. The modulatory effects of weak EF are governed by the field direction with respect to the cell. Our study explains a fundamental link between field-induced polarization, dendritic Ca²⁺ spike, and somato-dendritic coupling. The findings are crucial to interpret how weak EFs achieve specific modulation of cellular activity.

Apical Function in Neocortical Pyramidal Cells: A Common Pathway by Which General Anesthetics Can Affect Mental State

It has been argued that general anesthetics suppress the level of consciousness, or the contents of consciousness, or both. The distinction between level and content is important because, in addition to clarifying the mechanisms of anesthesia, it may help clarify the neural bases of consciousness. We assess these arguments in the light of evidence that both the level and the content of consciousness depend upon the contribution of apical input to the information processing capabilities of neocortical pyramidal cells which selectively amplify relevant signals. We summarize research suggesting that what neocortical pyramidal cells transmit information about can be distinguished from levels of arousal controlled by sub-cortical nuclei and from levels of prioritization specified by interactions within the thalamocortical system. Put simply, on the basis of the observations reviewed, we hypothesize that when conscious we have particular, directly experienced, percepts, thoughts, feelings and intentions, and that general anesthetics affect consciousness by interfering with the subcellular processes by which particular activities are selectively amplified when relevant to the current context.

Stable Sequential Activity Underlying the Maintenance of a Precisely Executed Skilled Behavior

A vast array of motor skills can be maintained throughout life. Do these behaviors require stability of individual neuron tuning or can the output of a given circuit remain constant despite fluctuations in single cells? This question is difficult to address due to the variability inherent in most motor actions studied in the laboratory. A notable exception, however, is the courtship song of the adult zebra finch, which is a learned, highly precise motor act mediated by orderly dynamics within premotor neurons of the forebrain. By longitudinally tracking the activity of excitatory projection neurons during singing using two-photon calcium imaging, we find that both the number and the precise timing of song-related spiking events remain nearly identical over the span of several weeks to months. These findings demonstrate that learned, complex behaviors can be stabilized by maintaining precise and invariant tuning at the level of single neurons.

Synaptic Potential and Plasticity of an SK2 Channel Gate Regulate Spike Burst Activity in Cerebellar Purkinje Cells

Neurons store information and participate in memory engrams as a result of experience-dependent changes in synaptic weights and in membrane excitability. Here, we examine excitatory postsynaptic potential (EPSP) amplitude and neuronal excitability in relation to these two mechanisms of plasticity. We analyze somato-dendritic double-patch recordings from cerebellar Purkinje cells while inducing intrinsic, SK2 channel-dependent plasticity or blocking SK channels with bath application of apamin. Both manipulations increase the build-up of EPSP amplitudes during an EPSP train and enhance the number of EPSP-evoked spikes, yielding insights into the mechanistic contribution of EPSP amplitude to single spikes and spike bursts. EPSP amplitude has an impact on whether spikes are fired or not, but direct measures of excitability (spike threshold/AHP) are better predictors of whether individual spikes or spike bursts are fired. Our findings show that Purkinje cell spiking is synaptically driven but that burst firing is gated by SK2 channel modulation and plasticity.

The Slow Dynamics of Intracellular Sodium Concentration Increase the Time Window of Neuronal Integration: A Simulation Study

Changes in intracellular Na⁺ concentration ([Na⁺]_i) are rarely taken into account when neuronal activity is examined. As opposed to Ca²⁺, [Na⁺]_i dynamics are strongly affected by longitudinal diffusion, and therefore they are governed by the morphological structure of the neurons, in addition to the localization of influx and efflux mechanisms. Here, we examined [Na⁺]_i dynamics and their effects on neuronal computation in three multi-compartmental neuronal models, representing three distinct cell types: accessory olfactory bulb (AOB) mitral cells, cortical layer V pyramidal cells, and cerebellar Purkinje cells. We added [Na⁺]_i as a state variable to these models, and allowed it to modulate the Na⁺ Nernst potential, the Na⁺-K⁺ pump current, and the Na⁺-Ca²⁺ exchanger rate. Our results indicate that in most cases [Na⁺]_i dynamics are significantly slower than [Ca²⁺]_i dynamics, and thus may exert a prolonged influence on neuronal computation in a neuronal type specific manner. We show that [Na⁺]_i dynamics affect neuronal activity via three main processes: reduction of EPSP amplitude in repeatedly active synapses due to reduction of the Na⁺ Nernst potential; activity-dependent hyperpolarization due to increased activity of the Na⁺-K⁺ pump; specific tagging of active synapses by extended Ca²⁺ elevation, intensified by concurrent back-propagating action potentials or complex spikes. Thus, we conclude that [Na⁺]_i dynamics should be considered whenever synaptic plasticity, extensive synaptic input, or bursting activity are examined.

Dendritic Properties Control Energy Efficiency of Action Potentials in Cortical Pyramidal Cells

Neural computation is performed by transforming input signals into sequences of action potentials (APs), which is metabolically expensive and limited by the energy available to the brain. The metabolic efficiency of single AP has important consequences for the computational power of the cell, which is determined by its biophysical properties and morphologies. Here we adopt biophysically-based two-compartment models to investigate how dendrites affect energy efficiency of APs in cortical pyramidal neurons. We measure the Na⁺ entry during the spike and examine how it is efficiently used for generating AP depolarization. We show that increasing the proportion of dendritic area or coupling conductance between two chambers decreases Na⁺ entry efficiency of somatic AP. Activating inward Ca²⁺ current in dendrites results in dendritic spike, which increases AP efficiency. Activating Ca²⁺-activated outward K⁺ current in dendrites, however, decreases Na⁺ entry efficiency. We demonstrate that the active and passive dendrites take effects by altering the overlap between Na⁺ influx and internal current flowing from soma to dendrite. We explain a fundamental link between dendritic properties and AP efficiency, which is essential to interpret how neural computation consumes metabolic energy and how biophysics and morphologies contribute to such consumption.

A Laminar Organization for Selective Cortico-Cortical Communication

The neocortex is central to mammalian cognitive ability, playing critical roles in sensory perception, motor skills and executive function. This thin, layered structure comprises distinct, functionally specialized areas that communicate with each other through the axons of pyramidal neurons. For the hundreds of such cortico-cortical pathways to underlie diverse functions, their cellular and synaptic architectures must differ so that they result in distinct computations at the target projection neurons. In what ways do these pathways differ? By originating and terminating in different laminae, and by selectively targeting specific populations of excitatory and inhibitory neurons, these “interareal” pathways can differentially control the timing and strength of synaptic inputs onto individual neurons, resulting in layer-specific computations. Due to the rapid development in transgenic techniques, the mouse has emerged as a powerful mammalian model for understanding the rules by which cortical circuits organize and function. Here we review our understanding of how cortical lamination constrains long-range communication in the mammalian brain, with an emphasis on the mouse visual cortical network. We discuss the laminar architecture underlying interareal communication, the role of neocortical layers in organizing the balance of excitatory and inhibitory actions, and highlight the structure and function of layer 1 in mouse visual cortex.

Cross-compartmental Modulation of Dendritic Signals for Retinal Direction Selectivity

Compartmentalized signaling in dendritic subdomains is critical for the function of many central neurons. In the retina, individual dendritic sectors of a starburst amacrine cell (SAC) are preferentially activated by different directions of linear motion, indicating limited signal propagation between the sectors. However, the mechanism that regulates this propagation is poorly understood. Here, we find that metabotropic glutamate receptor 2 (mGluR2) signaling, which acts on voltage-gated calcium channels in SACs, selectively restricts cross-sector signal propagation in SACs, but does not affect local dendritic computation within individual sectors. mGluR2 signaling ensures sufficient electrotonic isolation of dendritic sectors to prevent their depolarization during non-preferred motion, yet enables controlled multicompartmental signal integration that enhances responses to preferred motion. Furthermore, mGluR2-mediated dendritic compartmentalization in SACs is important for the functional output of direction-selective ganglion cells (DSGCs). Therefore, our results directly link modulation of dendritic compartmentalization to circuit-level encoding of motion direction in the retina.

Differential Contribution of Ca2+-Dependent Mechanisms to Hyperexcitability in Layer V Neurons of the Medial Entorhinal Cortex

Temporal lobe epilepsy is characterized by recurrent seizures in one or both temporal lobes of the brain; some in vitro models show that epileptiform discharges initiate in entorhinal layer V neurons and then spread into other areas of the temporal lobe. We previously found that, in the presence of GABA_A receptor antagonists, stimulation of afferent fibers, terminating both at proximal and distal dendritic locations, initiated hyperexcitable bursts in layer V medial entorhinal neurons. We investigated the differential contribution of Ca²⁺-dependent mechanisms to the plateaus underlying these bursts at proximal and distal synapses. We found that the NMDA glutamatergic antagonist D,L-2-amino-5-phosphonovaleric acid (APV; 50 μM) reduced both the area and duration of the bursts at both proximal and distal synapses by about half. The L-type Ca²⁺ channel blocker nimodipine (10 μM) and the R- and T-type Ca²⁺ channel blocker NiCl₂ (200 μM) decreased the area of the bursts to a lesser extent; none of these effects appeared to be location-dependent. Remarkably, the perfusion of flufenamic acid (FFA; 100 μM), to block Ca²⁺-activated non-selective cation currents (I_CAN) mediated by transient receptor potential (TRP) channels, had a location-dependent effect, by abolishing burst firing and switching the suprathreshold response to a single action potential (AP) for proximal stimulation, but only minimally affecting the bursts evoked by distal stimulation. A similar outcome was found when FFA was pressure-applied locally around the proximal dendrite of the recorded neurons and in the presence of a selective blocker of melastatin TRP (TRPM) channels, 9-phenanthrol (100 μM), whereas a selective blocker of canonical TRP (TRPC) channels, SKF 96365, did not affect the bursts. These results indicate that different mechanisms might contribute to the initiation of hyperexcitability in layer V neurons at proximal and distal synapses and could shed light on the initiation of epileptiform activity in the entorhinal cortex.

Modulating STDP Balance Impacts the Dendritic Mosaic

The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties. We have previously shown that spike timing-dependent plasticity (STDP) can lead to synaptic efficacies being arranged into spatially segregated clusters. This effectively partitions the dendritic tree into a tessellated imprint which we have called a dendritic mosaic. Here, using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and STDP learning, we investigated the impact of altered STDP balance on forming such a spatial organization. We show that cluster formation and extend depend on several factors, including the balance between potentiation and depression, the afferents' mean firing rate and crucially on the dendritic morphology. We find that STDP balance has an important role to play for this emergent mode of spatial organization since any imbalances lead to severe degradation- and in some case even destruction- of the mosaic. Our model suggests that, over a broad range of of STDP parameters, synaptic plasticity shapes the spatial arrangement of synapses, favoring the formation of clustered efficacy engrams.

The stochastic nature of action potential backpropagation in apical tuft dendrites

In cortical pyramidal neurons, backpropagating action potentials (bAPs) supply Ca²⁺ to synaptic contacts on dendrites. To determine whether the efficacy of AP backpropagation into apical tuft dendrites is stable over time, we performed dendritic Ca²⁺ and voltage imaging in rat brain slices. We found that the amplitude of bAP-Ca²⁺ in apical tuft branches was unstable, given that it varied from trial to trial (termed "bAP-Ca²⁺ flickering"). Small perturbations in dendritic physiology, such as spontaneous synaptic inputs, channel inactivation, or temperature-induced changes in channel kinetics, can cause bAP flickering. In the tuft branches, the density of Na⁺ and K⁺ channels was sufficient to support local initiation of fast spikelets by glutamate iontophoresis. We quantified the time delay between the somatic AP burst and the peak of dendritic Ca²⁺ transient in the apical tuft, because this delay is important for induction of spike-timing dependent plasticity. Depending on the frequency of the somatic AP triplets, Ca²⁺ signals peaked in the apical tuft 20-50 ms after the 1st AP in the soma. Interestingly, at low frequency (<20 Hz), the Ca²⁺ peaked sooner than at high frequency, because only the 1st AP invaded tuft. Activation of dendritic voltage-gated Ca²⁺ channels is sensitive to the duration of the dendritic voltage transient. In apical tuft branches, small changes in the duration of bAP voltage waveforms cause disproportionately large increases in dendritic Ca²⁺ influx (bAP-Ca²⁺ flickering). The stochastic nature of bAP-Ca²⁺ adds a new perspective on the mechanisms by which pyramidal neurons combine inputs arriving at different cortical layers.NEW & NOTEWORTHY The bAP-Ca²⁺ signal amplitudes in some apical tuft branches randomly vary from moment to moment. In repetitive measurements, successful AP invasions are followed by complete failures. Passive spread of voltage from the apical trunk into the tuft occasionally reaches the threshold for local Na⁺ spike, resulting in stronger Ca²⁺ influx. During a burst of three somatic APs, the peak of dendritic Ca²⁺ in the apical tuft occurs with a delay of 20-50 ms depending on AP frequency.

Bottom-up gamma and stages of waking

Gamma activity has been proposed to promote the feed forward or "bottom-up" flow of information from lower to higher regions of the brain during perception. The pedunculopontine nucleus (PPN) modulates waking and REM sleep, and is part of the reticular activating system (RAS). The properties of PPN cells are unique in that all PPN neurons fire maximally at gamma band frequency regardless of electrophysiological or transmitter type, thus proposed as one origin of "bottom-up" gamma. This property is based on the presence of intrinsic membrane oscillations subserved by high threshold, voltage-dependent calcium channels. Moreover, some PPN cells are electrically coupled. Assuming that the population of PPN neurons has the capacity to fire at ∼40Hz coherently, then the population as a whole can be expected to generate a stable gamma band signal. But what if not all the neurons are firing at the peaks of the oscillations? That means that some cells may fire only at the peaks of every second oscillation. Therefore, the population as a whole can be expected to be firing at a net ∼20Hz. If some cells are firing at the peaks of every fourth oscillation, then the PPN as a whole would be firing at ∼10Hz. Firing at rates below 10Hz would imply that the system is seldom firing at the peaks of any oscillation, basically asleep, in slow wave sleep, thus the activation of the RAS is insufficient to promote waking. This hypothesis carries certain implications, one of which is that we awaken in stages as more and more cells are recruited to fire at the peaks of more and more oscillations. For this system, it would imply that, as we awaken, we step from ∼10Hz to ∼20Hz to ∼30Hz to ∼40Hz, that is, in stages and presumably at different levels of awareness. A similar process can be expected to take place as we fall asleep. Awakening can then be considered to be stepwise, not linear. That is, the implication is that the process of waking is a stepwise event, not a gradual increase, suggesting that the brain can spend time at each of these different stages of arousal.

From single cells and single columns to cortical networks: dendritic excitability, coincidence detection and synaptic transmission in brain slices and brains

Although patch pipettes were initially designed to record extracellularly the elementary current events from muscle and neuron membranes, the whole-cell and loose cell-attached recording configurations proved to be useful tools for examination of signalling within and between nerve cells. In this Paton Prize Lecture, I will initially summarize work on electrical signalling within single neurons, describing communication between the dendritic compartments, soma and nerve terminals via forward- and backward-propagating action potentials. The newly discovered dendritic excitability endows neurons with the capacity for coincidence detection of spatially separated subthreshold inputs. When these are occurring during a time window of tens of milliseconds, this information is broadcast to other cells by the initiation of bursts of action potentials (AP bursts). The occurrence of AP bursts critically impacts signalling between neurons that are controlled by target-cell-specific transmitter release mechanisms at downstream synapses even in different terminals of the same neuron. This can, in turn, induce mechanisms that underly synaptic plasticity when AP bursts occur within a short time window, both presynaptically in terminals and postsynaptically in dendrites. A fundamental question that arises from these findings is: 'what are the possible functions of active dendritic excitability with respect to network dynamics in the intact cortex of behaving animals?' To answer this question, I highlight in this review the functional and anatomical architectures of an average cortical column in the vibrissal (whisker) field of the somatosensory cortex (vS1), with an emphasis on the functions of layer 5 thick-tufted cells (L5tt) embedded in this structure. Sensory-evoked synaptic and action potential responses of these major cortical output neurons are compared with responses in the afferent pathway, viz. the neurons in primary somatosensory thalamus and in one of their efferent targets, the secondary somatosensory thalamus. Coincidence-detection mechanisms appear to be implemented in vivo as judged from the occurrence of AP bursts. Three-dimensional reconstructions of anatomical projections suggest that inputs of several combinations of thalamocortical projections and intra- and transcolumnar connections, specifically those from infragranular layers, could trigger active dendritic mechanisms that generate AP bursts. Finally, recordings from target cells of a column reveal the importance of AP bursts for signal transfer to these cells. The observations lead to the hypothesis that in vS1 cortex, the sensory afferent sensory code is transformed, at least in part, from a rate to an interval (burst) code that broadcasts the occurrence of whisker touch to different targets of L5tt cells. In addition, the occurrence of pre- and postsynaptic AP bursts may, in the long run, alter touch representation in cortex.

Area-Specific Features of Pyramidal Neurons-a Comparative Study in Mouse and Rhesus Monkey

A principal challenge of systems neuroscience is to understand the unique characteristics of cortical neurons and circuits that enable area- and species-specific sensory encoding, motor function, cognition, and behavior. To address this issue, we compared properties of layer 3 pyramidal neurons in 2 cortical areas that span a broad range of cortical function-primary sensory (V1), to cognitive (frontal)-in the mouse and the rhesus monkey. Hierarchical clustering and discriminant analyses of 15 physiological and 25 morphological variables revealed 2 fundamental principles. First, V1 and frontal neurons are remarkably similar with regard to nearly every property in the mouse, while the opposite is true in the monkey, with V1 and frontal neurons exhibiting significant differences in nearly every property assessed. Second, neurons within visual and frontal areas differ significantly between the mouse and the monkey. Neurons in mouse and monkey V1 are the same size, but differ in nearly every other way; mouse frontal cortical neurons are smaller than those in the monkey and also differ substantially with regard to most other properties. These findings have broad implications for understanding the differential contributions of heterogeneous neuronal types in construction of cortical microcircuitry in diverse brain areas and species.

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.

Pyramidal Neurons Are Not Generalizable Building Blocks of Cortical Networks

A key challenge in cortical neuroscience is to gain a comprehensive understanding of how pyramidal neuron heterogeneity across different areas and species underlies the functional specialization of individual neurons, networks, and areas. Comparative studies have been important in this endeavor, providing data relevant to the question of which of the many inherent properties of individual pyramidal neurons are necessary and sufficient for species-specific network and areal function. In this mini review, the importance of pyramidal neuron structural properties for signaling are outlined, followed by a summary of our recent work comparing the structural features of mouse (C57/BL6 strain) and rhesus monkey layer 3 (L3) pyramidal neurons in primary visual and frontal association cortices and their implications for neuronal and areal function. Based on these and other published data, L3 pyramidal neurons plausibly might be considered broadly “generalizable” from one area to another in the mouse neocortex due to their many similarities, but major differences in the properties of these neurons in diverse areas in the rhesus monkey neocortex rules this out in the primate. Further, fundamental differences in the dendritic topology of mouse and rhesus monkey pyramidal neurons highlight the implausibility of straightforward scaling and/or extrapolation from mouse to primate neurons and cortical networks.

Semi-Supervised Learning of Cartesian Factors: A Top-Down Model of the Entorhinal Hippocampal Complex

The existence of place cells (PCs), grid cells (GCs), border cells (BCs), and head direction cells (HCs) as well as the dependencies between them have been enigmatic. We make an effort to explain their nature by introducing the concept of Cartesian Factors. These factors have specific properties: (i) they assume and complement each other, like direction and position and (ii) they have localized discrete representations with predictive attractors enabling implicit metric-like computations. In our model, HCs make the distributed and local representation of direction. Predictive attractor dynamics on that network forms the Cartesian Factor "direction." We embed these HCs and idiothetic visual information into a semi-supervised sparse autoencoding comparator structure that compresses its inputs and learns PCs, the distributed local and direction independent (allothetic) representation of the Cartesian Factor of global space. We use a supervised, information compressing predictive algorithm and form direction sensitive (oriented) GCs from the learned PCs by means of an attractor-like algorithm. Since the algorithm can continue the grid structure beyond the region of the PCs, i.e., beyond its learning domain, thus the GCs and the PCs together form our metric-like Cartesian Factors of space. We also stipulate that the same algorithm can produce BCs. Our algorithm applies (a) a bag representation that models the "what system" and (b) magnitude ordered place cell activities that model either the integrate-and-fire mechanism, or theta phase precession, or both. We relate the components of the algorithm to the entorhinal-hippocampal complex and to its working. The algorithm requires both spatial and lifetime sparsification that may gain support from the two-stage memory formation of this complex.

Contextual modulation of primary visual cortex by auditory signals

Early visual cortex receives non-feedforward input from lateral and top-down connections (Muckli & Petro 2013 Curr. Opin. Neurobiol.23, 195–201. (doi:10.1016/j.conb.2013.01.020)), including long-range projections from auditory areas. Early visual cortex can code for high-level auditory information, with neural patterns representing natural sound stimulation (Vetter et al. 2014 Curr. Biol.24, 1256–1262. (doi:10.1016/j.cub.2014.04.020)). We discuss a number of questions arising from these findings. What is the adaptive function of bimodal representations in visual cortex? What type of information projects from auditory to visual cortex? What are the anatomical constraints of auditory information in V1, for example, periphery versus fovea, superficial versus deep cortical layers? Is there a putative neural mechanism we can infer from human neuroimaging data and recent theoretical accounts of cortex? We also present data showing we can read out high-level auditory information from the activation patterns of early visual cortex even when visual cortex receives simple visual stimulation, suggesting independent channels for visual and auditory signals in V1. We speculate which cellular mechanisms allow V1 to be contextually modulated by auditory input to facilitate perception, cognition and behaviour. Beyond cortical feedback that facilitates perception, we argue that there is also feedback serving counterfactual processing during imagery, dreaming and mind wandering, which is not relevant for immediate perception but for behaviour and cognition over a longer time frame.

This article is part of the themed issue ‘Auditory and visual scene analysis’.

The effects of arousal on apical amplification and conscious state

Neocortical pyramidal cells can integrate two classes of input separately and use one to modulate response to the other. Their tuft dendrites are electrotonically separated from basal dendrites and soma by the apical dendrite, and apical hyperpolarization-activated currents (Ih) further isolate subthreshold integration of tuft inputs. When apical depolarization exceeds a threshold, however, it can enhance response to the basal inputs that specify the cell's selective sensitivity. This process is referred to as apical amplification (AA). We review evidence suggesting that, by regulating Ih in the apical compartments, adrenergic arousal controls the coupling between apical and somatic integration zones thus modifying cognitive capabilities closely associated with consciousness. Evidence relating AA to schizophrenia, sleep, and anesthesia is reviewed, and we assess theories that emphasize the relevance of AA to consciousness. Implications for theories of neocortical computation that emphasize context-sensitive modulation are summarized. We conclude that the findings concerning AA and its regulation by arousal offer a new perspective on states of consciousness, the function and evolution of neocortex, and psychopathology. Many issues worthy of closer examination arise.

The Impact of BK Channels on Cellular Excitability Depends on their Subcellular Location

Large conductance calcium-activated potassium channels (or BK channels) fulfil a multitude of roles in the central nervous system. At the soma of many neuronal cell types they control the speed of action potential (AP) repolarization and therefore they can have an impact on neuronal excitability. Due to their presence in nerve terminals they also regulate transmitter release. BK channels have also been shown to be present in the dendrites of some neurons where they can regulate the magnitude and duration of dendritic spikes. Here, we investigate the impact of modulating the activation of BK channels at different locations on the cellular excitability of cortical layer 5 pyramidal neurons. We find that while somatic BK channels help to repolarize APs at the soma and mediate the fast after-hyperpolarization, dendritic BK channels are responsible for repolarization of dendritic calcium spikes and thereby regulate somatic AP burst firing. We found no evidence for a role of dendritic BK channels in the regulation of backpropagating AP amplitude or duration. These experiments highlight the diverse roles of BK channels in regulating neuronal excitability and indicate that their functional impact depends on their subcellular location.

Is realistic neuronal modeling realistic?

Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

The role of dendritic spiking mechanisms in neural processing is so far poorly understood. To investigate the role of calcium spikes in the functional properties of the single neuron and recurrent networks, we investigated a three compartment neuron model of the layer 5 pyramidal neuron with calcium dynamics in the distal compartment. By performing single neuron simulations with noisy synaptic input and occasional large coincident input at either just the distal compartment or at both somatic and distal compartments, we show that the presence of calcium spikes confers a substantial advantage for coincidence detection in the former case and a lesser advantage in the latter. We further show that the experimentally observed critical frequency phenomenon, in which action potentials triggered by stimuli near the soma above a certain frequency trigger a calcium spike at distal dendrites, leading to further somatic depolarization, is not exhibited by a neuron receiving realistically noisy synaptic input, and so is unlikely to be a necessary component of coincidence detection. We next investigate the effect of calcium spikes in propagation of spiking activities in a feed-forward network (FFN) embedded in a balanced recurrent network. The excitatory neurons in the network are again connected to either just the distal, or both somatic and distal compartments. With purely distal connectivity, activity propagation is stable and distinguishable for a large range of recurrent synaptic strengths if the feed-forward connections are sufficiently strong, but propagation does not occur in the absence of calcium spikes. When connections are made to both the somatic and the distal compartments, activity propagation is achieved for neurons with active calcium dynamics at a much smaller number of neurons per pool, compared to a network of passive neurons, but quickly becomes unstable as the strength of recurrent synapses increases. Activity propagation at higher scaling factors can be stabilized by increasing network inhibition or introducing short term depression in the excitatory synapses, but the signal to noise ratio remains low. Our results demonstrate that the interaction of synchrony with dendritic spiking mechanisms can have profound consequences for the dynamics on the single neuron and network level.

The Current Status of Somatostatin-Interneurons in Inhibitory Control of Brain Function and Plasticity

The mammalian neocortex contains many distinct inhibitory neuronal populations to balance excitatory neurotransmission. A correct excitation/inhibition equilibrium is crucial for normal brain development, functioning, and controlling lifelong cortical plasticity. Knowledge about how the inhibitory network contributes to brain plasticity however remains incomplete. Somatostatin- (SST-) interneurons constitute a large neocortical subpopulation of interneurons, next to parvalbumin- (PV-) and vasoactive intestinal peptide- (VIP-) interneurons. Unlike the extensively studied PV-interneurons, acknowledged as key components in guiding ocular dominance plasticity, the contribution of SST-interneurons is less understood. Nevertheless, SST-interneurons are ideally situated within cortical networks to integrate unimodal or cross-modal sensory information processing and therefore likely to be important mediators of experience-dependent plasticity. The lack of knowledge on SST-interneurons partially relates to the wide variety of distinct subpopulations present in the sensory neocortex. This review informs on those SST-subpopulations hitherto described based on anatomical, molecular, or electrophysiological characteristics and whose functional roles can be attributed based on specific cortical wiring patterns. A possible role for these subpopulations in experience-dependent plasticity will be discussed, emphasizing on learning-induced plasticity and on unimodal and cross-modal plasticity upon sensory loss. This knowledge will ultimately contribute to guide brain plasticity into well-defined directions to restore sensory function and promote lifelong learning.

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases.

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

PyramidalExplorer: A New Interactive Tool to Explore Morpho-Functional Relations of Human Pyramidal Neurons

This work presents PyramidalExplorer, a new tool to interactively explore and reveal the detailed organization of the microanatomy of pyramidal neurons with functionally related models. It consists of a set of functionalities that allow possible regional differences in the pyramidal cell architecture to be interactively discovered by combining quantitative morphological information about the structure of the cell with implemented functional models. The key contribution of this tool is the morpho-functional oriented design that allows the user to navigate within the 3D dataset, filter and perform Content-Based Retrieval operations. As a case study, we present a human pyramidal neuron with over 9000 dendritic spines in its apical and basal dendritic trees. Using PyramidalExplorer, we were able to find unexpected differential morphological attributes of dendritic spines in particular compartments of the neuron, revealing new aspects of the morpho-functional organization of the pyramidal neuron.

Dendritic integration: 60 years of progress

Understanding how individual neurons integrate the thousands of synaptic inputs they receive is critical to understanding how the brain works. Modeling studies in silico and experimental work in vitro, dating back more than half a century, have revealed that neurons can perform a variety of different passive and active forms of synaptic integration on their inputs. But how are synaptic inputs integrated in the intact brain? With the development of new techniques, this question has recently received substantial attention, with new findings suggesting that many of the forms of synaptic integration observed in vitro also occur in vivo, including in awake animals. Here we review six decades of progress, which collectively highlights the complex ways that single neurons integrate their inputs, emphasizing the critical role of dendrites in information processing in the brain.

Effects of Neural Morphology and Input Distribution on Synaptic Processing by Global and Focal NMDA-Spikes

Cortical neurons can respond to glutamatergic stimulation with regenerative N-Methyl-D-aspartic acid (NMDA)-spikes. NMDA-spikes were initially thought to depend on clustered synaptic activation. Recent work had shown however a new variety of a global NMDA-spike, which can be generated by randomly distributed inputs. Very little is known about the factors that influence the generation of these global NMDA-spikes, as well the potentially distinct rules of synaptic integration and the computational significance conferred by the two types of NMDA-spikes. Here I show that the input resistance (R_IN) plays a major role in influencing spike initiation; while the classical, focal NMDA-spike depended upon the local (dendritic) R_IN, the threshold of global NMDA-spike generation was set by the somatic R_IN. As cellular morphology can exert a large influence on R_IN, morphologically distinct neuron types can have dissimilar rules for NMDA-spikes generation. For example, cortical neurons in superficial layers were found to be generally prone to global NMDA-spike generation. In contrast, electric properties of cortical layer 5b cells clearly favor focal NMDA-spikes. These differences can translate into diverse synaptic integration rules for the different classes of cortical cells; simulated superficial layers neurons were found to exhibit strong synaptic interactions between different dendritic branches, giving rise to a single integrative compartment mediated by the global NMDA-spike. In these cells, efficiency of postsynaptic activation was relatively little dependent on synaptic distribution. By contrast, layer 5b neurons were capable of true multi-unit computation involving independent integrative compartments formed by clustered synaptic input which could trigger focal NMDA-spikes. In a sharp contrast to superficial layers neurons, randomly distributed synaptic inputs were not very effective in driving firing the layer 5b cells, indicating a possibility for different computation performed by these important cortical neurons.

Modeling the calcium spike as a threshold triggered fixed waveform for synchronous inputs in the fluctuation regime

Modeling the layer 5 pyramidal neuron as a system of three connected isopotential compartments, the soma, proximal, and distal compartment, with calcium spike dynamics in the distal compartment following first order kinetics, we are able to reproduce in-vitro experimental results which demonstrate the involvement of calcium spikes in action potentials generation. To explore how calcium spikes affect the neuronal output in-vivo, we emulate in-vivo like conditions by embedding the neuron model in a regime of low background fluctuations with occasional large synchronous inputs. In such a regime, a full calcium spike is only triggered by the synchronous events in a threshold like manner and has a stereotypical waveform. Hence, in such a regime, we are able to replace the calcium dynamics with a simpler threshold triggered current of fixed waveform, which is amenable to analytical treatment. We obtain analytically the mean somatic membrane potential excursion due to a calcium spike being triggered while in the fluctuating regime. Our analytical form that accounts for the covariance between conductances and the membrane potential shows a better agreement with simulation results than a naive first order approximation.

Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron

The thick-tufted layer 5 (TTL5) pyramidal neuron is one of the most extensively studied neuron types in the mammalian neocortex and has become a benchmark for understanding information processing in excitatory neurons. By virtue of having the widest local axonal and dendritic arborization, the TTL5 neuron encompasses various local neocortical neurons and thereby defines the dimensions of neocortical microcircuitry. The TTL5 neuron integrates input across all neocortical layers and is the principal output pathway funneling information flow to subcortical structures. Several studies over the past decades have investigated the anatomy, physiology, synaptology, and pathophysiology of the TTL5 neuron. This review summarizes key discoveries and identifies potential avenues of research to facilitate an integrated and unifying understanding on the role of a central neuron in the neocortex.

Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing.

The spatiotemporal pattern of synaptic inputs is critical for synaptic integration and plasticity in neurons but whether these inputs are structured or random is not clear. Here the authors use in vivo calcium imaging to monitor the presynaptic activity of cerebellar parallel fibre axons and find clustered patterns of axonal activity during sensory processing.

Acetylcholine excites neocortical pyramidal neurons via nicotinic receptors

The neuromodulator acetylcholine (ACh) shapes neocortical function during sensory perception, motor control, arousal, attention, learning, and memory. Here we investigate the mechanisms by which ACh affects neocortical pyramidal neurons in adult mice. Stimulation of cholinergic axons activated muscarinic and nicotinic ACh receptors on pyramidal neurons in all cortical layers and in multiple cortical areas. Nicotinic receptor activation evoked short-latency, depolarizing postsynaptic potentials (PSPs) in many pyramidal neurons. Nicotinic receptor-mediated PSPs promoted spiking of pyramidal neurons. The duration of the increase in spiking was membrane potential dependent, with nicotinic receptor activation triggering persistent spiking lasting many seconds in neurons close to threshold. Persistent spiking was blocked by intracellular BAPTA, indicating that nicotinic ACh receptor activation evoked persistent spiking via a long-lasting calcium-activated depolarizing current. We compared nicotinic PSPs in primary motor cortex (M1), prefrontal cortex (PFC), and visual cortex. The laminar pattern of nicotinic excitation was not uniform but was broadly similar across areas, with stronger modulation in deep than superficial layers. Superimposed on this broad pattern were local differences, with nicotinic PSPs being particularly large and common in layer 5 of M1 but not layer 5 of PFC or primary visual cortex (V1). Hence, in addition to modulating the excitability of pyramidal neurons in all layers via muscarinic receptors, synaptically released ACh preferentially increases the activity of deep-layer neocortical pyramidal neurons via nicotinic receptors, thereby adding laminar selectivity to the widespread enhancement of excitability mediated by muscarinic ACh receptors.

Electrophysiological properties of genetically identified subtypes of layer 5 neocortical pyramidal neurons: Ca²⁺ dependence and differential modulation by norepinephrine

We studied neocortical pyramidal neurons from two lines of bacterial artificial chromosome mice (etv1 and glt; Gene Expression Nervous System Atlas: GENSAT project), each of which expresses enhanced green fluorescent protein (EGFP) in a different subpopulation of layer 5 pyramidal neurons. In barrel cortex, etv1 and glt pyramidal cells were previously reported to differ in terms of their laminar distribution, morphology, thalamic inputs, cellular targets, and receptive field size. In this study, we measured the laminar distribution of etv1 and glt cells. On average, glt cells were located more deeply; however, the distributions of etv1 and glt cells extensively overlap in layer 5. To test whether these two cell types differed in electrophysiological properties that influence firing behavior, we prepared acute brain slices from 2-4-wk-old mice, where EGFP-positive cells in somatosensory cortex were identified under epifluorescence and then studied using whole cell current- or voltage-clamp recordings. We studied the details of action potential parameters and repetitive firing, characterized by the larger slow afterhyperpolarizations (AHPs) in etv1 neurons and larger medium AHPs (mAHPS) in glt cells, and compared currents underlying the mAHP and slow AHP (sAHP) in etv1 and glt neurons. Etv1 cells exhibited lower dV/dt for spike polarization and repolarization and reduced direct current (DC) gain (lower f-I slope) for repetitive firing than glt cells. Most importantly, we found that 1) differences in the expression of Ca(2+)-dependent K(+) conductances (small-conductance calcium-activated potassium channels and sAHP channels) determine major functional differences between etv1 and glt cells, and 2) there is differential modulation of etv1 and glt neurons by norepinephrine.

A nonlinear cable framework for bidirectional synaptic plasticity

Finding the rules underlying how axons of cortical neurons form neural circuits and modify their corresponding synaptic strength is the still subject of intense research. Experiments have shown that internal calcium concentration, and both the precise timing and temporal order of pre and postsynaptic action potentials, are important constituents governing whether the strength of a synapse located on the dendrite is increased or decreased. In particular, previous investigations focusing on spike timing-dependent plasticity (STDP) have typically observed an asymmetric temporal window governing changes in synaptic efficacy. Such a temporal window emphasizes that if a presynaptic spike, arriving at the synaptic terminal, precedes the generation of a postsynaptic action potential, then the synapse is potentiated; however if the temporal order is reversed, then depression occurs. Furthermore, recent experimental studies have now demonstrated that the temporal window also depends on the dendritic location of the synapse. Specifically, it was shown that in distal regions of the apical dendrite, the magnitude of potentiation was smaller and the window for depression was broader, when compared to observations from the proximal region of the dendrite. To date, the underlying mechanism(s) for such a distance-dependent effect is (are) currently unknown. Here, using the ionic cable theory framework in conjunction with the standard calcium based plasticity model, we show for the first time that such distance-dependent inhomogeneities in the temporal learning window for STDP can be largely explained by both the spatial and active properties of the dendrite.

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

Frequency-dependent signal processing in apical dendrites of hippocampal CA1 pyramidal cells

Depending on an animal's behavioral state, hippocampal CA1 pyramidal cells receive distinct patterns of excitatory and inhibitory synaptic inputs. The time-dependent changes in the frequencies of these inputs and the nonuniform distribution of voltage-gated channels lead to dynamic fluctuations in membrane conductance. In this study, using a whole-cell patch-clamp method, we attempted to record and analyze the frequency dependencies of membrane responsiveness in Wistar rat hippocampal CA1 pyramidal cells following noise current injection directly into dendrites and somata under pharmacological blockade of all synaptic inputs. To estimate the frequency-dependent properties of membrane potential, membrane impedance was determined from the voltage response divided by the input current in the frequency domain. The cell membrane of most neurons showed low-pass filtering properties in all regions. In particular, the properties were strongly expressed in the somata or proximal dendrites. Moreover, the data revealed nonuniform distribution of dendritic impedance, which was high in the intermediate segment of the apical dendritic shaft (∼220-260μm from the soma). The low-pass filtering properties in the apical dendrites were more enhanced by membrane depolarization than those in the somata. Coherence spectral analysis revealed high coherence between the input signal and the output voltage response in the theta-gamma frequency range, and large lags emerged in the distal dendrites in the gamma frequency range. Our results suggest that apical dendrites of hippocampal CA1 pyramidal cells integrate synaptic inputs according to the frequency components of the input signal along the dendritic segments receiving the inputs.

Spike-timing prediction in cortical neurons with active dendrites

A complete single-neuron model must correctly reproduce the firing of spikes and bursts. We present a study of a simplified model of deep pyramidal cells of the cortex with active dendrites. We hypothesized that we can model the soma and its apical dendrite with only two compartments, without significant loss in the accuracy of spike-timing predictions. The model is based on experimentally measurable impulse-response functions, which transfer the effect of current injected in one compartment to current reaching the other. Each compartment was modeled with a pair of non-linear differential equations and a small number of parameters that approximate the Hodgkin-and-Huxley equations. The predictive power of this model was tested on electrophysiological experiments where noisy current was injected in both the soma and the apical dendrite simultaneously. We conclude that a simple two-compartment model can predict spike times of pyramidal cells stimulated in the soma and dendrites simultaneously. Our results support that regenerating activity in the apical dendritic is required to properly account for the dynamics of layer 5 pyramidal cells under in-vivo-like conditions.

Unsupervised learnable neuron model with nonlinear interaction on dendrites

Recent researches have provided strong circumstantial support to dendrites playing a key and possibly essential role in computations. In this paper, we propose an unsupervised learnable neuron model by including the nonlinear interactions between excitation and inhibition on dendrites. The model neuron self-adjusts its synaptic parameters, so that the synapse to dendrite, according to a generalized delta-rule-like algorithm. The model is used to simulate directionally selective cells by the unsupervised learning algorithm. In the simulations, we initialize the interaction and dendrite of the neuron randomly and use the generalized delta-rule-like unsupervised learning algorithm to learn the two-dimensional multi-directional selectivity problem without an external teacher's signals. Simulation results show that the directionally selective cells can be formed by unsupervised learning, acquiring the required number of dendritic branches, and if needed, enhanced and if not, eliminated. Further, the results show whether a synapse exists; if it exists, where and what type (excitatory or inhibitory) of synapse it is. This leads us to believe that the proposed neuron model may be considerably more powerful on computations than the McCulloch-Pitts model because theoretically a single neuron or a single layer of such neurons is capable of solving any complex problem. These may also lead to a completely new technique for analyzing the mechanisms and principles of neurons, dendrites, and synapses.