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Neuropeptides, members of a large and evolutionarily ancient family of proteinaceous cell-cell signaling molecules, are widely recognized as extremely potent regulators of brain function and behavior. At the cellular level, neuropeptides are known to act mainly via modulation of ion channel and synapse function, but functional impacts emerging at the level of complex cortical synaptic networks have resisted mechanistic analysis. New findings from single-cell RNA-seq transcriptomics now illuminate intricate patterns of cortical neuropeptide signaling gene expression and new tools now offer powerful molecular access to cortical neuropeptide signaling. Here we highlight some of these new findings and tools, focusing especially on prospects for experimental and theoretical exploration of peptidergic and synaptic networks interactions underlying cortical function and plasticity.
In this paper, we are aiming to propose a novel mathematical model that studies the dynamics of synaptic damage in terms of concentrations of toxic neuropeptides/neurotransmitters during neurotransmission processes. Our primary objective is to employ Wardrop's first and second principles within a neural network of the brain. In order to comprehensively incorporate Wardrop's first and second principles into the neural network of the brain, we introduce two novel concepts: \textit{neuropeptide's (neurotransmitter's) equilibrium} and \textit{synapses optimum}. The \textit{neuropeptide/neurotransmitter equilibrium} refers to \textit{a distribution of toxic neuropeptides/neurotransmitters that leads to uniform damage across all synaptic links}. Meanwhile, \textit{synapses optimum} is \textit{the most desirable distribution of toxic neuropeptides/neurotransmitters that minimizes the cumulative damage experienced by all synapses}. In the context of a neural network within the brain, an analogue of the price of anarchy is \textit{the price of cognition} which is \textit{the most unfavorable ratio between the overall impairment caused by toxic neuropeptide's (neurotransmitter's) equilibrium
Classical autoassociative memory models have been central to understanding emergent computations in recurrent neural circuits across diverse biological contexts. However, they typically neglect neuromodulatory agents that are known to strongly shape memory capacity and stability. Here we introduce a minimal, biophysically motivated associative memory network where neuropeptide-like signals are modeled by a self-adaptive, activity-dependent gating mechanism. Using many-body simulations and dynamical mean-field theory, we show that such gating fundamentally reorganizes the attractor structure: the network bypasses the classical spin-glass transition, maintaining robust, high-overlap retrieval far beyond the standard critical capacity, without shrinking basins of attraction. Mechanistically, the gate stabilizes transient ghost remnants of stored patterns even far above the Hopfield limit, converting them into multistable attractors. These results demonstrate that neuromodulation-like gating alone can dramatically enhance associative memory capacity, eliminate the sharp Hopfield-style catastrophic breakdown, and reshape the memory landscape, providing a simple, general route to richer
Transport of dense core vesicles (DCVs) in neurons is crucial for distributing molecules like neuropeptides and growth factors. We studied the experimental trajectories of dynein-driven directed movement of DCVs in the ALA neuron C. elegans over a duration of up to 6 seconds. We analysed the DCV movement in three strains of C. elegans: 1) with normal kinesin-1 function, 2) with reduced function in kinesin light chain 2 (KLC-2), and 3) a null mutation in kinesin light chain 1 (KLC-1). We find that DCVs move superdiffusively with displacement variance $var(x) \sim t^2$ in all three strains with low reversal rates and frequent immobilization of DCVs. The distribution of DCV displacements fits a beta-binomial distribution with the mean and the variance following linear and quadratic growth patterns, respectively. We propose a simple heterogeneous random walk model to explain the observed superdiffusive retrograde transport behaviour of DCV movement. This model involves a random probability with the beta density for a DCV to resume its movement or remain in the same position.
The spectacular successes of recurrent neural network models where key parameters are adjusted via backpropagation-based gradient descent have inspired much thought as to how biological neuronal networks might solve the corresponding synaptic credit assignment problem. There is so far little agreement, however, as to how biological networks could implement the necessary backpropagation through time, given widely recognized constraints of biological synaptic network signaling architectures. Here, we propose that extra-synaptic diffusion of local neuromodulators such as neuropeptides may afford an effective mode of backpropagation lying within the bounds of biological plausibility. Going beyond existing temporal truncation-based gradient approximations, our approximate gradient-based update rule, ModProp, propagates credit information through arbitrary time steps. ModProp suggests that modulatory signals can act on receiving cells by convolving their eligibility traces via causal, time-invariant and synapse-type-specific filter taps. Our mathematical analysis of ModProp learning, together with simulation results on benchmark temporal tasks, demonstrate the advantage of ModProp over e
A fundamental problem in neuroscience is understanding how a network's properties dictate its function. Connectomics provides one avenue to predict nervous system function. To test this explicitly, we systematically measure signal propagation in 23,427 pairs of neurons across the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties, and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from predictions based on anatomy. Using mutants, we show that extrasynaptic signaling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle dependent signaling on seconds-or-less timescales that evoke acute calcium transients often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that here extrasynaptically released neuropeptides serve a similar function as that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts neura
Hormonal processes along with enzymatic processing similar to that found in vertebrates occur in annelids. Amino acid sequence determination of annelids precursor gene products reveals the presence of the respective peptides that exhibit high sequence identity to their mammalian counterparts. Furthermore, these neuropeptides exert similar physiological function in annelids than the ones found in vertebrates. In this respect, the high conservation in course of evolution of these molecules families reflects their importance. Nevertheless, some specific neuropeptides to annelids or invertebrates have also been in these animals.
Neuropeptide SF (NPSF) is a member of RFamide neuropeptides that play diverse roles in central nervous system. Little is know about the effects of NPSF on brain functions. Antidepressant-like effect of NPSF was studied in modified mice FST. NPSF showed the antidepressant-like effects by decreasing the immobility time and increasing the climbing and swimming time. Furthermore, the involvement of the adrenergic, serotonergic, cholinergic or dopaminergic receptors in the antidepressant-like effect of NPSF was studied in modified mice FST. Mice were pretreated with a non-selective α-adrenergic receptor antagonist phenoxybenzamine, a \b{eta}-adrenergic receptor antagonist, propranolol, a non-selective 5-HT2 serotonergic receptor antagonist, cyproheptadine, nonselective muscarinic acetylcholine receptor antagonist, atropine, or D2, D3, D4 dopamine receptor antagonist, haloperidol. The present results confirmed that the antidepressant-like effect of NPSF is mediated, at least in part, by an interaction of the α-adrenergic, 5-HT2 serotonergic, muscarinic acetylcholine receptors and D2, D3, D4 dopamine receptor in a modified mouse FST.
We investigate how locomotory behavior is generated in the brain focusing on the paradigmatic connectome of nematode Caenorhabditis elegans (C. elegans) and on neuronal activity patterns that control forward locomotion. We map the neuronal network of the worm as a multilayer network that takes into account various neurotransmitters and neuropeptides. Using logistic regression analysis, we predict the neurons of the locomotory subnetwork. Combining Hindmarsh-Rose equations for neuronal activity with a leaky integrator model for muscular activity, we study the dynamics within this subnetwork and predict the forward locomotion of the worm using a harmonic wave model. The application of time-delayed feedback control reveals synchronization effects that contribute to a coordinated locomotion of C. elegans. Analyzing the synchronicity when the activity of certain neurons is silenced informs us about their significance for a coordinated locomotory behavior. Since the information processing is the same in humans and C. elegans, the study of the locomotory circuitry provides new insights for understanding how the brain generates motion behavior.
In neurons, neuropeptides are synthesized in the soma and are then transported along the axon in dense core vesicles (DCVs). DCVs are captured in varicosities located along the axon terminal called en passant boutons, which are active terminal sites that accumulate and release neurotransmitters. Recently developed experimental techniques allow for the estimation of the age of DCVs in various locations in the axon terminal. Accurate simulation of the mean age of DCVs in boutons requires the development of a model that would account for resident, transiting-anterograde, and transiting-retrograde DCV populations. In this paper, such a model is developed. The model is applied to simulating DCV transport in Drosophila type II motoneurons. The model simulates DCV transport and capture in the axon terminals and makes it possible to predict the age density distribution of DCVs in en passant boutons as well as DCV's mean age in boutons. The predicted prevalence of older organelles in distal boutons may explain the "dying back" pattern of axonal degeneration observed in dopaminergic neurons in Parkinson's disease. The predicted difference of two hours between the age of older DCVs residing i
Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function. Here we present a draft monoamine connectome, along with a partial neuropeptide connectome, for the nematode C. elegans, based on new and published expression data for biosynthetic genes and receptors. Thus, the neuronal connectome can be represented as a multiplex network, with synaptic, gap junction, and neuromodulatory layers representing alternative modes of interneuronal interaction and with distinct network structures. In particular, the monoamine network exhibits novel topological properties, with a highly disassortative star-like structure and a rich-club of interconnected broadcasting hubs. Despite the low degree of overlap between layers, we find highly significant modes of interaction, pinpointing network locations and multilink motifs where aminergic and neuropeptide signalling modulate synaptic activity. The multilayer connectome of C. elegans represents a clear exemplar of a biological multiplex network and provides a prototype for understand
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