Changes In Synaptic Strength Research Articles

Neural dynamic transitions caused by changes of synaptic strength in heterogeneous networks

Sleep-dependent memory consolidation (SDMC) is an unaddressed and challenging functional issue regarding neural dynamics. Based on experimental findings, the synaptic homeostasis hypothesis for understanding SDMC implies a link between changes of synaptic strength and transitions of neural dynamics (including tonic and bursting activities). However, the causality of the link has been unclear. Recently, it has been found that, the synaptic changes can cause the dynamical transitions and so can produce the slow-wave activity (SWA) similar to that observed during sleep in a homogeneous network (Zhou et al., 2021). Since many real neural networks are heterogeneous in topology, we herein further investigated the transitions and the SWA driven by the synaptic changes in heterogeneous networks. It was found that synaptic changes can also cause the dynamical transitions and the SWA. Differently, the transitions in heterogeneous networks are hierarchical for neurons with different degrees, whether in electrically or chemically coupled networks. The critical synaptic strengths related to the transitions for neurons depend strongly on their degrees. The larger the degree, the smaller the critical synaptic strength. We showed that, they obey power-law relations, both in electrically coupled networks and in chemically coupled networks in the presence of inhibitory synapses. Particularly, it was found that the networked critical synaptic strength depends only on the networked maximum degree in electrically coupled networks. We showed, both numerically and analytically, that they also satisfy a power-law function. In general, our study revealed a possible causal relationship between changes of synaptic strength and transitions of neural dynamics in heterogeneous networks. Further interesting and challenging investigations are briefly discussed as well.

Neurotransmitters in Prevention and Treatment of Alzheimer's Disease.

Alzheimer's disease (AD) is the most frequent cause of cognitive impairment in middle-aged and older populations. There is a lack of drugs that demonstrate significant efficacy in AD, so the study of the pathogenesis of AD is of great importance. More efficacious interventions are needed, as reflected by our population's fast aging. Synaptic plasticity is the capacity of neurons to adjust their connections, and it is strongly tied to learning and memory, cognitive function, and brain injury recovery. Changes in synaptic strength, such as long-term potentiation (LTP) or inhibition (LTD), are thought to represent the biological foundation of the early stages of learning and memory. The results of numerous studies confirm that neurotransmitters and their receptors play an important role in the regulation of synaptic plasticity. However, so far, there is no definite correlation between the function of neurotransmitters in aberrant neural oscillation and AD-related cognitive impairment. We summarized the AD process to understand the impact of neurotransmitters in the progression and pathogenesis of AD, including the current status of neurotransmitter target drugs, and the latest evidence of neurotransmitters' function and changes in the AD process.

Noise-modulated multistable synapses in a Wilson-Cowan-based model of plasticity.

Frequency-dependent plasticity refers to changes in synaptic strength in response to different stimulation frequencies. Resonance is a factor known to be of importance in such frequency dependence, however, the role of neural noise in the process remains elusive. Considering the brain is an inherently noisy system, understanding its effects may prove beneficial in shaping therapeutic interventions based on non-invasive brain stimulation protocols. The Wilson-Cowan (WC) model is a well-established model to describe the average dynamics of neural populations and has been shown to exhibit bistability in the presence of noise. However, the important question of how the different stable regimes in the WC model can affect synaptic plasticity when cortical populations interact has not yet been addressed. Therefore, we investigated plasticity dynamics in a WC-based model of interacting neural populations coupled with activity-dependent synapses in which a periodic stimulation was applied in the presence of noise of controlled intensity. The results indicate that for a narrow range of the noise variance, synaptic strength can be optimized. In particular, there is a regime of noise intensity for which synaptic strength presents a triple-stable state. Regulating noise intensity affects the probability that the system chooses one of the stable states, thereby controlling plasticity. These results suggest that noise is a highly influential factor in determining the outcome of plasticity induced by stimulation.

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.

A synaptic plasticity induced change in synaptic intensity variation and neurodynamic transition during awakening-sleep cycle

It has been found experimentally that learning during wakefulness leads to a net enhancement of synaptic strength, accompanied by the neural dynamical transition from tonic to bursting firing, while the net synaptic strength decreases to a baseline level during sleep, accompanied by the transition from bursting to tonic firing. In this paper, we establish a model of synaptic plasticity, which can realize synaptic strength changes and neural dynamical transitions in wakefulness-sleep cycle by using the coupled Hindmarsh-Rose neurons. Through numerical simulation and theoretical analysis, it is further found that the average synaptic weight of the neural network can reach a stable value during either prolonged wakefulness or prolonged sleep, which depends on the ratio of some specific parameters in the model. Particularly, the synaptic weight exhibits a stable log-normal distribution observed in a real neural system, when the average synaptic weight reaches a stable value. Moreover, the fluctuation of this weight distribution is positively correlated with the fluctuation of noise in the synaptic plasticity model. The provided model of the synaptic plasticity and its dynamics results can provide a theoretical reference for studying the physiological mechanism of synaptic plasticity and neuronal firings during the wakefulness-sleep cycle, and they are expected to have potential applications in the development of therapeutic interventions for sleep disorders.

Mitochondrial Ca2+ uniporter haploinsufficiency enhances long-term potentiation at hippocampal mossy fibre synapses.

Long-term changes in synaptic strength form the basis of learning and memory. These changes rely upon energy-demanding mechanisms, which are regulated by local Ca2+ signalling. Mitochondria are optimised for providing energy and buffering Ca2+. However, our understanding of the role of mitochondria in regulating synaptic plasticity is incomplete. Here, we have used optical and electrophysiological techniques in cultured hippocampal neurons and ex vivo hippocampal slices from mice with haploinsufficiency of the mitochondrial Ca2+ uniporter (MCU+/-) to address whether reducing mitochondrial Ca2+ uptake alters synaptic transmission and plasticity. We found that cultured MCU+/- hippocampal neurons have impaired Ca2+ clearance, and consequently enhanced synaptic vesicle fusion at presynapses occupied by mitochondria. Furthermore, long-term potentiation (LTP) at mossy fibre (MF) synapses, a process which is dependent on presynaptic Ca2+ accumulation, is enhanced in MCU+/- slices. Our results reveal a previously unrecognised role for mitochondria in regulating presynaptic plasticity of a major excitatory pathway involved in learning and memory.

Human iPSC-Derived Cortical Neurons Display Homeostatic Plasticity.

Maintaining the excitability of neurons and circuits is fundamental for healthy brain functions. The global compensatory increase in excitatory synaptic strength, in response to decreased activity, is one of the main homeostatic mechanisms responsible for such regulation. This type of plasticity has been extensively characterized in rodents in vivo and in vitro, but few data exist on human neurons maturation. We have generated an in vitro cortical model system, based on differentiated human-induced pluripotent stem cells, chronically treated with tetrodotoxin, to investigate homeostatic plasticity at different developmental stages. Our findings highlight the presence of homeostatic plasticity in human cortical networks and show that the changes in synaptic strength are due to both pre- and post-synaptic mechanisms. Pre-synaptic plasticity involves the potentiation of neurotransmitter release machinery, associated to an increase in synaptic vesicle proteins expression. At the post-synaptic level, we report an increase in the expression of post-synaptic density proteins, involved in glutamatergic receptor anchoring. These results extend our understanding of neuronal homeostasis and reveal the developmental regulation of its expression in human cortical networks. Since induced pluripotent stem cell-derived neurons can be obtained from patients with neurodevelopmental and neurodegenerative diseases, our platform offers a versatile model for assessing human neural plasticity under physiological and pathological conditions.

Synaptic Remodeling of the Auditory Cortex Following Bilateral Blindness: Evidence of Cross-modal Plasticity.

We aimed to evaluate structural dynamic changes of neurons in the auditory cortex after visual deprivation. We longitudinally tracked dendritic spines for 3 weeks after visual deprivation in vivo using a two-photon microscope. GFP-labeled dendritic spines in the auditory cortex were serially followed after bilateral enucleation. The turnover rate, density, and size of the spines in the dendrites were evaluated 1, 2, and 3 weeks after visual deprivation. The turnover rate of the dendritic spines in the auditory cortex increased at 1 week (20.1±7.3%) after bilateral enucleation compared to baseline (12.5±7.9%); the increase persisted for up to 3 weeks (20.9±11.0%). The spine loss rate was slightly higher than the spine gain rate. The average spine density (number of spines per 1 μm of dendrite) was significantly lower at 2 weeks (2W; 0.22±0.06 1/μm) and 3 W (0.22±0.08 1/μm) post-nucleation compared to baseline (0.026±0.09 1/μm). We evaluated the change of synaptic strength in the stable spines at each time point. The normalized spine size in the auditory cortex was significantly increased after bilateral blindness at 1 W postoperatively (1.36±0.92), 2 W postoperatively (1.40±1.18), and 3 W postoperatively (1.36±0.88) compared to baseline. Sensory deprivation resulted in remodeling of the neural circuitry in the spared cortex, via cross-modal plasticity in the direction of partial breakdown of synapses, and enhanced strength of the remaining synapses.

Neural signature of flexible coding in prefrontal cortex

The ability of prefrontal cortex to quickly encode novel associations is crucial for adaptive behavior and central to working memory. Fast Hebbian changes in synaptic strength permit forming new associations, but neuronal signatures of this have been elusive. We devised a trialwise index of pattern similarity to look for rapid changes in population codes. Based on a computational model of working memory, we hypothesized that synaptic strength-and consequently, the tuning of neurons-could change if features of a subsequent stimulus need to be "reassociated," i.e., if bindings between features need to be broken to encode the new item. As a result, identical stimuli might elicit different neural responses. As predicted, neural response similarity dropped following rebinding, but only in prefrontal cortex. The history-dependent changes were expressed on top of traditional, fixed selectivity and were not explainable by carryover of previous firing into the current trial or by neural adaptation.

Heterosynaptic plasticity in biomembrane memristors controlled by pH

In biology, heterosynaptic plasticity maintains homeostasis in synaptic inputs during associative learning and memory, and initiates long-term changes in synaptic strengths that nonspecifically modulate different synapse types. In bioinspired neuromorphic circuits, heterosynaptic plasticity may be used to extend the functionality of two-terminal, biomimetic memristors. In this article, we explore how changes in the pH of droplet interface bilayer aqueous solutions modulate the memristive responses of a lipid bilayer membrane in the pH range 4.97–7.40. Surprisingly, we did not find conclusive evidence for pH-dependent shifts in the voltage thresholds (V*) needed for alamethicin ion channel formation in the membrane. However, we did observe a clear modulation in the dynamics of pore formation with pH in time-dependent, pulsed voltage experiments. Moreover, at the same voltage, lowering the pH resulted in higher steady-state currents because of increased numbers of conductive peptide ion channels in the membrane. This was due to increased partitioning of alamethicin monomers into the membrane at pH 4.97, which is below the pKa (~5.3–5.7) of carboxylate groups on the glutamate residues of the peptide, making the monomers more hydrophobic. Neutralization of the negative charges on these residues, under acidic conditions, increased the concentration of peptide monomers in the membrane, shifting the equilibrium concentrations of peptide aggregate assemblies in the membrane to favor greater numbers of larger, increasingly more conductive pores. It also increased the relaxation time constants for pore formation and decay, and enhanced short-term facilitation and depression of the switching characteristics of the device. Modulating these thresholds globally and independently of alamethicin concentration and applied voltage will enable the assembly of neuromorphic computational circuitry with enhanced functionality.Impact statementWe describe how to use pH as a modulatory “interneuron” that changes the voltage-dependent memristance of alamethicin ion channels in lipid bilayers by changing the structure and dynamical properties of the bilayer. Having the ability to independently control the threshold levels for pore conduction from voltage or ion channel concentration enables additional levels of programmability in a neuromorphic system. In this article, we note that barriers to conduction from membrane-bound ion channels can be lowered by reducing solution pH, resulting in higher currents, and enhanced short-term learning behavior in the form of paired-pulse facilitation. Tuning threshold values with environmental variables, such as pH, provide additional training and learning algorithms that can be used to elicit complex functionality within spiking neural networks.Graphical abstract

Norepinephrine potentiates and serotonin depresses visual cortical responses by transforming eligibility traces

Reinforcement allows organisms to learn which stimuli predict subsequent biological relevance. Hebbian mechanisms of synaptic plasticity are insufficient to account for reinforced learning because neuromodulators signaling biological relevance are delayed with respect to the neural activity associated with the stimulus. A theoretical solution is the concept of eligibility traces (eTraces), silent synaptic processes elicited by activity which upon arrival of a neuromodulator are converted into a lasting change in synaptic strength. Previously we demonstrated in visual cortical slices the Hebbian induction of eTraces and their conversion into LTP and LTD by the retroactive action of norepinephrine and serotonin Here we show in vivo in mouse V1 that the induction of eTraces and their conversion to LTP/D by norepinephrine and serotonin respectively potentiates and depresses visual responses. We also show that the integrity of this process is crucial for ocular dominance plasticity, a canonical model of experience-dependent plasticity.

Emergence of associative learning in a neuromorphic inference network

Objective. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes—by modelling the activity of functional neural networks at a mesoscopic scale—the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. Approach. We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. Main results. Persistent changes of synaptic strength—that mirrored neurophysiological observations—emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. Significance. These findings show that: (a) an ensemble of free energy minimizing neurons—organized in a biological plausible architecture—can recapitulate functional self-organization observed in nature, such as associative plasticity, and (b) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.

Cortical Representation of Touch in Silico

With its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents’. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex’s granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.

An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster.

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting, and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short term to long term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.

MAPK signaling and a mobile scaffold complex regulate AMPA receptor transport to modulate synaptic strength.

Synaptic plasticity depends on rapid experience-dependent changes in the number of neurotransmitter receptors. Previously, we demonstrated that motor-mediated transport of AMPA receptors (AMPARs) to and from synapses is a critical determinant of synaptic strength. Here, we describe two convergent signaling pathways that coordinate the loading of synaptic AMPARs onto scaffolds, and scaffolds onto motors, thus providing a mechanism for experience-dependent changes in synaptic strength. We find that an evolutionarily conserved JIP-protein scaffold complex and two classes of mitogen-activated protein kinase (MAPK) proteins mediate AMPAR transport by kinesin-1 motors. Genetic analysis combined with invivo, real-time imaging in Caenorhabditis elegans revealed that CaMKII is required for loading AMPARs onto the scaffold, and MAPK signaling is required for loading the scaffold complex onto motors. Our data support a model where CaMKII signaling and a MAPK-signaling pathway cooperate to facilitate the rapid exchange of AMPARs required for early stages of synaptic plasticity.

Synaptic plasticity controls the emergence of population-wide invariant representations in balanced network models

The intensity and the features of sensory stimuli are encoded in the activity of neurons in the cortex. In the visual and piriform cortices, the stimulus intensity rescales the activity of the population without changing its selectivity for the stimulus features. The cortical representation of the stimulus is therefore intensity invariant. This emergence of network-invariant representations appears robust to local changes in synaptic strength induced by synaptic plasticity, even though (i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and (ii) in networks with balanced excitation and inhibition, synaptic plasticity determines the nonlinear network behavior. In this study we investigate the consistency of invariant representations with a variety of synaptic states in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the emergence of intensity-invariant or intensity-dependent selectivity. In particular, we demonstrate that an effective power-law synaptic transformation at the population level is necessary for invariance. In a range of firing rates, purely depressing short-term synapses fulfills this condition, and in this case, the network is contrast-invariant. Instead, facilitating short-term plasticity generally narrows the network selectivity. We found that facilitating and depressing short-term plasticity can be combined to approximate a power-law that leads to contrast invariance. These results explain how the physiology of individual synapses is linked to the emergence of invariant representations of sensory stimuli at the network level.

Heterogeneous Responses to Changes in Inhibitory Synaptic Strength in Networks of Spiking Neurons.

How does the dynamics of neurons in a network respond to changes in synaptic weights? Answer to this question would be important for a full understanding of synaptic plasticity. In this article, we report our numerical study of the effects of changes in inhibitory synaptic weights on the spontaneous activity of networks of spiking neurons with conductance-based synapses. Networks with biologically realistic features, which were reconstructed from multi-electrode array recordings taken in a cortical neuronal culture, and their modifications were used in the simulations. The magnitudes of the synaptic weights of all the inhibitory connections are decreased by a uniform amount subjecting to the condition that inhibitory connections would not be turned into excitatory ones. Our simulation results reveal that the responses of the neurons are heterogeneous: while the firing rate of some neurons increases as expected, the firing rate of other neurons decreases or remains unchanged. The same results show that heterogeneous responses also occur for an enhancement of inhibition. This heterogeneity in the responses of neurons to changes in inhibitory synaptic strength suggests that activity-induced modification of synaptic strength does not necessarily generate a positive feedback loop on the dynamics of neurons connected in a network. Our results could be used to understand the effects of bicuculline on spiking and bursting activities of neuronal cultures. Using reconstructed networks with biologically realistic features enables us to identify a long-tailed distribution of average synaptic weights for outgoing links as a crucial feature in giving rise to bursting in neuronal networks and in determining the overall response of the whole network to changes in synaptic strength. For networks whose average synaptic weights for outgoing links have a long-tailed distribution, bursting is observed and the average firing rate of the whole network increases upon inhibition suppression or decreases upon inhibition enhancement. For networks whose average synaptic weights for outgoing links are approximately normally distributed, bursting is not found and the average firing rate of the whole network remains approximately constant upon changes in inhibitory synaptic strength.

Disinhibitory Circuitry Gates Associative Synaptic Plasticity in Olfactory Cortex.

Inhibitory microcircuits play an essential role in regulating cortical responses to sensory stimuli. Interneurons that inhibit dendritic or somatic integration act as gatekeepers for neural activity, synaptic plasticity, and the formation of sensory representations. Conversely, interneurons that selectively inhibit other interneurons can open gates through disinhibition. In the anterior piriform cortex, relief of inhibition permits associative LTP of excitatory synapses between pyramidal neurons. However, the interneurons and circuits mediating disinhibition have not been elucidated. In this study, we use an optogenetic approach in mice of both sexes to identify the inhibitory interneurons and disinhibitory circuits that regulate LTP. We focused on three prominent interneuron classes: somatostatin (SST), parvalbumin (PV), and vasoactive intestinal polypeptide (VIP) interneurons. We find that LTP is gated by the inactivation SST or PV interneurons and by the activation of VIP interneurons. Further, VIP interneurons strongly inhibit putative SST cells during LTP induction but only weakly inhibit PV interneurons. Together, these findings suggest that VIP interneurons mediate a disinhibitory circuit that gates synaptic plasticity during the formation of olfactory representations.SIGNIFICANCE STATEMENT Inhibitory interneurons stabilize neural activity during sensory processing. However, inhibition must also be modulated to allow sensory experience shape neural responses. In olfactory cortex, inhibition regulates activity-dependent increases in excitatory synaptic strength that accompany odor learning. We identify two inhibitory interneuron classes that act as gatekeepers preventing excitatory enhancement. We demonstrate that driving a third class of interneurons inhibits the gatekeepers and opens the gate for excitatory enhancement. All three inhibitory neuron classes comprise disinhibitory microcircuit motifs found throughout the cortex. Our findings suggest that a common disinhibitory microcircuit promotes changes in synaptic strength during sensory processing and learning.

Stabilization of Spine Synaptopodin by mGluR1 Is Required for mGluR-LTD.

Dendritic spines, actin-rich protrusions forming the postsynaptic sites of excitatory synapses, undergo activity-dependent molecular and structural remodeling. Activation of Group 1 metabotropic glutamate receptors (mGluR1 and mGluR5) by synaptic or pharmacological stimulation, induces LTD, but whether this is accompanied with spine elimination remains unresolved. A subset of telencephalic mushroom spines contains the spine apparatus (SA), an enigmatic organelle composed of stacks of smooth endoplasmic reticulum, whose formation depends on the expression of the actin-bundling protein Synaptopodin. Allocation of Synaptopodin to spines appears governed by cell-intrinsic mechanisms as the relative frequency of spines harboring Synaptopodin is conserved in vivo and in vitro Here we show that expression of Synaptopodin/SA in spines is required for induction of mGluR-LTD at Schaffer collateral-CA1 synapses of male mice. Post-mGluR-LTD, mushroom spines lacking Synaptopodin/SA are selectively lost, whereas spines harboring it are preserved. This process, dependent on activation of mGluR1 but not mGluR5, is conserved in mature mouse neurons and rat neurons of both sexes. Mechanistically, we find that mGluR1 supports physical retention of Synaptopodin within excitatory spine synapses during LTD while triggering lysosome-dependent degradation of the protein residing in dendritic shafts. Together, these results reveal a cellular mechanism, dependent on mGluR1, which enables selective preservation of stronger spines containing Synaptopodin/SA while eliminating weaker ones and potentially countering spurious strengthening by de novo recruitment of Synaptopodin. Overall, our results identify spines with Synaptopodin/SA as the locus of mGluR-LTD and underscore the importance of the molecular microanatomy of spines in synaptic plasticity.SIGNIFICANCE STATEMENT Long-term changes in functional synaptic strength are associated with modification of synaptic connectivity through stabilization or elimination of dendritic spines, the postsynaptic locus of excitatory synapses. How heterogeneous spine microanatomy instructs spine remodeling after long-term synaptic depression (LTD) remains unclear. Metabotropic glutamate receptors mGluR1 and mGluR5 induce a form of LTD critical to circuit function in physiological and disease conditions. Our results identify spines containing the protein Synaptopodin, which enables local assembly of a spine apparatus, as the locus of expression of mGluR-LTD and demonstrate a specific role of mGluR1 in promoting selective loss after mGluR-LTD of mature dendritic spines lacking Synaptopodin/spine apparatus. These findings highlight the fundamental contribution of spine microanatomy in selectively enabling functional and structural plasticity.

Estimating null and potent modes of feedforward communication in a computational model of cortical activity

Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, pairwise correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.