Efficient Low-Pass Dendro-Somatic Coupling in the Apical Dendrite of Layer 5 Pyramidal Neurons in the Anterior Cingulate Cortex

A cellular mechanism contributing to pain-induced analgesia

ABSTRACT

The anterior cingulate cortex (ACC) plays a crucial role in the perception of pain. It is consistently activated by noxious stimuli and its hyperactivity in chronic pain indicates plasticity in the local neuronal network. However, the way persistent pain effects and modifies different neuronal cell types in the ACC and how this contributes to sensory sensitization is not completely understood. This study confirms the existence of 2 primary subtypes of pyramidal neurons in layer 5 of the rostral, agranular ACC, which we could classify as intratelencephalic (IT) and cortico-subcortical (SC) projecting neurons, similar to other cortical brain areas. Through retrograde labeling, whole-cell patch-clamp recording, and morphological analysis, we thoroughly characterized their different electrophysiological and morphological properties. When examining the effects of peripheral inflammatory pain on these neuronal subtypes, we observed time-dependent plastic changes in excitability. During the acute phase, both subtypes exhibited reduced excitability, which normalized to pre-inflammatory levels after day 7. Daily conditioning with nociceptive stimuli during this period induced an increase in excitability specifically in SC neurons, which was correlated with a decrease in mechanical sensitization. Subsequent inhibition of the activity of SC neurons projecting to the periaqueductal gray with in vivo chemogenetics, resulted in reinstatement of the hypersensitivity. Accordingly, it was sufficient to enhance the excitability of these neurons chemogenetically in the inflammatory pain condition to induce hypoalgesia. These findings suggest a cell type-specific effect on the descending control of nociception and a cellular mechanism for pain-induced analgesia. Furthermore, increased excitability in this neuronal population is hypoalgesic rather than hyperalgesic.

Sequence learning, prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

Essentially all data processed by mammals and many other living organisms is sequential. This holds true for all types of sensory input data as well as motor output activity. Being able to form memories of such sequential data, to predict future sequence elements, and to replay learned sequences is a necessary prerequisite for survival. It has been hypothesized that sequence learning, prediction and replay constitute the fundamental computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) constitutes an abstract powerful algorithm implementing this form of computation and has been proposed to serve as a model of neocortical processing. In this study, we are reformulating this algorithm in terms of known biological ingredients and mechanisms to foster the verifiability of the HTM hypothesis based on electrophysiological and behavioral data. The proposed model learns continuously in an unsupervised manner by biologically plausible, local plasticity mechanisms, and successfully predicts and replays complex sequences. Apart from establishing contact to biology, the study sheds light on the mechanisms determining at what speed we can process sequences and provides an explanation of fast sequence replay observed in the hippocampus and in the neocortex.

The cAMP Response Element- Binding Protein/Brain-Derived Neurotrophic Factor Pathway in Anterior Cingulate Cortex Regulates Neuropathic Pain and Anxiodepression Like Behaviors in Rats

Neuropathic pain is often accompanied by anxiety and depression-like manifestations. Many studies have shown that alterations in synaptic plasticity in the anterior cingulate cortex (ACC) play a critical role, but the specific underlying mechanisms remain unclear. Previously, we showed that cAMP response element-binding protein (CREB) in the dorsal root ganglion (DRG) acts as a transcription factor contributing to neuropathic pain development. At the same time, brain-derived neurotrophic factor (BDNF), as important targets of CREB, is intricate in neuronal growth, differentiation, as well as the establishment of synaptic plasticity. Here, we found that peripheral nerve injury activated the spinal cord and ACC, and silencing the ACC resulted in significant relief of pain sensitivity, anxiety, and depression in SNI rats. In parallel, the CREB/BDNF pathway was activated in the spinal cord and ACC. Central specific knockdown and peripheral non-specific inhibition of CREB reversed pain sensitivity and anxiodepression induced by peripheral nerve injury. Consequently, we identified cingulate CREB/BDNF as an assuring therapeutic method for treating neuropathic pain as well as related anxiodepression.

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.

Presynaptic NMDA Receptors Influence Ca2+ Dynamics by Interacting with Voltage-Dependent Calcium Channels during the Induction of Long-Term Depression

Spike-timing-dependent long-term depression (t-LTD) of glutamatergic layer (L)4-L2/3 synapses in developing neocortex requires activation of astrocytes by endocannabinoids (eCBs), which release glutamate onto presynaptic NMDA receptors (preNMDARs). The exact function of preNMDARs in this context is still elusive and strongly debated. To elucidate their function, we show that bath application of the eCB 2-arachidonylglycerol (2-AG) induces a preNMDAR-dependent form of chemically induced LTD (eCB-LTD) in L2/3 pyramidal neurons in the juvenile somatosensory cortex of rats. Presynaptic Ca²⁺ imaging from L4 spiny stellate axons revealed that action potential (AP) evoked Ca²⁺ transients show a preNMDAR-dependent broadening during eCB-LTD induction. However, blockade of voltage-dependent Ca²⁺ channels (VDCCs) did not uncover direct preNMDAR-mediated Ca²⁺ transients in the axon. This suggests that astrocyte-mediated glutamate release onto preNMDARs does not result in a direct Ca²⁺ influx, but that it instead leads to an indirect interaction with presynaptic VDCCs, boosting axonal Ca²⁺ influx. These results reveal one of the main remaining missing pieces in the signaling cascade of t-LTD at developing cortical synapses.

Dendritic Computing: Branching Deeper into Machine Learning

In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. Four major computational implications are identified as improved expressivity, more efficient use of resources, utilizing internal learning signals, and enabling continual learning. We then discuss examples of how dendritic computations have been used to solve real-world classification problems with performance reported on well known data sets used in machine learning. The works are categorized according to the three primary methods of plasticity used-structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.

Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses

Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. Through carefully chosen parameter fits, solvable in the least-squares sense, we obtain accurate reduced compartmental models at any level of complexity. We show that (back-propagating) action potentials, Ca²⁺ spikes, and N-methyl-D-aspartate spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input resistance between the ablated branches and the next proximal dendrite. Furthermore, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide software that automatizes the simplification, eliminating a common hurdle toward including dendritic computations in network models.