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

Effects of dendritic Ca2+ spike on the modulation of spike timing with transcranial direct current stimulation in cortical pyramidal neurons

Transcranial direct current stimulation (tDCS) generates a weak electric field (EF) within the brain, which induces opposite polarization in the soma and distal dendrite of cortical pyramidal neurons. The somatic polarization directly affects the spike timing, and dendritic polarization modulates the synaptically evoked dendritic activities. Ca2+ spike, the most dramatic dendritic activity, is crucial for synaptic integration and top-down signal transmission, thereby indirectly influencing the output spikes of pyramidal cells. Nevertheless, the role of dendritic Ca2+ spike in the modulation of neural spike timing with tDCS remains largely unclear. In this study, we use morphologically and biophysically realistic models of layer 5 pyramidal cells (L5 PCs) to simulate the dendritic Ca2+ spike and somatic Na+ spike in response to distal dendritic synaptic inputs under weak EF stimulation. Our results show that weak EFs modulate the spike timing through the modulation of dendritic Ca2+ spike and somatic polarization, and such field effects are dependent on synaptic inputs. At weak synaptic inputs, the spike timing is advanced due to the facilitation of dendritic Ca2+ spike by field-induced dendritic depolarization. Conversely, it is delayed by field-induced dendritic hyperpolarization. In this context, the Ca2+ spike exhibits heightened sensitivity to weak EFs, thereby governing the changes in spike timing. At strong synaptic inputs, somatic polarization dominates the changes in spike timing due to the decreased sensitivity of Ca2+ spike to EFs. Consequently, the spike timing is advanced/delayed by field-induced somatic depolarization/hyperpolarization. Moreover, EFs have significant effects on the changes in the timing of somatic spike and Ca2+ spike when synaptic current injection coincides with the onset of EFs. Field effects on spike timing follow a cosine dependency on the field polar angle, with maximum effects in the field direction parallel to the somato-dendritic axis. Furthermore, our results are robust to morphological and biological diversity. These findings clarify the modulation of spike timing with weak EFs and highlight the crucial role of dendritic Ca2+ spike. These predictions shed light on the neural basis of tDCS and should be considered when understanding the effect of tDCS on population dynamics and cognitive behavior.

Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3

Apical and basal dendrites of pyramidal neurons receive anatomically and functionally distinct inputs, implying compartment-level functional diversity during behavior. To test this, we imaged in vivo calcium signals from soma, apical dendrites, and basal dendrites in mouse hippocampal CA3 pyramidal neurons during head-fixed navigation. To capture compartment-specific population dynamics, we developed computational tools to automatically segment dendrites and extract accurate fluorescence traces from densely labeled neurons. We validated the method on sparsely labeled preparations and synthetic data, predicting an optimal labeling density for high experimental throughput and analytical accuracy. Our method detected rapid, local dendritic activity. Dendrites showed robust spatial tuning, similar to soma but with higher activity rates. Across days, apical dendrites remained more stable and outperformed in decoding of the animal's position. Thus, population-level apical and basal dendritic differences may reflect distinct compartment-specific input-output functions and computations in CA3. These tools will facilitate future studies mapping sub-cellular activity and their relation to behavior.

Learning enhances behaviorally relevant representations in apical dendrites

Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of $4\times 4\,{\text{mm}^{2}}$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around $50\,\text{Hz}$ may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.

Neural ensembles: role of intrinsic excitability and its plasticity

Synaptic connectivity defines groups of neurons that engage in correlated activity during specific functional tasks. These co-active groups of neurons form ensembles, the operational units involved in, for example, sensory perception, motor coordination and memory (then called an engram). Traditionally, ensemble formation has been thought to occur via strengthening of synaptic connections via long-term potentiation (LTP) as a plasticity mechanism. This synaptic theory of memory arises from the learning rules formulated by Hebb and is consistent with many experimental observations. Here, we propose, as an alternative, that the intrinsic excitability of neurons and its plasticity constitute a second, non-synaptic mechanism that could be important for the initial formation of ensembles. Indeed, enhanced neural excitability is widely observed in multiple brain areas subsequent to behavioral learning. In cortical structures and the amygdala, excitability changes are often reported as transient, even though they can last tens of minutes to a few days. Perhaps it is for this reason that they have been traditionally considered as modulatory, merely supporting ensemble formation by facilitating LTP induction, without further involvement in memory function (memory allocation hypothesis). We here suggest-based on two lines of evidence-that beyond modulating LTP allocation, enhanced excitability plays a more fundamental role in learning. First, enhanced excitability constitutes a signature of active ensembles and, due to it, subthreshold synaptic connections become suprathreshold in the absence of synaptic plasticity (iceberg model). Second, enhanced excitability promotes the propagation of dendritic potentials toward the soma and allows for enhanced coupling of EPSP amplitude (LTP) to the spike output (and thus ensemble participation). This permissive gate model describes a need for permanently increased excitability, which seems at odds with its traditional consideration as a short-lived mechanism. We propose that longer modifications in excitability are made possible by a low threshold for intrinsic plasticity induction, suggesting that excitability might be on/off-modulated at short intervals. Consistent with this, in cerebellar Purkinje cells, excitability lasts days to weeks, which shows that in some circuits the duration of the phenomenon is not a limiting factor in the first place. In our model, synaptic plasticity defines the information content received by neurons through the connectivity network that they are embedded in. However, the plasticity of cell-autonomous excitability could dynamically regulate the ensemble participation of individual neurons as well as the overall activity state of an ensemble.

The plasticity of pyramidal neurons in the behaving brain

Neurons are plastic. That is, they change their activity according to different behavioural conditions. This endows pyramidal neurons with an incredible computational power for the integration and processing of synaptic inputs. Plasticity can be investigated at different levels of investigation within a single neuron, from spines to dendrites, to synaptic input. Although most of our knowledge stems from the in vitro brain slice preparation, plasticity plays a vital role during behaviour by providing a flexible substrate for the execution of appropriate actions in our ever-changing environment. Owing to advances in recording techniques, the plasticity of neurons and the neural networks in which they are embedded is now beginning to be realized in the in vivo intact brain. This review focuses on the structural and functional synaptic plasticity of pyramidal neurons, with a specific focus on the latest developments from in vivo studies. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Multitask learning of a biophysically-detailed neuron model

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behavior of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. While LFP and EEG are an important downstream application, the main focus of this paper lies in predicting dendritic voltages within each compartment to capture the entire electrophysiology of a biophysically-detailed neuron model. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials.

Variation and convergence in the morpho-functional properties of the mammalian neocortex

Man's natural inclination to classify and hierarchize the living world has prompted neurophysiologists to explore possible differences in brain organisation between mammals, with the aim of understanding the diversity of their behavioural repertoires. But what really distinguishes the human brain from that of a platypus, an opossum or a rodent? In this review, we compare the structural and electrical properties of neocortical neurons in the main mammalian radiations and examine their impact on the functioning of the networks they form. We discuss variations in overall brain size, number of neurons, length of their dendritic trees and density of spines, acknowledging their increase in humans as in most large-brained species. Our comparative analysis also highlights a remarkable consistency, particularly pronounced in marsupial and placental mammals, in the cell typology, intrinsic and synaptic electrical properties of pyramidal neuron subtypes, and in their organisation into functional circuits. These shared cellular and network characteristics contribute to the emergence of strikingly similar large-scale physiological and pathological brain dynamics across a wide range of species. These findings support the existence of a core set of neural principles and processes conserved throughout mammalian evolution, from which a number of species-specific adaptations appear, likely allowing distinct functional needs to be met in a variety of environmental contexts.

A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.

Dysfunctions of cellular context-sensitivity in neurodevelopmental learning disabilities

Pyramidal neurons have a pivotal role in the cognitive capabilities of neocortex. Though they have been predominantly modeled as integrate-and-fire point processors, many of them have another point of input integration in their apical dendrites that is central to mechanisms endowing them with the sensitivity to context that underlies basic cognitive capabilities. Here we review evidence implicating impairments of those mechanisms in three major neurodevelopmental disabilities, fragile X, Down syndrome, and fetal alcohol spectrum disorders. Multiple dysfunctions of the mechanisms by which pyramidal cells are sensitive to context are found to be implicated in all three syndromes. Further deciphering of these cellular mechanisms would lead to the understanding of and therapies for learning disabilities beyond any that are currently available.

Intrinsic and synaptic determinants of receptive field plasticity in Purkinje cells of the mouse cerebellum

Non-synaptic (intrinsic) plasticity of membrane excitability contributes to aspects of memory formation, but it remains unclear whether it merely facilitates synaptic long-term potentiation or plays a permissive role in determining the impact of synaptic weight increase. We use tactile stimulation and electrical activation of parallel fibers to probe intrinsic and synaptic contributions to receptive field plasticity in awake mice during two-photon calcium imaging of cerebellar Purkinje cells. Repetitive activation of both stimuli induced response potentiation that is impaired in mice with selective deficits in either synaptic or intrinsic plasticity. Spatial analysis of calcium signals demonstrated that intrinsic, but not synaptic plasticity, enhances the spread of dendritic parallel fiber response potentiation. Simultaneous dendrite and axon initial segment recordings confirm these dendritic events affect axonal output. Our findings support the hypothesis that intrinsic plasticity provides an amplification mechanism that exerts a permissive control over the impact of long-term potentiation on neuronal responsiveness.

Morpho-electric diversity of human hippocampal CA1 pyramidal neurons

Hippocampal pyramidal neuron activity underlies episodic memory and spatial navigation. Although extensively studied in rodents, extremely little is known about human hippocampal pyramidal neurons, even though the human hippocampus underwent strong evolutionary reorganization and shows lower theta rhythm frequencies. To test whether biophysical properties of human Cornu Amonis subfield 1 (CA1) pyramidal neurons can explain observed rhythms, we map the morpho-electric properties of individual CA1 pyramidal neurons in human, non-pathological hippocampal slices from neurosurgery. Human CA1 pyramidal neurons have much larger dendritic trees than mouse CA1 pyramidal neurons, have a large number of oblique dendrites, and resonate at 2.9 Hz, optimally tuned to human theta frequencies. Morphological and biophysical properties suggest cellular diversity along a multidimensional gradient rather than discrete clustering. Across the population, dendritic architecture and a large number of oblique dendrites consistently boost memory capacity in human CA1 pyramidal neurons by an order of magnitude compared to mouse CA1 pyramidal neurons.

Impact of light-emitting diodes on visual cortex layer 5 pyramidal neurons (V1-L5PNs)-A rodent study

Purpose

Light-induced neural retinal insult leads to alterations in the visual cortex neurons. We examined light-induced neuronal alterations in the visual cortex layer 5 pyramidal neurons (V1-L5PNs) of adult male Wistar rats.

Methods

A total of 24 rats were divided into the following groups (n=6 each): control (NC), blue (BL), white (WL), and yellow (YL). The exposure groups were subjected to light-emitting diodes (LED) exposure (450-500 lx) of differing wavelengths for 90 days (12:12 16 light-dark cycle). After LED exposure, the animals were sacrificed, and the brain tissues were removed and impregnated in freshly prepared Golgi-Cox stain for 21 days. Sholl's grading analysis was used to quantify the apical and basal dendritic branching points and intersections of the V1-L5PNs.

Results

There was a significant difference in the number of apical branching points among all groups (p<0.001), with a particularly notable difference between the BL and WL groups (p<0.001). A post hoc test revealed that all exposure groups (BL, WL, and YL) had fewer apical branching points (p<0.001) on an average of 3.6 µm and a significant reduction in the dendritic intersections (p<0.001) compared to the number of branching points extending from layer Va (V1) neurons.

Conclusions

Chronic and cumulative exposure to blue and white LEDs led to the pruning of V1-L5PNs, which might impair visual processing.

Gene expression in the primate orbitofrontal cortex related to anxious temperament

Anxiety disorders are among the most prevalent psychiatric disorders, causing significant suffering and disability. Relative to other psychiatric disorders, anxiety disorders tend to emerge early in life, supporting the importance of developmental mechanisms in their emergence and maintenance. Behavioral inhibition (BI) is a temperament that emerges early in life and, when stable and extreme, is linked to an increased risk for the later development of anxiety disorders and other stress-related psychopathology. Understanding the neural systems and molecular mechanisms underlying this dispositional risk could provide insight into treatment targets for anxiety disorders. Nonhuman primates (NHPs) have an anxiety-related temperament, called anxious temperament (AT), that is remarkably similar to BI in humans, facilitating the design of highly translational models for studying the early risk for stress-related psychopathology. Because of the recent evolutionary divergence between humans and NHPs, many of the anxiety-related brain regions that contribute to psychopathology are highly similar in terms of their structure and function, particularly with respect to the prefrontal cortex. The orbitofrontal cortex plays a critical role in the flexible encoding and regulation of threat responses, in part through connections with subcortical structures like the amygdala. Here, we explore individual differences in the transcriptional profile of cells within the region, using laser capture microdissection and single nuclear sequencing, providing insight into the molecules underlying individual differences in AT-related function of the pOFC, with a particular focus on previously implicated cellular systems, including neurotrophins and glucocorticoid signaling.

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

Synaptic input and temperature influence sensory coding in a mechanoreceptor

Many neurons possess more than one spike initiation zone (SIZ), which adds to their computational power and functional flexibility. Integrating inputs from different origins is especially relevant for sensory neurons that rely on relative spike timing for encoding sensory information. Yet, it is poorly understood if and how the propagation of spikes generated at one SIZ in response to sensory stimulation is affected by synaptic inputs triggering activity of other SIZ, and by environmental factors like temperature. The mechanosensory Touch (T) cell in the medicinal leech is an ideal model system to study these potential interactions because it allows intracellular recording and stimulation of its soma while simultaneously touching the skin in a body-wall preparation. The T cell reliably elicits spikes in response to somatic depolarization, as well as to tactile skin stimulation. Latencies of spikes elicited in the skin vary across cells, depending on the touch location relative to the cell's receptive field. However, repetitive stimulation reveals that tactilely elicited spikes are more precisely timed than spikes triggered by somatic current injection. When the soma is hyperpolarized to mimic inhibitory synaptic input, first spike latencies of tactilely induced spikes increase. If spikes from both SIZ follow shortly after each other, the arrival time of the second spike at the soma can be delayed. Although the latency of spikes increases by the same factor when the temperature decreases, the effect is considerably stronger for the longer absolute latencies of spikes propagating from the skin to the soma. We therefore conclude that the propagation time of spikes from the skin is modulated by internal factors like synaptic inputs, and by external factors like temperature. Moreover, fewer spikes are detected when spikes from both origins are expected to arrive at the soma in temporal proximity. Hence, the leech T cell might be a key for understanding how the interaction of multiple SIZ impacts temporal and rate coding of sensory information, and how cold-blooded animals can produce adequate behavioral responses to sensory stimuli based on temperature-dependent relative spike timing.

Intrinsic and synaptic determinants of receptive field plasticity in Purkinje cells of the mouse cerebellum

Non-synaptic ('intrinsic') plasticity of membrane excitability contributes to aspects of memory formation, but it remains unclear whether it merely facilitates synaptic long-term potentiation (LTP), or whether it plays a permissive role in determining the impact of synaptic weight increase. We use tactile stimulation and electrical activation of parallel fibers to probe intrinsic and synaptic contributions to receptive field (RF) plasticity in awake mice during two-photon calcium imaging of cerebellar Purkinje cells. Repetitive activation of both stimuli induced response potentiation that is impaired in mice with selective deficits in either intrinsic plasticity (SK2 KO) or LTP (CaMKII TT305/6VA). Intrinsic, but not synaptic, plasticity expands the local, dendritic RF representation. Simultaneous dendrite and axon initial segment recordings confirm that these dendritic events affect axonal output. Our findings support the hypothesis that intrinsic plasticity provides an amplification mechanism that exerts a permissive control over the impact of LTP on neuronal responsiveness.

The h-current controls cortical recurrent network activity through modulation of dendrosomatic communication

A fundamental feature of the cerebral cortex is the ability to rapidly turn on and off maintained activity within ensembles of neurons through recurrent excitation balanced by inhibition. Here we demonstrate that reduction of the h-current, which is especially prominent in pyramidal cell dendrites, strongly increases the ability of local cortical networks to generate maintained recurrent activity. Reduction of the h-current resulted in hyperpolarization and increase in input resistance of both the somata and apical dendrites of layer 5 pyramidal cells, while strongly increasing the dendrosomatic transfer of low (<20 Hz) frequencies, causing an increased responsiveness to dynamic clamp-induced recurrent network-like activity injected into the dendrites and substantially increasing the duration of spontaneous Up states. We propose that modulation of the h-current may strongly control the ability of cortical networks to generate recurrent persistent activity and the formation and dissolution of neuronal ensembles.

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

The apical dendrites of pyramidal neurons in sensory cortex receive primarily top-down signals from associative and motor regions, while cell bodies and nearby dendrites are heavily targeted by locally recurrent or bottom-up inputs from the sensory periphery. Based on these differences, a number of theories in computational neuroscience postulate a unique role for apical dendrites in learning. However, due to technical challenges in data collection, little data is available for comparing the responses of apical dendrites to cell bodies over multiple days. Here we present a dataset collected through the Allen Institute Mindscope's OpenScope program that addresses this need. This dataset comprises high-quality two-photon calcium imaging from the apical dendrites and the cell bodies of visual cortical pyramidal neurons, acquired over multiple days in awake, behaving mice that were presented with visual stimuli. Many of the cell bodies and dendrite segments were tracked over days, enabling analyses of how their responses change over time. This dataset allows neuroscientists to explore the differences between apical and somatic processing and plasticity.

Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3

Anatomically segregated apical and basal dendrites of pyramidal neurons receive functionally distinct inputs, but it is unknown if this results in compartment-level functional diversity during behavior. Here we imaged calcium signals from apical dendrites, soma, and basal dendrites of pyramidal neurons in area CA3 of mouse hippocampus during head-fixed navigation. To examine dendritic population activity, we developed computational tools to identify dendritic regions of interest and extract accurate fluorescence traces. We identified robust spatial tuning in apical and basal dendrites, similar to soma, though basal dendrites had reduced activity rates and place field widths. Across days, apical dendrites were more stable than soma or basal dendrites, resulting in better decoding of the animal's position. These population-level dendritic differences may reflect functionally distinct input streams leading to different dendritic computations in CA3. These tools will facilitate future studies of signal transformations between cellular compartments and their relation to behavior.

Higher-order thalamic nuclei facilitate the generalization and maintenance of spike-and-wave discharges of absence seizures

Spike-and-wave discharges (SWDs), generated by the cortico-thalamo-cortical (CTC) network, are pathological, large amplitude oscillations and the hallmark of absence seizures (ASs). SWDs begin in a cortical initiation network in both humans and animal models, including the Genetic Absence Epilepsy Rats from Strasbourg (GAERS), where it is located in the primary somatosensory cortex (S1). The behavioral manifestation of an AS occurs when SWDs spread from the cortical initiation site to the whole brain, however, the mechanisms behind this rapid propagation remain unclear. Here we investigated these processes beyond the principal CTC network, in higher-order (HO) thalamic nuclei (lateral posterior (LP) and posterior (PO) nuclei) since their diffuse connectivity and known facilitation of intracortical communications make these nuclei key candidates to support SWD generation and maintenance. In freely moving GAERS, multi-site LFP in LP, PO and multiple cortical regions revealed a novel feature of SWDs: during SWDs there are short periods (named SWD-breaks) when cortical regions far from S1, such the primary visual cortex (V1), become transiently unsynchronized from the ongoing EEG rhythm. Inactivation of HO nuclei with local muscimol injections or optogenetic perturbation of HO nuclei activity increased the occurrence of SWD-breaks and the former intervention also increased the SWD propagation-time from S1. The neural underpinnings of these findings were explored further by silicon probe recordings from single units of PO which uncovered two previously unknown groups of excitatory neurons based on their burst firing dynamics at SWD onset. Moreover, a switch from tonic to burst firing at SWD onset was shown to be an important feature since it was much less prominent for non-generalized events, i.e. SWDs that remained local to S1. Additionally, one group of neurons showed a reverse of this switch during SWD-breaks, demonstrating the importance of this firing pattern throughout the SWD. In summary, these results support the view that multiple HO thalamic nuclei are utilized at SWD onset and contribute to cortical synchrony throughout the paroxysmal discharge.

Paired optogenetic and visual stimulation can change the orientation selectivity of visual cortex neurons

Synaptic plasticity is currently considered the main mechanism underlying the plastic modification of neural networks. The vast majority of studies of synaptic plasticity are carried out on reduced preparations, but the situation in vivo is fundamentally different from that in vitro. In this work, we used the Hebbian paradigm, which is known to induce long-term changes in synaptic strength in vitro, to manipulate the properties of a single pyramidal neuron in the mouse visual cortex. We have shown that optogenetic stimulation of a ChR2-expressing pyramidal neuron in the primary visual cortex of Thy-ChR2 mice paired with the presentation of a visual stimulus of non-optimal orientation induces long-term changes in the properties of the receptive field, manifested in alteration of the orientation selectivity of the cell. Non-paired stimulation did not lead to changes in the properties of the receptive field of the neuron during the experiment. Thus, we have demonstrated the role of associative plasticity in the dynamic organization of the receptive fields of neurons in the visual cortex.

Processing of cell assemblies in the lateral entorhinal cortex

There is evidence that olfactory cortex responds to its afferent input with the generation of cell assemblies: collections of principal neurons that fire together over a time scale of tens of ms. If such assemblies form an odor representation, then a fundamental question is how each assembly then induces neuronal activity in downstream structures. We have addressed this question in a detailed model of superficial layers of lateral entorhinal cortex, a recipient of input from olfactory cortex and olfactory bulb. Our results predict that the response of the fan cell subpopulation can be approximated by a relatively simple Boolean process, somewhat along the lines of the McCulloch/Pitts scheme; this is the case because of the sparsity of recurrent excitation amongst fan cells. However, because of recurrent excitatory connections between layer 2 and layer 3 pyramidal cells, synaptic and probably also gap junctional, the response of pyramidal cell subnetworks cannot be so approximated. Because of the highly structured anatomy of entorhinal output projections, our model suggests that downstream targets of entorhinal cortex (dentate gyrus, hippocampal CA3, CA1, piriform cortex, olfactory bulb) receive differentially processed information.

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 10⁶ neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin.

Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for.

The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

Understanding the brain’s function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanity’s great undertakings. In experimental and clinical work probing the brain’s activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

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

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

Layer-specific pyramidal neuron properties underlie diverse anterior cingulate cortical motor and limbic networks

The laminar cellular and circuit mechanisms by which the anterior cingulate cortex (ACC) exerts flexible control of motor and affective information for goal-directed behavior have not been elucidated. Using multimodal tract-tracing, in vitro patch-clamp recording and computational approaches in rhesus monkeys (M. mulatta), we provide evidence that specialized motor and affective network dynamics can be conferred by layer-specific biophysical and structural properties of ACC pyramidal neurons targeting two key downstream structures -the dorsal premotor cortex (PMd) and the amygdala (AMY). AMY-targeting neurons exhibited significant laminar differences, with L5 more excitable (higher input resistance and action potential firing rates) than L3 neurons. Between-pathway differences were found within L5, with AMY-targeting neurons exhibiting greater excitability, apical dendritic complexity, spine densities, and diversity of inhibitory inputs than PMd-targeting neurons. Simulations using a pyramidal-interneuron network model predict that these layer- and pathway-specific single-cell differences contribute to distinct network oscillatory dynamics. L5 AMY-targeting networks are more tuned to slow oscillations well-suited for affective and contextual processing timescales, while PMd-targeting networks showed strong beta/gamma synchrony implicated in rapid sensorimotor processing. These findings are fundamental to our broad understanding of how layer-specific cellular and circuit properties can drive diverse laminar activity found in flexible behavior.

The neural hierarchy of consciousness

The chief undertaking in the studies of consciousness is that of unravelling "the minimal set of neural processes that are together sufficient for the conscious experience of a particular content - the neural correlates of consciousness". To this day, this crusade remains at an impasse, with a clash of two main theories: consciousness may arise either in a graded and cortically-localised fashion, or in an all-or-none and widespread one. In spite of the long-lasting theoretical debates, neurophysiological theories of consciousness have been mostly dissociated from them. Herein, a theoretical review will be put forth with the aim to change that. In its first half, we will cover the hard available evidence on the neurophysiology of consciousness, whereas in its second half we will weave a series of considerations on both theories and substantiate a novel take on conscious awareness: the levels of processing approach, partitioning the conscious architecture into lower- and higher-order, graded and nonlinear.

The guide to dendritic spikes of the mammalian cortex in vitro and in vivo

Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.

Allometric rules for mammalian cortical layer 5 neuron biophysics

The biophysical properties of neurons are the foundation for computation in the brain. Neuronal size is a key determinant of single neuron input-output features and varies substantially across species^1-3. However, it is unknown whether different species adapt neuronal properties to conserve how single neurons process information^4-7. Here we characterize layer 5 cortical pyramidal neurons across 10 mammalian species to identify the allometric relationships that govern how neuronal biophysics change with cell size. In 9 of the 10 species, we observe conserved rules that control the conductance of voltage-gated potassium and HCN channels. Species with larger neurons, and therefore a decreased surface-to-volume ratio, exhibit higher membrane ionic conductances. This relationship produces a conserved conductance per unit brain volume. These size-dependent rules result in large but predictable changes in somatic and dendritic integrative properties. Human neurons do not follow these allometric relationships, exhibiting much lower voltage-gated potassium and HCN conductances. Together, our results in layer 5 neurons identify conserved evolutionary principles for neuronal biophysics in mammals as well as notable features of the human cortex.

What is the dynamical regime of cerebral cortex?

Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size with the factors that cancel, rather than tight, meaning that the net input is very small relative to the canceling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas.

Burst control: Synaptic conditions for burst generation in cortical layer 5 pyramidal neurons

The output of neocortical layer 5 pyramidal cells (L5PCs) is expressed by a train of single spikes with intermittent bursts of multiple spikes at high frequencies. The bursts are the result of nonlinear dendritic properties, including Na+, Ca2+, and NMDA spikes, that interact with the ~10,000 synapses impinging on the neuron's dendrites. Output spike bursts are thought to implement key dendritic computations, such as coincidence detection of bottom-up inputs (arriving mostly at the basal tree) and top-down inputs (arriving mostly at the apical tree). In this study we used a detailed nonlinear model of L5PC receiving excitatory and inhibitory synaptic inputs to explore the conditions for generating bursts and for modulating their properties. We established the excitatory input conditions on the basal versus the apical tree that favor burst and show that there are two distinct types of bursts. Bursts consisting of 3 or more spikes firing at < 200 Hz, which are generated by stronger excitatory input to the basal versus the apical tree, and bursts of ~2-spikes at ~250 Hz, generated by prominent apical tuft excitation. Localized and well-timed dendritic inhibition on the apical tree differentially modulates Na+, Ca2+, and NMDA spikes and, consequently, finely controls the burst output. Finally, we explored the implications of different burst classes and respective dendritic inhibition for regulating synaptic plasticity.

Apical amplification-a cellular mechanism of conscious perception?

We present a theoretical view of the cellular foundations for network-level processes involved in producing our conscious experience. Inputs to apical synapses in layer 1 of a large subset of neocortical cells are summed at an integration zone near the top of their apical trunk. These inputs come from diverse sources and provide a context within which the transmission of information abstracted from sensory input to their basal and perisomatic synapses can be amplified when relevant. We argue that apical amplification enables conscious perceptual experience and makes it more flexible, and thus more adaptive, by being sensitive to context. Apical amplification provides a possible mechanism for recurrent processing theory that avoids strong loops. It makes the broadcasting hypothesized by global neuronal workspace theories feasible while preserving the distinct contributions of the individual cells receiving the broadcast. It also provides mechanisms that contribute to the holistic aspects of integrated information theory. As apical amplification is highly dependent on cholinergic, aminergic, and other neuromodulators, it relates the specific contents of conscious experience to global mental states and to fluctuations in arousal when awake. We conclude that apical dendrites provide a cellular mechanism for the context-sensitive selective amplification that is a cardinal prerequisite of conscious perception.

EMULATING DENDRITIC COMPUTING PARADIGMS ON ANALOG NEUROMORPHIC HARDWARE

BrainScaleS-2 is an accelerated and highly configurable neuromorphic system with physical models of neurons and synapses. Beyond networks of spiking point neurons, it allows for the implementation of user-defined neuron morphologies. Both passive propagation of electric signals between compartments as well as dendritic spikes and plateau potentials can be emulated. In this paper, three multi-compartment neuron morphologies are chosen to demonstrate passive propagation of postsynaptic potentials, spatio-temporal coincidence detection of synaptic inputs in a dendritic branch, and the replication of the BAC burst firing mechanism found in layer 5 pyramidal neurons of the neocortex.

Rethinking Single Neuron Electrical Compartmentalization: Dendritic Contributions to Network Computation In Vivo

Decades of experimental and theoretical work support a now well-established theory that active dendritic processing contributes to the computational power of individual neurons. This theory is based on the high degree of electrical compartmentalization observed in the dendrites of single neurons in ex vivo preparations. Compartmentalization allows dendrites to conduct semi-independent operations on their inputs before final integration and output at the axon, producing a "network-in-a-neuron." However, recent in vivo functional imaging experiments in mouse cortex have reported surprisingly little evidence for strong dendritic compartmentalization. In this review, we contextualize these new findings and discuss their impact on the future of the field. Specifically, we consider how highly coordinated, and thus less compartmentalized, activity in soma and dendrites can contribute to cortical computations including nonlinear mixed selectivity, prediction/expectation, multiplexing, and credit assignment.

Sex-Specific Disruption of Distinct mPFC Inhibitory Neurons in Spared-Nerve Injury Model of Neuropathic Pain

The medial prefrontal cortex (mPFC) modulates a range of behaviors, including responses to noxious stimuli. While various pain modalities alter mPFC function, our understanding of changes to specific cell types underlying pain-induced mPFC dysfunction remains incomplete. Proper activity of cortical GABAergic interneurons is essential for normal circuit function. We find that nerve injury increases excitability of layer 5 parvalbumin-expressing neurons in the prelimbic (PL) region of the mPFC from male, but not female, mice. Conversely, nerve injury dampens excitability in somatostatin-expressing neurons in layer 2/3 of the PL region; however, effects are differential between males and females. Nerve injury slightly increases the frequency of spontaneous excitatory post-synaptic currents (sEPSCs) in layer 5 parvalbumin-expressing neurons in males but reduces frequency of sEPSCs in layer 2/3 somatostatin-expressing neurons in females. Our findings provide key insight into how nerve injury drives maladaptive and sex-specific alterations to GABAergic circuits in cortical regions implicated in chronic pain.

Illusions, Delusions, and Your Backwards Bayesian Brain: A Biased Visual Perspective

The retinal image is insufficient for determining what is "out there," because many different real-world geometries could produce any given retinal image. Thus, the visual system must infer which external cause is most likely, given both the sensory data and prior knowledge that is either innate or learned via interactions with the environment. We will describe a general framework of "hierarchical Bayesian inference" that we and others have used to explore the role of cortico-cortical feedback in the visual system, and we will further argue that this approach to "seeing" makes our visual systems prone to perceptual errors in a variety of different ways. In this deliberately provocative and biased perspective, we argue that the neuromodulator, dopamine, may be a crucial link between neural circuits performing Bayesian inference and the perceptual idiosyncrasies of people with schizophrenia.

Sodium channelopathies of skeletal muscle and brain

Voltage-gated sodium channels initiate action potentials in nerve, skeletal muscle, and other electrically excitable cells. Mutations in them cause a wide range of diseases. These channelopathy mutations affect every aspect of sodium channel function, including voltage sensing, voltage-dependent activation, ion conductance, fast and slow inactivation, and both biosynthesis and assembly. Mutations that cause different forms of periodic paralysis in skeletal muscle were discovered first and have provided a template for understanding structure, function, and pathophysiology at the molecular level. More recent work has revealed multiple sodium channelopathies in the brain. Here we review the well-characterized genetics and pathophysiology of the periodic paralyses of skeletal muscle and then use this information as a foundation for advancing our understanding of mutations in the structurally homologous α-subunits of brain sodium channels that cause epilepsy, migraine, autism, and related comorbidities. We include studies based on molecular and structural biology, cell biology and physiology, pharmacology, and mouse genetics. Our review reveals unexpected connections among these different types of sodium channelopathies.

Neocortical Layer 1: An Elegant Solution to Top-Down and Bottom-Up Integration

Many of our daily activities, such as riding a bike to work or reading a book in a noisy cafe, and highly skilled activities, such as a professional playing a tennis match or a violin concerto, depend upon the ability of the brain to quickly make moment-to-moment adjustments to our behavior in response to the results of our actions. Particularly, they depend upon the ability of the neocortex to integrate the information provided by the sensory organs (bottom-up information) with internally generated signals such as expectations or attentional signals (top-down information). This integration occurs in pyramidal cells (PCs) and their long apical dendrite, which branches extensively into a dendritic tuft in layer 1 (L1). The outermost layer of the neocortex, L1 is highly conserved across cortical areas and species. Importantly, L1 is the predominant input layer for top-down information, relayed by a rich, dense mesh of long-range projections that provide signals to the tuft branches of the PCs. Here, we discuss recent progress in our understanding of the composition of L1 and review evidence that L1 processing contributes to functions such as sensory perception, cross-modal integration, controlling states of consciousness, attention, and learning.

A Minimal Biophysical Model of Neocortical Pyramidal Cells: Implications for Frontal Cortex Microcircuitry and Field Potential Generation

Ca²⁺ spikes initiated in the distal trunk of layer 5 pyramidal cells (PCs) underlie nonlinear dynamic changes in the gain of cellular response, critical for top-down control of cortical processing. Detailed models with many compartments and dozens of ionic channels can account for this Ca²⁺ spike-dependent gain and associated critical frequency. However, current models do not account for all known Ca²⁺-dependent features. Previous attempts to include more features have required increasing complexity, limiting their interpretability and utility for studying large population dynamics. We overcome these limitations in a minimal two-compartment biophysical model. In our model, a basal-dendrites/somatic compartment included fast-inactivating Na⁺ and delayed-rectifier K⁺ conductances, while an apical-dendrites/trunk compartment included persistent Na⁺, hyperpolarization-activated cation (I _h ), slow-inactivating K⁺, muscarinic K⁺, and Ca²⁺ L-type. The model replicated the Ca²⁺ spike morphology and its critical frequency plus three other defining features of layer 5 PC synaptic integration: linear frequency-current relationships, back-propagation-activated Ca²⁺ spike firing, and a shift in the critical frequency by blocking I _h Simulating 1000 synchronized layer 5 PCs, we reproduced the current source density patterns evoked by Ca²⁺ spikes and describe resulting medial-frontal EEG on a male macaque monkey. We reproduced changes in the current source density when I _h was blocked. Thus, a two-compartment model with five crucial ionic currents in the apical dendrites reproduces all features of these neurons. We discuss the utility of this minimal model to study the microcircuitry of agranular areas of the frontal lobe involved in cognitive control and responsible for event-related potentials, such as the error-related negativity.SIGNIFICANCE STATEMENT A minimal model of layer 5 pyramidal cells replicates all known features crucial for distal synaptic integration in these neurons. By redistributing voltage-gated and returning transmembrane currents in the model, we establish a theoretical framework for the investigation of cortical microcircuit contribution to intracranial local field potentials and EEG. This tractable model will enable biophysical evaluation of multiscale electrophysiological signatures and computational investigation of cortical processing.

General Anesthesia Decouples Cortical Pyramidal Neurons

The mystery of general anesthesia is that it specifically suppresses consciousness by disrupting feedback signaling in the brain, even when feedforward signaling and basic neuronal function are left relatively unchanged. The mechanism for such selectiveness is unknown. Here we show that three different anesthetics have the same disruptive influence on signaling along apical dendrites in cortical layer 5 pyramidal neurons in mice. We found that optogenetic depolarization of the distal apical dendrites caused robust spiking at the cell body under awake conditions that was blocked by anesthesia. Moreover, we found that blocking metabotropic glutamate and cholinergic receptors had the same effect on apical dendrite decoupling as anesthesia or inactivation of the higher-order thalamus. If feedback signaling occurs predominantly through apical dendrites, the cellular mechanism we found would explain not only how anesthesia selectively blocks this signaling but also why conscious perception depends on both cortico-cortical and thalamo-cortical connectivity.

Cellular Mechanisms of Conscious Processing

Recent breakthroughs in neurobiology indicate that the time is ripe to understand how cellular-level mechanisms are related to conscious experience. Here, we highlight the biophysical properties of pyramidal cells, which allow them to act as gates that control the evolution of global activation patterns. In conscious states, this cellular mechanism enables complex sustained dynamics within the thalamocortical system, whereas during unconscious states, such signal propagation is prohibited. We suggest that the hallmark of conscious processing is the flexible integration of bottom-up and top-down data streams at the cellular level. This cellular integration mechanism provides the foundation for Dendritic Information Theory, a novel neurobiological theory of consciousness.

I-waves in motor cortex revisited

I-waves represent high-frequency (~ 600 Hz) repetitive discharge of corticospinal fibers elicited by single-pulse stimulation of motor cortex. First detected and examined in animal preparations, this multiple discharge can also be recorded in humans from the corticospinal tract with epidural spinal electrodes. The exact underpinning neurophysiology of I-waves is still unclear, but there is converging evidence that they originate at the cortical level through synaptic input from specific excitatory interneuronal circuitries onto corticomotoneuronal cells, controlled by GABAAergic interneurons. In contrast, there is at present no supportive evidence for the alternative hypothesis that I-waves are generated by high-frequency oscillations of the membrane potential of corticomotoneuronal cells upon initial strong depolarization. Understanding I-wave physiology is essential for understanding how TMS activates the motor cortex.

Objective Morphological Classification of Neocortical Pyramidal Cells

A consensus on the number of morphologically different types of pyramidal cells (PCs) in the neocortex has not yet been reached, despite over a century of anatomical studies, due to the lack of agreement on the subjective classifications of neuron types, which is based on expert analyses of neuronal morphologies. Even for neurons that are visually distinguishable, there is no common ground to consistently define morphological types. The objective classification of PCs can be achieved with methods from algebraic topology, and the dendritic arborization is sufficient for the reliable identification of distinct types of cortical PCs. Therefore, we objectively identify 17 types of PCs in the rat somatosensory cortex. In addition, we provide a solution to the challenging problem of whether 2 similar neurons belong to different types or to a continuum of the same type. Our topological classification does not require expert input, is stable, and helps settle the long-standing debate on whether cell-types are discrete or continuous morphological variations of each other.

Electrophysiology prediction of single neurons based on their morphology.. [DOI: 10.1101/2020.02.04.933697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon).

In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model.

Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.

Dendritic action potentials and computation in human layer 2/3 cortical neurons

The active electrical properties of dendrites shape neuronal input and output and are fundamental to brain function. However, our knowledge of active dendrites has been almost entirely acquired from studies of rodents. In this work, we investigated the dendrites of layer 2 and 3 (L2/3) pyramidal neurons of the human cerebral cortex ex vivo. In these neurons, we discovered a class of calcium-mediated dendritic action potentials (dCaAPs) whose waveform and effects on neuronal output have not been previously described. In contrast to typical all-or-none action potentials, dCaAPs were graded; their amplitudes were maximal for threshold-level stimuli but dampened for stronger stimuli. These dCaAPs enabled the dendrites of individual human neocortical pyramidal neurons to classify linearly nonseparable inputs—a computation conventionally thought to require multilayered networks.

Conditioning by subthreshold synaptic input changes the intrinsic firing pattern of CA3 hippocampal neurons

Unlike synaptic strength, intrinsic excitability is assumed to be a stable property of neurons. For example, learning of somatic conductances is generally not incorporated into computational models, and the discharge pattern of neurons in response to test stimuli is frequently used as a basis for phenotypic classification. However, it is increasingly evident that signal processing properties of neurons are more generally plastic on the timescale of minutes. Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions toward adapting and intrinsic burst behaviors, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade and found that the observed transitions are likely due to recruitment of low-voltage calcium and Kv7 potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits.NEW & NOTEWORTHY Although firing patterns are widely conserved across the animal phyla, it is still a mystery why nerve cells present such diversity of discharge dynamics upon somatic step currents. Adding a new timing dimension to the intrinsic plasticity literature, here we show that CA3 neurons rapidly adapt through the space of known firing patterns in response to the subthreshold signals that they receive from their embedding circuit, potentially adjusting their network processing to the temporal statistics of their circuit.

Metabolic Cost of Dendritic Ca2+ Action Potentials in Layer 5 Pyramidal Neurons

Pyramidal neurons consume most signaling-related energy to generate action potentials (APs) and perform synaptic integration. Dendritic Ca²⁺ spike is an important integration mechanism for coupling inputs from different cortical layers. Our objective was to quantify the metabolic energy associated with the generation of Ca²⁺ APs in the dendrites. We used morphology-based computational models to simulate the dendritic Ca²⁺ spikes in layer 5 pyramidal neurons. We calculated the energy cost by converting Ca²⁺ influx into the number of ATP required to restore and maintain the homeostasis of intracellular Ca²⁺ concentrations. We quantified the effects of synaptic inputs, dendritic voltage, back-propagating Na⁺ spikes, and Ca²⁺ inactivation on Ca²⁺ spike cost. We showed that much more ATP molecules were required for reversing Ca²⁺ influx in the dendrites than for Na⁺ ion pumping in the soma during a Ca²⁺ AP. Increasing synaptic input increased the rate of dendritic depolarization and underlying Ca²⁺ influx, resulting in higher ATP consumption. Depolarizing dendritic voltage resulted in the inactivation of Ca²⁺ channels and reduced the ATP cost, while dendritic hyperpolarization increased the spike cost by de-inactivating Ca²⁺ channels. A back-propagating Na⁺ AP initiated in the soma increased Ca²⁺ spike cost in the apical dendrite when it coincided with a synaptic input within a time window of several milliseconds. Increasing Ca²⁺ inactivation rate reduced Ca²⁺ spike cost, while slowing Ca²⁺ inactivation increased the spike cost. The results revealed that the energy demand of a Ca²⁺ AP was dynamically dependent on the state of dendritic activity. These findings were important for predicting the energy budget for signaling in pyramidal cells, interpreting functional imaging data, and designing energy-efficient neuromorphic devices.