Homeostatic plasticity in the CNS: synaptic and intrinsic forms

The study of experience-dependent plasticity has been dominated by questions of how Hebbian plasticity mechanisms act during learning and development. This is unsurprising as Hebbian plasticity constitutes the most fully developed and influential model of how information is stored in neural circuits and how neural circuitry can develop without extensive genetic instructions. Yet Hebbian plasticity may not be sufficient for understanding either learning or development: the dramatic changes in synapse number and strength that can be produced by this kind of plasticity tend to threaten the stability of neural circuits. Recent work has suggested that, in addition to Hebbian plasticity, homeostatic regulatory mechanisms are active in a variety of preparations. These mechanisms alter both the synaptic connections between neurons and the intrinsic electrical properties of individual neurons, in such a way as to maintain some constancy in neuronal properties despite the changes wrought by Hebbian mechanisms. Here we review the evidence for homeostatic plasticity in the central nervous system, with special emphasis on results from cortical preparations.     

Reliable Neuronal Systems: The Importance of Heterogeneity

For every engineer it goes without saying: in order to build a reliable system we need components that consistently behave precisely as they should. It is also well known that neurons, the building blocks of brains, do not satisfy this constraint. Even neurons of the same type come with huge variances in their properties and these properties also vary over time. Synapses, the connections between neurons, are highly unreliable in forwarding signals. In this paper we argue that both these fact add variance to neuronal processes, and that this variance is not a handicap of neural systems, but that instead predictable and reliable functional behavior of neural systems depends crucially on this variability. In particular, we show that higher variance allows a recurrently connected neural population to react more sensitively to incoming signals, and processes them faster and more energy efficient. This, for example, challenges the general assumption that the intrinsic variability of neurons in the brain is a defect that has to be overcome by synaptic plasticity in the process of learning.     

bHLH Transcription factors and mammalian neuronal differentiation

The basic helix-loop-helix (bHLH) factor Mashl is expressed in the developing nervous system. Null mutation of Mash1 results in loss of olfactory and autonomic neurons and delays differentiation of retinal neurons, indicating that Mash1 promotes neuronal differentiation. Other bHLH genes, Math/NeuroD/Neurogenin, all expressed in the developing nervous system, have also been suggested to promote neuronal differentiation. In contrast, another bHLH factor, HES1, which is expressed by neural precursor cells but not by neurons, represses Mash1 expression and antagonizes Mash1 activity in a dominant negative manner. Forced expression of HES1 in precursor cells blocks neuronal differentiation in the brain and retina, indicating that HES1 is a negative regulator of neuronal differentiation. Conversely, null mutation of HES1 up-regulates Mash1 expression, accelerates neuronal differentiation, and causes severe defects of the brain and eyes. Thus, HES1 regulates brain and eye morphogenesis by inhibiting premature neuronal differentiation, and the down-regulation of HES1 expression at the right time is required for normal development of the nervous system. Interestingly, HES1 can repress its own expression by binding to its promoter, suggesting that negative autoregulation may contribute to down-regulation of HES1 expression during neural development. Recent studies indicate that HES1 expression is also controlled by RBP-J, a mammalian homologue of Suppressor of Hairless [Su(H)], and Notch, a key membrane protein that may regulate lateral specification through RBP-J during neural development. Thus, the Notch → RBP-J → HES1 ÷ Mash1 pathway may play a critical role in neuronal differentiation.     

Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network’s topology has received a lot of attentions in neuroscience and has been the center of many research initiatives such as Human Connectome Project. Nevertheless, direct and invasive approaches that slice and observe the neural tissue have proven to be time consuming, complex and costly. As a result, the inverse methods that utilize firing activity of neurons in order to identify the (functional) connections have gained momentum recently, especially in light of rapid advances in recording technologies; It will soon be possible to simultaneously monitor the activities of tens of thousands of neurons in real time. While there are a number of excellent approaches that aim to identify the functional connections from firing activities, the scalability of the proposed techniques plays a major challenge in applying them on large-scale datasets of recorded firing activities. In exceptional cases where scalability has not been an issue, the theoretical performance guarantees are usually limited to a specific family of neurons or the type of firing activities. In this paper, we formulate the neural network reconstruction as an instance of a graph learning problem, where we observe the behavior of nodes/neurons (i.e., firing activities) and aim to find the links/connections. We develop a scalable learning mechanism and derive the conditions under which the estimated graph for a network of Leaky Integrate and Fire (LIf) neurons matches the true underlying synaptic connections. We then validate the performance of the algorithm using artificially generated data (for benchmarking) and real data recorded from multiple hippocampal areas in rats.     

Brain Size and Number of Neurons: An Exercise in Synthetic Neuroanatomy

Certain remarkable invariances have long been known in comparative neuroanatomy, such as the proportionality between neuronal density and the inverse of the cubic root of brain volume or that between the square root of brain weight and the cubic root of body weight. Very likely these quantitative relations reflect some general principles of the architecture of neuronal networks. Under the assumption that most of brain volume is due to fibers, we propose four abstract models: I, constant fiber length per neuron; II, fiber length proportionate to brain diameter; III, complete set of connections between all neurons; IV, complete set of connections between compartments each containing the square root of the total number of neurons. Model I conforms well to the cerebellar cortex. Model II yields the observed comparative invariances between number of neurons and brain size. Model III is totally unrealistic, while Model IV is compatible with the volume of the hemispheric white substance in different mammalian species.     

Differentiation of Neuronal Cells in Fragile X Syndrome

Neural stem cells are multipotent cells which give rise to neurons and glia of the mammalian central nervous system. Recently, we found that differentiation of neural stem cells is altered in fragile X syndrome, a developmental brain disorder with disturbances in the molecular mechanisms that mediate learning and memory. The absence of fragile X mental retardation protein caused an increased number of new-born cells in the subventricular region of the embryonic mouse brain and substantial aberrances in the differentiation of both human and mouse neural stem cells in vitro. Here, alterations of neuronal cell differentiation in fragile X syndrome, the implications of our recent findings, and some open questions that need to be addressed, are discussed.     

Maximization of learning speed in the motor cortex due to neuronal redundancy

Many redundancies play functional roles in motor control and motor learning. For example, kinematic and muscle redundancies contribute to stabilizing posture and impedance control, respectively. Another redundancy is the number of neurons themselves; there are overwhelmingly more neurons than muscles, and many combinations of neural activation can generate identical muscle activity. The functional roles of this neuronal redundancy remains unknown. Analysis of a redundant neural network model makes it possible to investigate these functional roles while varying the number of model neurons and holding constant the number of output units. Our analysis reveals that learning speed reaches its maximum value if and only if the model includes sufficient neuronal redundancy. This analytical result does not depend on whether the distribution of the preferred direction is uniform or a skewed bimodal, both of which have been reported in neurophysiological studies. Neuronal redundancy maximizes learning speed, even if the neural network model includes recurrent connections, a nonlinear activation function, or nonlinear muscle units. Furthermore, our results do not rely on the shape of the generalization function. The results of this study suggest that one of the functional roles of neuronal redundancy is to maximize learning speed.     

Visual activity regulates neural progenitor cells in developing xenopus CNS through musashi1

Regulation of progenitor cell fate determines the numbers of neurons in the developing brain. While proliferation of neural progenitors predominates during early central nervous system (CNS) development, progenitor cell fate shifts toward differentiation as CNS circuits develop, suggesting that signals from developing circuits may regulate proliferation and differentiation. We tested whether activity regulates neurogenesis in?vivo in the developing visual system of Xenopus tadpoles. Both cell proliferation and the number of musashi1-immunoreactive progenitors in the optic tectum decrease as visual system connections become stronger. Visual deprivation for 2?days increased proliferation of musashi1-immunoreactive radial glial progenitors, while visual experience increased neuronal differentiation. Morpholino-mediated knockdown and overexpression of musashi1 indicate that musashi1 is necessary and sufficient for neural progenitor proliferation in the CNS. These data demonstrate a mechanism by which increased brain activity in developing circuits decreases cell proliferation and increases neuronal differentiation through the downregulation of musashi1 in response to circuit activity.     

Sculpting the nervous system: glial control of neuronal development

Glial cells are not passive spectators during nervous system assembly, rather they are active participants that exert significant control over neuronal development. Well-established roles for glia in shaping the developing nervous system include providing trophic support to neurons, modulating axon pathfinding, and driving nerve fasciculation. Exciting recent studies have revealed additional ways in which glial cells also modulate neurodevelopment. Glial cells regulate the number of neurons at early developmental stages by dynamically influencing neural precursor divisions, and at later stages by promoting neuronal cell death through engulfment. Glia also participate in the fine sculpting of neuronal connections by pruning excess axonal projections, shaping dendritic spines, and secreting multiple factors that promote synapse formation and functional maturation. These recent insights provide further compelling evidence that glial cells, through their diverse cellular actions, are essential contributors to the construction of a functionally mature nervous system.     

Spontaneous coordinated activity in cultured networks: Analysis of multiple ignition sites,primary circuits,and burst phase delay distributions

All higher order central nervous systems exhibit spontaneous neural activity, though the purpose and mechanistic origin of such activity remains poorly understood. We quantitatively analyzed the ignition and spread of collective spontaneous electrophysiological activity in networks of cultured cortical neurons growing on microelectrode arrays. Leader neurons, which form a mono-synaptically connected primary circuit, and initiate a majority of network bursts were found to be a small subset of recorded neurons. Leader/follower firing delay times formed temporally stable positively skewed distributions. Blocking inhibitory synapses usually resulted in shorter delay times with reduced variance. These distributions are characterizations of general aspects of internal network dynamics and provide estimates of pair-wise synaptic distances. The resulting analysis produced specific quantitative constraints and insights into the activation patterns of collective neuronal activity in self-organized cortical networks, which may prove useful for models emulating spontaneously active systems.     

CNS Targets Support and Sustain Differentiation of Cultured Neuronal and Retinal Progenitor Cells

Superior colliculus (SC) is the target of retinal neurons, allowing them to form connections. Cultured stem cells/progenitors can potentially be used as donor tissue to reconstruct degenerated retina including perhaps replacing lost ganglion cells in glaucoma. In which case, it will be essential for these cells to integrate with the central nervous system targets. Here, we have investigated if the mid-brain region containing superior colliculus (SC) provides a permissive environment for the survival and differentiation of neural progenitors, including retinal progenitor cells propagated in cultures. Neural (NPCs) and retinal progenitor cells (RPCs) from green fluorescent protein (GFP) transgenic mice were cultured. Passage two through four neural and retinal progenitor cells were subsequently cocultured with the SC organotypic slices and maintained in culture for 17 and eight days respectively. Differentiation of the neurons was studied by immunocytochemistry for retinotypic neuronal markers. Retinal progenitor cells cocultured with SC slices were able to differentiate into various neuronal morphologies. Some cocultured progenitor cells differentiated into neurons as suggested by class III β tubulin immunoreactivity. In addition, specific retinotypic neuronal differentiation of RPC was detected by immunoreactivity for calbindin and PKC. SC provides a permissive environment that supports survival and differentiation of the progenitor cells.     

Isolation of an early neural maker gene abundantly expressed in the nervous system of the ascidian, Halocynthia roretzi

Ascidian tadpole larvae possess a primitive nervous system, which is a prospective prototype of the chordate nervous system. It is composed of relatively few cells but sufficient for complex larval behavior. Here we report on HrETR-1, a gene zygotically expressed in a large proportion of the developing neural cells of the ascidian, Halocynthia roretzi. HrETR-1 is an early neural marker which can be used for analyzing neural differentiation. HrETR-1 expression intensified in most neural cells of genes isolated to date, in both central and peripheral nervous systems including palps as early as the 110-cell stage. Using this gene as a probe, we characterized neural cells in the nervous system as well as confirming their origins. Also, we recognized three types of peripheral epidermal neurons which presumably correlate to the larval neurons previously reported for another ascidian. Among these, five bilateral neurons located in the anterior region of the trunk appeared to be derived from a8.26 blastomeres.     

An experimental approach to research on the integrative properties of a single brain neuron

The difficulties in investigation of the single neuron integrative properties in mammalian brain are connected with the absence of a mean of determination of neuronal set contribution in their activity. In given paper an experimental approach to the decision of this task is proposed. It consists in neuron activity investigation in the model learning training situation under condition of partial functional isolation from nervous cells by means of decrease in extracellular calcium concentration level using ethylene glycol tetraacetate. As indicator of such isolation was a partial or total reduction of responses in investigated neurons to electric microstimulation of neighbouring (200-240 mkm) parts of neocortex. The results of analysis of neuron responses in sensomotor cortex in the rat's brain in the process of acetylcholine repetitive local application give possibility to propose that some neurons don't exhibit plasticity according to the indicator in the dynamics of impulse discharge rate in responses to transmitter.     

Energy coding in biological neural networks

According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, we present a brand new scientific theory that offers an unique mechanism for brain information processing. We demonstrate that the neural coding produced by the activity of the brain is well described by our theory of energy coding. Due to the energy coding model’s ability to reveal mechanisms of brain information processing based upon known biophysical properties, we can not only reproduce various experimental results of neuro-electrophysiology, but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, we estimate that the theory has very important consequences for quantitative research of cognitive function.     

Development of neural circuitry for precise temporal sequences through spontaneous activity, axon remodeling, and synaptic plasticity

Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas, including songbird premotor nucleus, cat visual cortex, and primary motor cortex. Synfire chains-networks in which groups of neurons are connected via excitatory synapses into a unidirectional chain-are thought to underlie the generation of such sequences. It is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. Here, we show through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and axon remodeling-the pruning of prolific weak connections that follows the emergence of a finite number of strong connections. The formation process begins with a random network. A subset of neurons, called training neurons, intermittently receive superthreshold external input. Gradually, a synfire chain emerges through a recruiting process, in which neurons within the network connect to the tail of the chain started by the training neurons. The model is robust to varying parameters, as well as natural events like neuronal turnover and massive lesions. Our model suggests that long synfire chain can form during the development through self-organization, and axon remodeling, ubiquitous in developing neural circuits, is essential in the process.     

Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data

The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can efficiently solve difficult segmentation tasks. In this model, multiplicative recurrent connections learn a context-dependent gating of dendro-somatic information transfers to minimize error in the prediction of somatic responses by the dendrites. Consequently, these connections filter the redundant input features represented by the dendrites but unnecessary in the given context. The model was tested on both synthetic and real neural data. In particular, the model was successful for segmenting multiple cell assemblies repeating in large-scale calcium imaging data containing thousands of cortical neurons. Our results suggest that recurrent gating of dendro-somatic signal transfers is crucial for cortical learning of context-dependent segmentation tasks.     

Developmental plasticity in neural circuits controlling birdsong: Sexual differentiation and the neural basis of learning

In many species of passerine songbirds, males learn their song during defined periods of life. Female song in often reduced or absent, as are the brain regions controlling song. Sexual differences in the brain arise because of the action of sex steroids, which trigger the formation of some neural pathways (especially the pathway from the higher vocal center to the robust nucleus) and prevent the atrophy of others in males. These neural changes occur during periods of developmental song learning and can recur during periods of learning in adult birds. The process of learning is correlated with major increases or decreases in the number of neurons in specific neuronal populations, suggesting that the formation or loss of specific neural pathways regulates the ability to learn. Species differences in sexual differentiation and learning allow informative cross-species comparisons of neural structure and behavior. © 1992 John Wiley & Sons, Inc.