Integrating Hebbian and homeostatic plasticity: introduction

Rapid rebalancing of co-tuned ensemble activity in the auditory cortex

Sensory information is represented by small varying neuronal ensembles in sensory cortices. In the auditory cortex (AC) repeated presentations of the same sound activate differing ensembles indicating high trial-by trial variability in activity even though the sounds activate the same percept. Efficient processing of complex acoustic signals requires that these sparsely distributed neuronal ensembles actively interact in order to provide a constant percept. Thus, the differing ensembles might interact to process the incoming sound inputs. Here, we probe interactions within and across ensembles by combining in vivo 2-photon Ca2+ imaging and holographic optogenetic stimulation to study how increased activity of single cells level affects the cortical network. We stimulated a small number of neurons sharing the same frequency preference alongside the presentation of a target pure tone, further increasing their tone-evoked activity. We found that other non-stimulated co-tuned neurons decreased their tone-evoked activity when the frequency of the presented pure tone matched to their tuning property, while non co-tuned neurons were unaffected. Activity decrease was greater for non-stimulated co-tuned neurons with higher frequency selectivity. Co-tuned and non co-tuned neurons were spatially intermingled. Our results shows that co-tuned ensembles communicated and balanced their total activity across the larger network. The rebalanced network activity due to external stimulation remained constant. These effects suggest that co-tuned ensembles in AC interact and rapidly rebalance their activity to maintain encoding homeostasis, and that the rebalanced network is persistent.

Astrocytes enhance plasticity response during reversal learning

Astrocytes play a key role in the regulation of synaptic strength and are thought to orchestrate synaptic plasticity and memory. Yet, how specifically astrocytes and their neuroactive transmitters control learning and memory is currently an open question. Recent experiments have uncovered an astrocyte-mediated feedback loop in CA1 pyramidal neurons which is started by the release of endocannabinoids by active neurons and closed by astrocytic regulation of the D-serine levels at the dendrites. D-serine is a co-agonist for the NMDA receptor regulating the strength and direction of synaptic plasticity. Activity-dependent D-serine release mediated by astrocytes is therefore a candidate for mediating between long-term synaptic depression (LTD) and potentiation (LTP) during learning. Here, we show that the mathematical description of this mechanism leads to a biophysical model of synaptic plasticity consistent with the phenomenological model known as the BCM model. The resulting mathematical framework can explain the learning deficit observed in mice upon disruption of the D-serine regulatory mechanism. It shows that D-serine enhances plasticity during reversal learning, ensuring fast responses to changes in the external environment. The model provides new testable predictions about the learning process, driving our understanding of the functional role of neuron-glia interaction in learning.

Synaptic architecture of a memory engram in the mouse hippocampus

Memory engrams are formed through experience-dependent remodeling of neural circuits, but their detailed architectures have remained unresolved. Using 3D electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway following chemogenetic labeling of cellular ensembles with a remote history of correlated excitation during associative learning. Projection neurons involved in memory acquisition expanded their connectomes via multi-synaptic boutons without altering the numbers and spatial arrangements of individual axonal terminals and dendritic spines. This expansion was driven by presynaptic activity elicited by specific negative valence stimuli, regardless of the co-activation state of postsynaptic partners. The rewiring of initial ensembles representing an engram coincided with local, input-specific changes in the shapes and organelle composition of glutamatergic synapses, reflecting their weights and potential for further modifications. Our findings challenge the view that the connectivity among neuronal substrates of memory traces is governed by Hebbian mechanisms, and offer a structural basis for representational drifts.

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

Memory formation is usually associated with Hebbian learning and synaptic plasticity, which changes the synaptic strengths but omits structural changes. A recent study suggests that structural plasticity can also lead to silent memory engrams, reproducing a conditioned learning paradigm with neuron ensembles. However, this study is limited by its way of synapse formation, enabling the formation of only one memory engram. Overcoming this, our model allows the formation of many engrams simultaneously while retaining high neurophysiological accuracy, e.g., as found in cortical columns. We achieve this by substituting the random synapse formation with the Model of Structural Plasticity. As a homeostatic model, neurons regulate their activity by growing and pruning synaptic elements based on their current activity. Utilizing synapse formation based on the Euclidean distance between the neurons with a scalable algorithm allows us to easily simulate 4 million neurons with 343 memory engrams. These engrams do not interfere with one another by default, yet we can change the simulation parameters to form long-reaching associations. Our model's analysis shows that homeostatic engram formation requires a certain spatiotemporal order of events. It predicts that synaptic pruning precedes and enables synaptic engram formation and that it does not occur as a mere compensatory response to enduring synapse potentiation as in Hebbian plasticity with synaptic scaling. Our model paves the way for simulations addressing further inquiries, ranging from memory chains and hierarchies to complex memory systems comprising areas with different learning mechanisms.

Deprivation-Induced Plasticity in the Early Central Circuits of the Rodent Visual, Auditory, and Olfactory Systems

Activity-dependent neuronal plasticity is crucial for animals to adapt to dynamic sensory environments. Traditionally, it has been investigated using deprivation approaches in animal models primarily in sensory cortices. Nevertheless, emerging evidence emphasizes its significance in sensory organs and in subcortical regions where cranial nerves relay information to the brain. Additionally, critical questions started to arise. Do different sensory modalities share common cellular mechanisms for deprivation-induced plasticity at these central entry points? Does the deprivation duration correlate with specific plasticity mechanisms? This study systematically reviews and meta-analyzes research papers that investigated visual, auditory, or olfactory deprivation in rodents of both sexes. It examines the consequences of sensory deprivation in homologous regions at the first central synapse following cranial nerve transmission (vision - lateral geniculate nucleus and superior colliculus; audition - ventral and dorsal cochlear nucleus; olfaction - olfactory bulb). The systematic search yielded 91 papers (39 vision, 22 audition, 30 olfaction), revealing substantial heterogeneity in publication trends, experimental methods, measures of plasticity, and reporting across the sensory modalities. Despite these differences, commonalities emerged when correlating plasticity mechanisms with the duration of sensory deprivation. Short-term deprivation (up to 1 d) reduced activity and increased disinhibition, medium-term deprivation (1 d to a week) involved glial changes and synaptic remodeling, and long-term deprivation (over a week) primarily led to structural alterations. These findings underscore the importance of standardizing methodologies and reporting practices. Additionally, they highlight the value of cross-modal synthesis for understanding how the nervous system, including peripheral, precortical, and cortical areas, respond to and compensate for sensory inputs loss.

Disuse-driven plasticity in the human thalamus and putamen

Motor adaptation in cortico-striato-thalamo-cortical loops has been studied mainly in animals using invasive electrophysiology. Here, we leverage functional neuroimaging in humans to study motor circuit plasticity in the human subcortex. We employed an experimental paradigm that combined two weeks of upper-extremity immobilization with daily resting-state and motor task fMRI before, during, and after the casting period. We previously showed that limb disuse leads to decreased functional connectivity (FC) of the contralateral somatomotor cortex (SM1) with the ipsilateral somatomotor cortex, increased FC with the cingulo-opercular network (CON) as well as the emergence of high amplitude, fMRI signal pulses localized in the contralateral SM1, supplementary motor area and the cerebellum. From our prior observations, it remains unclear whether the disuse plasticity affects the thalamus and striatum. We extended our analysis to include these subcortical regions and found that both exhibit strengthened cortical FC and spontaneous fMRI signal pulses induced by limb disuse. The dorsal posterior putamen and the central thalamus, mainly CM, VLP and VIM nuclei, showed disuse pulses and FC changes that lined up with fmri task activations from the Human connectome project motor system localizer, acquired before casting for each participant. Our findings provide a novel understanding of the role of the cortico-striato-thalamo-cortical loops in human motor plasticity and a potential link with the physiology of sleep regulation. Additionally, similarities with FC observation from Parkinson Disease (PD) questions a pathophysiological link with limb disuse.

Contribution of membrane-associated oscillators to biological timing at different timescales

Environmental rhythms such as the daily light-dark cycle selected for endogenous clocks. These clocks predict regular environmental changes and provide the basis for well-timed adaptive homeostasis in physiology and behavior of organisms. Endogenous clocks are oscillators that are based on positive feedforward and negative feedback loops. They generate stable rhythms even under constant conditions. Since even weak interactions between oscillators allow for autonomous synchronization, coupling/synchronization of oscillators provides the basis of self-organized physiological timing. Amongst the most thoroughly researched clocks are the endogenous circadian clock neurons in mammals and insects. They comprise nuclear clockworks of transcriptional/translational feedback loops (TTFL) that generate ∼24 h rhythms in clock gene expression entrained to the environmental day-night cycle. It is generally assumed that this TTFL clockwork drives all circadian oscillations within and between clock cells, being the basis of any circadian rhythm in physiology and behavior of organisms. Instead of the current gene-based hierarchical clock model we provide here a systems view of timing. We suggest that a coupled system of autonomous TTFL and posttranslational feedback loop (PTFL) oscillators/clocks that run at multiple timescales governs adaptive, dynamic homeostasis of physiology and behavior. We focus on mammalian and insect neurons as endogenous oscillators at multiple timescales. We suggest that neuronal plasma membrane-associated signalosomes constitute specific autonomous PTFL clocks that generate localized but interlinked oscillations of membrane potential and intracellular messengers with specific endogenous frequencies. In each clock neuron multiscale interactions of TTFL and PTFL oscillators/clocks form a temporally structured oscillatory network with a common complex frequency-band comprising superimposed multiscale oscillations. Coupling between oscillator/clock neurons provides the next level of complexity of an oscillatory network. This systemic dynamic network of molecular and cellular oscillators/clocks is suggested to form the basis of any physiological homeostasis that cycles through dynamic homeostatic setpoints with a characteristic frequency-band as hallmark. We propose that mechanisms of homeostatic plasticity maintain the stability of these dynamic setpoints, whereas Hebbian plasticity enables switching between setpoints via coupling factors, like biogenic amines and/or neuropeptides. They reprogram the network to a new common frequency, a new dynamic setpoint. Our novel hypothesis is up for experimental challenge.

Direct Current Stimulation Modulates Synaptic Facilitation via Distinct Presynaptic Calcium Channels

Transcranial direct current stimulation (tDCS) is a subthreshold neurostimulation technique known for ameliorating neuropsychiatric conditions. The principal mechanism of tDCS is the differential polarization of subcellular neuronal compartments, particularly the axon terminals that are sensitive to external electrical fields. Yet, the underlying mechanism of tDCS is not fully clear. Here, we hypothesized that direct current stimulation (DCS)-induced modulation of presynaptic calcium channel conductance alters axon terminal dynamics with regard to synaptic vesicle release. To examine the involvement of calcium-channel subtypes in tDCS, we recorded spontaneous excitatory postsynaptic currents (sEPSCs) from cortical layer-V pyramidal neurons under DCS while selectively inhibiting distinct subtypes of voltage-dependent calcium channels. Blocking P/Q or N-type calcium channels occluded the effects of DCS on sEPSCs, demonstrating their critical role in the process of DCS-induced modulation of spontaneous vesicle release. However, inhibiting T-type calcium channels did not occlude DCS-induced modulation of sEPSCs, suggesting that despite being active in the subthreshold range, T-type calcium channels are not involved in the axonal effects of DCS. DCS modulates synaptic facilitation by regulating calcium channels in axon terminals, primarily via controlling P/Q and N-type calcium channels, while T-type calcium channels are not involved in this mechanism.

A human in vitro neuronal model for studying homeostatic plasticity at the network level

Mechanisms that underlie homeostatic plasticity have been extensively investigated at single-cell levels in animal models, but are less well understood at the network level. Here, we used microelectrode arrays to characterize neuronal networks following induction of homeostatic plasticity in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons co-cultured with rat astrocytes. Chronic suppression of neuronal activity through tetrodotoxin (TTX) elicited a time-dependent network re-arrangement. Increased expression of AMPA receptors and the elongation of axon initial segments were associated with increased network excitability following TTX treatment. Transcriptomic profiling of TTX-treated neurons revealed up-regulated genes related to extracellular matrix organization, while down-regulated genes related to cell communication; also astrocytic gene expression was found altered. Overall, our study shows that hiPSC-derived neuronal networks provide a reliable in vitro platform to measure and characterize homeostatic plasticity at network and single-cell levels; this platform can be extended to investigate altered homeostatic plasticity in brain disorders.

Neural Plasticity in Sensorimotor Brain-Machine Interfaces

Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.

Dynamics of AMPA receptors regulate epileptogenesis in patients with epilepsy

The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs) contribute to epileptogenesis. Thirty patients with epilepsy and 31 healthy controls are scanned using positron emission tomography with our recently developed radiotracer for AMPARs, [¹¹C]K-2, which measures the density of cell-surface AMPARs. In patients with focal-onset seizures, an increase in AMPAR trafficking augments the amplitude of abnormal gamma activity detected by electroencephalography. In contrast, patients with generalized-onset seizures exhibit a decrease in AMPARs coupled with increased amplitude of abnormal gamma activity. Patients with epilepsy had reduced AMPAR levels compared with healthy controls, and AMPARs are reduced in larger areas of the cortex in patients with generalized-onset seizures compared with those with focal-onset seizures. Thus, epileptic brain function can be regulated by the enhanced trafficking of AMPAR due to Hebbian plasticity with increased simultaneous neuronal firing and compensational downregulation of cell-surface AMPARs by the synaptic scaling.

A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing

Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving.

Visual Plasticity in Adulthood: Perspectives from Hebbian and Homeostatic Plasticity

The visual system retains profound plastic potential in adulthood. In the current review, we summarize the evidence of preserved plasticity in the adult visual system during visual perceptual learning as well as both monocular and binocular visual deprivation. In each condition, we discuss how such evidence reflects two major cellular mechanisms of plasticity: Hebbian and homeostatic processes. We focus on how these two mechanisms work together to shape plasticity in the visual system. In addition, we discuss how these two mechanisms could be further revealed in future studies investigating cross-modal plasticity in the visual system.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.

Spike-Timing-Dependent Plasticity With Activation-Dependent Scaling for Receptive Fields Development

Spike-timing-dependent plasticity (STDP) is one of the most popular and deeply biologically motivated forms of unsupervised Hebbian-type learning. In this article, we propose a variant of STDP extended by an additional activation-dependent scale factor. The consequent learning rule is an efficient algorithm, which is simple to implement and applicable to spiking neural networks (SNNs). It is demonstrated that the proposed plasticity mechanism combined with competitive learning can serve as an effective mechanism for the unsupervised development of receptive fields (RFs). Furthermore, the relationship between synaptic scaling and lateral inhibition is explored in the context of the successful development of RFs. Specifically, we demonstrate that maintaining a high level of synaptic scaling followed by its rapid increase is crucial for the development of neuronal mechanisms of selectivity. The strength of the proposed solution is assessed in classification tasks performed on the Modified National Institute of Standards and Technology (MNIST) data set with an accuracy level of 94.65% (a single network) and 95.17% (a network committee)-comparable to the state-of-the-art results of single-layer SNN architectures trained in an unsupervised manner. Furthermore, the training process leads to sparse data representation and the developed RFs have the potential to serve as local feature detectors in multilayered spiking networks. We also prove theoretically that when applied to linear Poisson neurons, our rule conserves total synaptic strength, guaranteeing the convergence of the learning process.

Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback

There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence.

The regulatory role of NAAG-mGluR3 signaling on cortical synaptic plasticity after hypoxic ischemia

Background

Synapses can adapt to changes in the intracerebral microenvironment by regulation of presynaptic neurotransmitter release and postsynaptic neurotransmitter receptor expression following hypoxic ischemia (HI) injury. The peptide neurotransmitter N-acetylaspartylglutamate (NAAG) exerts a protective effect on neurons after HI and may be involved in maintaining the function of synaptic networks. In this study, we investigated the changes in the expression of NAAG, glutamic acid (Glu) and metabotropic glutamate receptors (mGluRs), as well as the dynamic regulation of neurotransmitters in the brain after HI, and assessed their effects on synaptic plasticity of the cerebral cortex.

Methods

Thirty-six Yorkshire newborn pigs (3-day-old, males, 1.0–1.5 kg) were selected and randomly divided into normal saline (NS) group (n = 18) and glutamate carboxypeptidase II inhibition group (n = 18), both groups were divided into control group, 0–6 h, 6–12 h, 12–24 h, 24–48 h and 48–72 h groups (all n = 3) according to different post-HI time. The content of Glu and NAAG after HI injury were detected by 1H-MRS scanning, immunofluorescence staining of mGluRs, synaptophysin (syph) along with postsynaptic density protein-95 (PSD95) and transmission electron microscopy were performed. ANOVA, Tukey and LSD test were used to compare the differences in metabolite and protein expression levels among subgroups. Correlation analysis was performed using Pearson analysis with a significance level of α = 0.05.

Results

We observed that the NAAG and mGluR3 expression levels in the brain increased and then decreased after HI and was significantly higher in the 12–24 h (P < 0.05, Tukey test). There was a significant positive correlation between Glu content and the expression of mGluR1/mGluR5 after HI with r = 0.521 (P = 0.027) and r = 0.477 (P = 0.045), respectively. NAAG content was significantly and positively correlated with the level of mGluR3 expression (r = 0.472, P = 0.048). When hydrolysis of NAAG was inhibited, the expression of synaptic protein PSD95 and syph decreased significantly.

Conclusions

After 12–24 h of HI injury, there was a one-time elevation in NAAG levels, which was consistent with the corresponding mGluR3 receptor expression trend; the NAAG maintains cortical synaptic plasticity and neurotransmitter homeostasis by inhibiting presynaptic glutamate vesicle release, regulating postsynaptic density proteins and postsynaptic receptor expression after pathway activation.

Postsynaptic Potential Energy as Determinant of Synaptic Plasticity

Metabolic energy can be used as a unifying principle to control neuronal activity. However, whether and how metabolic energy alone can determine the outcome of synaptic plasticity remains unclear. This study proposes a computational model of synaptic plasticity that is completely determined by energy. A simple quantitative relationship between synaptic plasticity and postsynaptic potential energy is established. Synaptic weight is directly proportional to the difference between the baseline potential energy and the suprathreshold potential energy and is constrained by the maximum energy supply. Results show that the energy constraint improves the performance of synaptic plasticity and avoids setting the hard boundary of synaptic weights. With the same set of model parameters, our model can reproduce several classical experiments in homo- and heterosynaptic plasticity. The proposed model can explain the interaction mechanism of Hebbian and homeostatic plasticity at the cellular level. Homeostatic synaptic plasticity at different time scales coexists. Homeostatic plasticity operating on a long time scale is caused by heterosynaptic plasticity and, on the same time scale as Hebbian synaptic plasticity, is caused by the constraint of energy supply.

Heterosynaptic Plasticity and the Experience-Dependent Refinement of Developing Neuronal Circuits

Neurons remodel the structure and strength of their synapses during critical periods of development in order to optimize both perception and cognition. Many of these developmental synaptic changes are thought to occur through synapse-specific homosynaptic forms of experience-dependent plasticity. However, homosynaptic plasticity can also induce or contribute to the plasticity of neighboring synapses through heterosynaptic interactions. Decades of research in vitro have uncovered many of the molecular mechanisms of heterosynaptic plasticity that mediate local compensation for homosynaptic plasticity, facilitation of further bouts of plasticity in nearby synapses, and cooperative induction of plasticity by neighboring synapses acting in concert. These discoveries greatly benefited from new tools and technologies that permitted single synapse imaging and manipulation of structure, function, and protein dynamics in living neurons. With the recent advent and application of similar tools for in vivo research, it is now feasible to explore how heterosynaptic plasticity contribute to critical periods and the development of neuronal circuits. In this review, we will first define the forms heterosynaptic plasticity can take and describe our current understanding of their molecular mechanisms. Then, we will outline how heterosynaptic plasticity may lead to meaningful refinement of neuronal responses and observations that suggest such mechanisms are indeed at work in vivo. Finally, we will use a well-studied model of cortical plasticity—ocular dominance plasticity during a critical period of visual cortex development—to highlight the molecular overlap between heterosynaptic and developmental forms of plasticity, and suggest potential avenues of future research.

A Correspondence Between Normalization Strategies in Artificial and Biological Neural Networks

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalization, weight normalization, and their many variants help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this article, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neuron's activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent-that is, both push activation patterns of hidden units toward a homeostatic state, where all neurons are equally used-and we argue that such representations can improve coding capacity, discrimination, and regularization. As a proof of concept, we develop an algorithm, inspired by a neural normalization technique called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several data sets. Overall, we hope this bidirectional connection will inspire neuroscientists and machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

Memory destabilization during reconsolidation: a consequence of homeostatic plasticity?

Remembering is not a static process: When retrieved, a memory can be destabilized and become prone to modifications. This phenomenon has been demonstrated in a number of brain regions, but the neuronal mechanisms that rule memory destabilization and its boundary conditions remain elusive. Using two distinct computational models that combine Hebbian plasticity and synaptic downscaling, we show that homeostatic plasticity can function as a destabilization mechanism, accounting for behavioral results of protein synthesis inhibition upon reactivation with different re-exposure times. Furthermore, by performing systematic reviews, we identify a series of overlapping molecular mechanisms between memory destabilization and synaptic downscaling, although direct experimental links between both phenomena remain scarce. In light of these results, we propose a theoretical framework where memory destabilization can emerge as an epiphenomenon of homeostatic adaptations prompted by memory retrieval.

The critical period: neurochemical and synaptic mechanisms shared by the visual cortex and the brain stem respiratory system

The landmark studies of Wiesel and Hubel in the 1960's initiated a surge of investigations into the critical period of visual cortical development, when abnormal visual experience can alter cortical structures and functions. Most studies focused on the visual cortex, with relatively little attention to subcortical structures. The goal of the present review is to elucidate neurochemical and synaptic mechanisms common to the critical periods of the visual cortex and the brain stem respiratory system in the normal rat. In both regions, the critical period is a time of (i) heightened inhibition; (ii) reduced expression of brain-derived neurotrophic factor (BDNF); and (iii) synaptic imbalance, with heightened inhibition and suppressed excitation. The last two mechanisms are contrary to the conventional premise. Synaptic imbalance renders developing neurons more vulnerable to external stressors. However, the critical period is necessary to enable each system to strengthen its circuitry, adapt to its environment, and transition from immaturity to maturity, when a state of relative synaptic balance is attained. Failure to achieve such a balance leads to neurological disorders.

A Brain-Inspired Homeostatic Neuron Based on Phase-Change Memories for Efficient Neuromorphic Computing

One of the main goals of neuromorphic computing is the implementation and design of systems capable of dynamic evolution with respect to their own experience. In biology, synaptic scaling is the homeostatic mechanism which controls the frequency of neural spikes within stable boundaries for improved learning activity. To introduce such control mechanism in a hardware spiking neural network (SNN), we present here a novel artificial neuron based on phase change memory (PCM) devices capable of internal regulation via homeostatic and plastic phenomena. We experimentally show that this mechanism increases the robustness of the system thus optimizing the multi-pattern learning under spike-timing-dependent plasticity (STDP). It also improves the continual learning capability of hybrid supervised-unsupervised convolutional neural networks (CNNs), in terms of both resilience and accuracy. Furthermore, the use of neurons capable of self-regulating their fire responsivity as a function of the PCM internal state enables the design of dynamic networks. In this scenario, we propose to use the PCM-based neurons to design bio-inspired recurrent networks for autonomous decision making in navigation tasks. The agent relies on neuronal spike-frequency adaptation (SFA) to explore the environment via penalties and rewards. Finally, we show that the conductance drift of the PCM devices, contrarily to the applications in neural network accelerators, can improve the overall energy efficiency of neuromorphic computing by implementing bio-plausible active forgetting.

Extracellular Protons Mediate Presynaptic Homeostatic Potentiation at the Mouse Neuromuscular Junction

At the vertebrate neuromuscular junction (NMJ), presynaptic homeostatic potentiation (PHP) refers to the upregulation of neurotransmitter release via an increase in quantal content (QC) when the postsynaptic nicotinic acetylcholine receptors (nAChRs) are partially blocked. The mechanism of PHP has not been completely worked out. In particular, the identity of the presumed retrograde signal is still a mystery. We investigated the role of acid-sensing ion channels (ASICs) and extracellular protons in mediating PHP at the mouse NMJ. We found that blocking AISCs using benzamil, psalmotoxin-1 (PcTx1), or mambalgin-3 (Mamb3) prevented PHP. Likewise, extracellular acidification from pH 7.4 to 7.2 triggered a significant, reversable increase in QC and this increase could be prevented by PcTx1. Interestingly, an acidic saline (pH 7.2) also precluded the subsequent induction of PHP. Using immunofluorescence we observed ASIC2a and ASIC1 subunits at the NMJ. Our results indicate that protons and ASIC channels are involved in activating PHP at the mouse NMJ. We speculate that the partial blockade of nAChRs leads to a modest decrease in the pH of the synaptic cleft (∼0.2 pH units) and this activates ASIC channels on the presynaptic nerve terminal.

Brief Sensory Deprivation Triggers Cell Type-Specific Structural and Functional Plasticity in Olfactory Bulb Neurons

Can alterations in experience trigger different plastic modifications in neuronal structure and function, and if so, how do they integrate at the cellular level? To address this question, we interrogated circuitry in the mouse olfactory bulb responsible for the earliest steps in odor processing. We induced experience-dependent plasticity in mice of either sex by blocking one nostril for one day, a minimally invasive manipulation that leaves the sensory organ undamaged and is akin to the natural transient blockage suffered during common mild rhinal infections. We found that such brief sensory deprivation produced structural and functional plasticity in one highly specialized bulbar cell type: axon-bearing dopaminergic neurons in the glomerular layer. After 24 h naris occlusion, the axon initial segment (AIS) in bulbar dopaminergic neurons became significantly shorter, a structural modification that was also associated with a decrease in intrinsic excitability. These effects were specific to the AIS-positive dopaminergic subpopulation because no experience-dependent alterations in intrinsic excitability were observed in AIS-negative dopaminergic cells. Moreover, 24 h naris occlusion produced no structural changes at the AIS of bulbar excitatory neurons, mitral/tufted and external tufted cells, nor did it alter their intrinsic excitability. By targeting excitability in one specialized dopaminergic subpopulation, experience-dependent plasticity in early olfactory networks might act to fine-tune sensory processing in the face of continually fluctuating inputs.SIGNIFICANCE STATEMENT Sensory networks need to be plastic so they can adapt to changes in incoming stimuli. To see how cells in mouse olfactory circuits can change in response to sensory challenges, we blocked a nostril for just one day, a naturally relevant manipulation akin to the deprivation that occurs with a mild cold. We found that this brief deprivation induces forms of axonal and intrinsic functional plasticity in one specific olfactory bulb cell subtype: axon-bearing dopaminergic interneurons. In contrast, intrinsic properties of axon-lacking bulbar dopaminergic neurons and neighboring excitatory neurons remained unchanged. Within the same sensory circuits, specific cell types can therefore make distinct plastic changes in response to an ever-changing external landscape.

The role of astrocyte-mediated plasticity in neural circuit development and function

Neuronal networks are capable of undergoing rapid structural and functional changes called plasticity, which are essential for shaping circuit function during nervous system development. These changes range from short-term modifications on the order of milliseconds, to long-term rearrangement of neural architecture that could last for the lifetime of the organism. Neural plasticity is most prominent during development, yet also plays a critical role during memory formation, behavior, and disease. Therefore, it is essential to define and characterize the mechanisms underlying the onset, duration, and form of plasticity. Astrocytes, the most numerous glial cell type in the human nervous system, are integral elements of synapses and are components of a glial network that can coordinate neural activity at a circuit-wide level. Moreover, their arrival to the CNS during late embryogenesis correlates to the onset of sensory-evoked activity, making them an interesting target for circuit plasticity studies. Technological advancements in the last decade have uncovered astrocytes as prominent regulators of circuit assembly and function. Here, we provide a brief historical perspective on our understanding of astrocytes in the nervous system, and review the latest advances on the role of astroglia in regulating circuit plasticity and function during nervous system development and homeostasis.

Tumour Necrosis Factor in Neuroplasticity, Neurogenesis and Alcohol Use Disorder

Alcohol use disorder is a pervasive and detrimental condition that involves changes in neuroplasticity and neurogenesis. Alcohol activates the neuroimmune system and alters the inflammatory status of the brain. Tumour necrosis factor (TNF) is a well characterised neuroimmune signal but its involvement in alcohol use disorder is unknown. In this review, we discuss the variable findings of TNF's effect on neuroplasticity and neurogenesis. Acute ethanol exposure reduces TNF release while chronic alcohol intake generally increases TNF levels. Evidence suggests TNF potentiates excitatory transmission, promotes anxiety during alcohol withdrawal and is involved in drug use in rodents. An association between craving for alcohol and TNF is apparent during withdrawal in humans. While anti-inflammatory therapies show efficacy in reversing neurogenic deficit after alcohol exposure, there is no evidence for TNF's essential involvement in alcohol's effect on neurogenesis. Overall, defining TNF's role in alcohol use disorder is complicated by poor understanding of its variable effects on synaptic transmission and neurogenesis. While TNF may be of relevance during withdrawal, the neuroimmune system likely acts through a larger group of inflammatory cytokines to alter neuroplasticity and neurogenesis. Understanding the individual relevance of TNF in alcohol use disorder awaits a more comprehensive understanding of TNF's effects within the brain.

GRIP1 regulates synaptic plasticity and learning and memory

Hebbian plasticity is a key mechanism for higher brain functions, such as learning and memory. This form of synaptic plasticity primarily involves the regulation of synaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) abundance and properties, whereby AMPARs are inserted into synapses during long-term potentiation (LTP) or removed during long-term depression (LTD). The molecular mechanisms underlying AMPAR trafficking remain elusive, however. Here we show that glutamate receptor interacting protein 1 (GRIP1), an AMPAR-binding protein shown to regulate the trafficking and synaptic targeting of AMPARs, is required for LTP and learning and memory. GRIP1 is recruited into synapses during LTP, and deletion of Grip1 in neurons blocks synaptic AMPAR accumulation induced by glycine-mediated depolarization. In addition, Grip1 knockout mice exhibit impaired hippocampal LTP, as well as deficits in learning and memory. Mechanistically, we find that phosphorylation of serine-880 of the GluA2 AMPAR subunit (GluA2-S880) is decreased while phosphorylation of tyrosine-876 on GluA2 (GluA2-Y876) is elevated during chemically induced LTP. This enhances the strength of the GRIP1-AMPAR association and, subsequently, the insertion of AMPARs into the postsynaptic membrane. Together, these results demonstrate an essential role of GRIP1 in regulating AMPAR trafficking during synaptic plasticity and learning and memory.

Clinical Application of Brain Plasticity in Neurosurgery

Brain plasticity is an ongoing process of reorganization not only on the macroscopic level but also from underlying changes at the cellular and molecular levels of neurons. This evolution has not yet been fully understood. The objective of this paper is to review and understand neuroplasticity through the review of literature, imaging, and intraoperative evidence.

Virtual reality gaming as a neurorehabilitation tool for brain injuries in adults: A systematic review

OBJECTIVE

Evidence of the effectiveness of virtual reality (VR) in motor and cognitive rehabilitation for traumatic brain injury (TBI) continues to be mixed. Therefore, we conducted a systematic literature review in accordance with PRISMA guidelines to strategically evaluate the strength of evidence supporting the use of VR as a rehabilitation tool for motor function and cognition in patients with TBI.

METHOD

The van Tulder criteria were modified to determine the quality of the outcomes of studies deemed eligible for inclusion in the review.

OUTCOMES AND RESULTS

Twelve studies were considered eligible for inclusion in the systematic review. These studies utilized methods of varying quality such as case and quasi-experimental studies and found moderately positive support for the effectiveness of VR-enhanced rehabilitation for both motor skills and cognitive deficits.

CONCLUSIONS AND IMPLICATIONS

The varying quality of the included studies provides moderate support for use of VR-enhanced rehabilitation techniques per the van Tulder criteria. This highlights the continued gap in the literature for robust studies that enable providers, policy makers, and the public to draw conclusions about the effectiveness of VR-enhanced rehabilitation for traumatic brain injury. Continued pursuit of analyses in the context of newer immersive VR-enhanced rehabilitation is recommended.

Hebbian and Homeostatic Synaptic Plasticity-Do Alterations of One Reflect Enhancement of the Other?

During the past 50 years, the cellular and molecular mechanisms of synaptic plasticity have been studied in great detail. A plethora of signaling pathways have been identified that account for synaptic changes based on positive and negative feedback mechanisms. Yet, the biological significance of Hebbian synaptic plasticity (= positive feedback) and homeostatic synaptic plasticity (= negative feedback) remains a matter of debate. Specifically, it is unclear how these opposing forms of plasticity, which share common downstream mechanisms, operate in the same networks, neurons, and synapses. Based on the observation that rapid and input-specific homeostatic mechanisms exist, we here discuss a model that is based on signaling pathways that may adjust a balance between Hebbian and homeostatic synaptic plasticity. Hence, “alterations” in Hebbian plasticity may, in fact, resemble “enhanced” homeostasis, which rapidly returns synaptic strength to baseline. In turn, long-lasting experience-dependent synaptic changes may require attenuation of homeostatic mechanisms or the adjustment of homeostatic setpoints at the single-synapse level. In this context, we propose a role for the proteolytic processing of the amyloid precursor protein (APP) in setting a balance between the ability of neurons to express Hebbian and homeostatic synaptic plasticity.

An inexpensive programmable optogenetic platform for controlled neuronal activation regimens in C. elegans

In Caenorhabditis elegans, optogenetic stimulation has been widely used to assess neuronal function, control animal movement, or assay circuit responses to controlled stimuli. Most studies are performed on single animals and require high-end components such as lasers and shutters. We present an accessible platform that enables controlled optogenetic stimulation of C. elegans in two modes: single animal stimulation with locomotion tracking and entire population stimulation for neuronal exercise regimens. The system consists of accessible electronic components: a high-power light-emitting diode, Arduino board, and relay are integrated with MATLAB to enable programmable optogenetic stimulation regimens. This system provides flexibility in optogenetic stimulation in freely moving animals while providing quantitative information of optogenetic-driven locomotion responses. We show the applicability of this platform in single animals by stimulation of cholinergic motor neurons in C. elegans and quantitative assessment of contractile responses. In addition, we tested synaptic plasticity by coupling the entire-population stimulation mode with measurements of synaptic strength using an aldicarb assay, where clear changes in synaptic strength were observed after regimens of neuronal exercise. This platform is composed of inexpensive components, while providing the illumination strength of high-end systems, which require expensive lasers, shutters, or automated stages. This platform requires no moving parts but provides flexibility in stimulation regimens.

Disruption of NMDAR Function Prevents Normal Experience-Dependent Homeostatic Synaptic Plasticity in Mouse Primary Visual Cortex

Homeostatic regulation of synaptic strength allows for maintenance of neural activity within a dynamic range for proper circuit function. There are largely two distinct modes of synaptic plasticity that allow for homeostatic adaptation of cortical circuits: synaptic scaling and sliding threshold (BCM theory). Previous findings suggest that the induction of synaptic scaling is not prevented by blocking NMDARs, whereas the sliding threshold model posits that the synaptic modification threshold of LTP and LTD readjusts with activity and thus the outcome of synaptic plasticity is NMDAR dependent. Although synaptic scaling and sliding threshold have been considered two distinct mechanisms, there are indications from recent studies that these two modes of homeostatic plasticity may interact or that they may operate under two distinct activity regimes. Here, we report using both sexes of mouse that acute genetic knock-out of the obligatory subunit of NMDAR or acute pharmacological block of NMDAR prevents experience-dependent homeostatic regulation of AMPAR-mediated miniature EPSCs in layer 2/3 of visual cortex. This was not due to gross changes in postsynaptic neuronal activity with inhibiting NMDAR function as determine by c-Fos expression and two-photon Ca²⁺ imaging in awake mice. Our results suggest that experience-dependent homeostatic regulation of intact cortical circuits is mediated by NMDAR-dependent plasticity mechanisms, which supports a sliding threshold model of homeostatic adaptation.SIGNIFICANCE STATEMENT Prolonged changes in sensory experience lead to homeostatic adaptation of excitatory synaptic strength in sensory cortices. Both sliding threshold and synaptic scaling models can account for the observed homeostatic synaptic plasticity. Here we report that visual experience-dependent homeostatic plasticity of excitatory synapses observed in superficial layers of visual cortex is dependent on NMDAR function. In particular, both strengthening of synapses induced by visual deprivation and the subsequent weakening by reinstatement of visual experience were prevented in the absence of functional NMDARs. Our results suggest that sensory experience-dependent homeostatic adaptation depends on NMDARs, which supports the sliding threshold model of plasticity and input-specific homeostatic control observed in vivo.

Dendritic spines: Revisiting the physiological role

Dendritic spines are small, thin, specialized protrusions from neuronal dendrites, primarily localized in the excitatory synapses. Sophisticated imaging techniques revealed that dendritic spines are complex structures consisting of a dense network of cytoskeletal, transmembrane and scaffolding molecules, and numerous surface receptors. Molecular signaling pathways, mainly Rho and Ras family small GTPases pathways that converge on actin cytoskeleton, regulate the spine morphology and dynamics bi-directionally during synaptic activity. During synaptic plasticity the number and shapes of dendritic spines undergo radical reorganizations. Long-term potentiation (LTP) induction promote spine head enlargement and the formation and stabilization of new spines. Long-term depression (LTD) results in their shrinkage and retraction. Reports indicate increased spine density in the pyramidal neurons of autism and Fragile X syndrome patients and reduced density in the temporal gyrus loci of schizophrenic patients. Post-mortem reports of Alzheimer's brains showed reduced spine number in the hippocampus and cortex. This review highlights the spine morphogenesis process, the activity-dependent structural plasticity and mechanisms by which synaptic activity sculpts the dendritic spines, the structural and functional changes in spines during learning and memory using LTP and LTD processes. It also discusses on spine status in neurodegenerative diseases and the impact of nootropics and neuroprotective agents on the functional restoration of dendritic spines.

Remembering to Forget: A Dual Role for Sleep Oscillations in Memory Consolidation and Forgetting

It has been known since the time of patient H. M. and Karl Lashley's equipotentiality studies that the hippocampus and cortex serve mnestic functions. Current memory models maintain that these two brain structures accomplish unique, but interactive, memory functions. Specifically, most modeling suggests that memories are rapidly acquired during waking experience by the hippocampus, before being later consolidated into the cortex for long-term storage. Sleep has been shown to be critical for the transfer and consolidation of memories in the cortex. Like memory consolidation, a role for sleep in adaptive forgetting has both historical precedent, as Francis Crick suggested in 1983 that sleep was for "reverse-learning," and recent empirical support. In this article I review the evidence indicating that the same brain activity involved in sleep replay associated memory consolidation is responsible for sleep-dependent forgetting. In reviewing the literature, it became clear that both a cellular mechanism for systems consolidation and an agreed upon general, as well as cellular, mechanism for sleep-dependent forgetting is seldom discussed or is lacking. I advocate here for a candidate cellular systems consolidation mechanism wherein changes in calcium kinetics and the activation of consolidative signaling cascades arise from the triple phase locking of non-rapid eye movement sleep (NREMS) slow oscillation, sleep spindle and sharp-wave ripple rhythms. I go on to speculatively consider several sleep stage specific forgetting mechanisms and conclude by discussing a notional function of NREM-rapid eye movement sleep (REMS) cycling. The discussed model argues that the cyclical organization of sleep functions to first lay down and edit and then stabilize and integrate engrams. All things considered, it is increasingly clear that hallmark sleep stage rhythms, including several NREMS oscillations and the REMS hippocampal theta rhythm, serve the dual function of enabling simultaneous memory consolidation and adaptive forgetting. Specifically, the same sleep rhythms that consolidate new memories, in the cortex and hippocampus, simultaneously organize the adaptive forgetting of older memories in these brain regions.

Homeostatic synaptic plasticity as a metaplasticity mechanism - a molecular and cellular perspective

The molecular mechanisms underlying various types of synaptic plasticity are historically regarded as separate processes involved in independent cellular events. However, recent progress in our molecular understanding of Hebbian and homeostatic synaptic plasticity supports the observation that these two types of plasticity share common cellular events, and are often altered together in neurological diseases. Here, we discuss the emerging concept of homeostatic synaptic plasticity as a metaplasticity mechanism with a focus on cellular signaling processes that enable a direct interaction between Hebbian and homeostatic plasticity. We also identify distinct and shared molecular players involved in these cellular processes that may be explored experimentally in future studies to test the hypothesis that homeostatic synaptic plasticity serves as a metaplasticity mechanism to integrate changes in neuronal activity and support optimal Hebbian learning.

Increased Corticomuscular Coherence and Brain Activation Immediately After Short-Term Neuromuscular Electrical Stimulation

Neuromuscular Electrical Stimulation (NMES) is commonly used in motor rehabilitation for stroke patients. It has been verified that NMES can improve muscle strength and activate the brain, but the studies on how NMES affects the corticomuscular connection are limited. Some studies found an increased corticomuscular coherence (CMC) after a long-term NMES. However, it is still unknown about CMC during NMES, as relatively pure EMG is very difficult to obtain with the contamination of NMES current pulses. In order to approach the condition during NMES, we designed an experiment with short-term NMES and immediately captured data within 100 s. The repetition of wrist flexion was used to realize static muscle contractions for CMC calculation and dynamic contractions for event-related desynchronization (ERD). The result of 13 healthy participants showed that maximal values (p = 0.0020) and areas (p = 0.0098) of CMC and beta ERD were significantly increased immediately after NMES. It was concluded that a short-term NMES can still reinforce corticomuscular functional connection and brain activation related to motor task. This study verified the immediate strengthen of corticomuscular changes after NMES, which was expected to be the basis of long-term neural plasticity induced by NMES.

Homeostatic plasticity in neural development

Throughout life, neural circuits change their connectivity, especially during development, when neurons frequently extend and retract dendrites and axons, and form and eliminate synapses. In spite of their changing connectivity, neural circuits maintain relatively constant activity levels. Neural circuits achieve functional stability by homeostatic plasticity, which equipoises intrinsic excitability and synaptic strength, balances network excitation and inhibition, and coordinates changes in circuit connectivity. Here, we review how diverse mechanisms of homeostatic plasticity stabilize activity in developing neural circuits.

Targeted cortical reorganization using optogenetics in non-human primates

Brain stimulation modulates the excitability of neural circuits and drives neuroplasticity. While the local effects of stimulation have been an active area of investigation, the effects on large-scale networks remain largely unexplored. We studied stimulation-induced changes in network dynamics in two macaques. A large-scale optogenetic interface enabled simultaneous stimulation of excitatory neurons and electrocorticographic recording across primary somatosensory (S1) and motor (M1) cortex (Yazdan-Shahmorad et al., 2016). We tracked two measures of network connectivity, the network response to focal stimulation and the baseline coherence between pairs of electrodes; these were strongly correlated before stimulation. Within minutes, stimulation in S1 or M1 significantly strengthened the gross functional connectivity between these areas. At a finer scale, stimulation led to heterogeneous connectivity changes across the network. These changes reflected the correlations introduced by stimulation-evoked activity, consistent with Hebbian plasticity models. This work extends Hebbian plasticity models to large-scale circuits, with significant implications for stimulation-based neurorehabilitation.

From riding a bike to reaching for a cup of coffee, all skilled actions rely on precise connections between the sensory and motor areas of the brain. While sensory areas receive and analyse input from the senses, motor areas plan and trigger muscle contractions. Precisely adjusting the connections between these and other areas enables us to learn new skills, and it also helps us to relearn skills lost as a result of brain injury or stroke.

About 70 years ago, a psychologist named Donald Hebb came up with an idea for how this process might occur. He proposed that whenever two neurons are active at the same time, the connection between them becomes stronger. This idea, that ‘cells that fire together, wire together’, became known as Hebb’s rule. Many studies have since shown that Hebb’s rule can explain changes in the strength of connections between pairs of neurons. But can it also explain how connections between entire brain regions become stronger or weaker?

New results show that it can. The data were obtained using a technique called optogenetics, in which viruses are used to introduce genes for light-sensitive proteins into neurons. Shining light onto the brain will then activate any cells within that area that contain the resulting proteins. Yazdan-Shahmorad, Silversmith et al. used this technique to activate small regions of either sensory or motor brain tissue in live macaque monkeys. Doing so strengthened the overall connectivity between the two areas. The effects were more variable at the level of smaller brain regions, with some connections becoming weaker rather than stronger. However, Yazdan-Shahmorad, Silversmith et al. show that Hebb’s rule explains most of the observed changes.

Many neurological and psychiatric disorders stem from abnormal brain connectivity. Simple forms of brain stimulation are already used to treat certain neurological disorders, such as Parkinson’s disease. Stimulating the brain to induce specific changes in connectivity may ultimately enable us to leverage the brain’s natural learning mechanisms to cure, instead of just treat, these conditions.

Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks

Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.