Cortical reorganization consistent with spike timing–but not correlation-dependent plasticity

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa’s arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.

Forgetting memristor based STDP learning circuit for neural networks

The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. This paper proposes a new STDP learning rule implementation circuit based on the forgetting memristor. This kind of forgetting memory resistance synapse makes the neural network have the function of time-division multiplexing, but the instability of short-term memory will affect the learning ability of the neural network. This paper analyzes and discusses the influence of synapses with long-term and short-term memory on the learning characteristics of neural network STDP, which lays a foundation for the construction of time-division multiplexing neural network with long-term and short-term memory synapses. Through this circuit, it is found that the volatile memristor has different behaviors to the stimulus signal in different initial states, and the resulting LTP phenomenon is more in line with the forgetting effect in biology. This circuit has multiple adjustable parameters, which can fit the STDP learning rules under different conditions. The application of neural network proves the availability of this circuit.

Synaptic Plasticity Forms and Functions

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and memory. Computational and engineering work corroborate the power of learning through the directed adjustment of connection weights. Here we review the fundamental elements of four broadly categorized forms of synaptic plasticity and discuss their functional capabilities and limitations. Although standard, correlation-based, Hebbian synaptic plasticity has been the primary focus of neuroscientists for decades, it is inherently limited. Three-factor plasticity rules supplement Hebbian forms with neuromodulation and eligibility traces, while true supervised types go even further by adding objectives and instructive signals. Finally, a recently discovered hippocampal form of synaptic plasticity combines the above elements, while leaving behind the primary Hebbian requirement. We suggest that the effort to determine the neural basis of adaptive behavior could benefit from renewed experimental and theoretical investigation of more powerful directed types of synaptic plasticity.

Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.

Energy-Efficient Artificial Synapses Based on Oxide Tunnel Junctions

The development of artificial synapses has enabled the establishment of brain-inspired computing systems, which provides a promising approach for overcoming the inherent limitations of current computer systems. The two-terminal memristors that faithfully mimic the function of biological synapses have intensive prospects in the neural network field. Here, we propose a high-performance artificial synapse based on oxide tunnel junctions with oxygen vacancy migration. Both short-term and long-term plasticities are mimicked in one device. The oxygen vacancy migration through oxide ultrathin films is utilized to manipulate long-term plasticity. Essential synaptic functions, such as paired pulse facilitation, post-tetanic potentiation, as well as spike-timing-dependent plasticity, are successfully implemented in one device by finely modifying the shape of the pre- and postsynaptic spikes. Ultralow femtojoule energy consumption comparable to that of the human brain indicates its potential application in efficient neuromorphic computing. Oxide tunnel junctions proposed in this work provide an alternative approach for realizing energy-efficient brain-like chips.

Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal-Oxide-Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.

Macroscopic Cluster Organizations Change the Complexity of Neural Activity

In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of spiking neurons with different topological properties of a macroscopic network based on the Watts and Strogatz model. The complexity of spontaneous activity is analyzed using multiscale entropy, and the structural properties of the network are analyzed using complex network theory. Experimental results show that a macroscopic structure with high clustering and high degree centrality increases the firing rates of neurons in a neuron group and enhances intraconnections from the excitatory neurons to inhibitory neurons in a neuron group. As a result, the intensity of the specific frequency components of neural activity increases. This decreases the complexity of neural activity. Finally, we discuss the research relevance of the complexity of the brain activity.

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters

Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.

TMS-induced neuronal plasticity enables targeted remodeling of visual cortical maps

Transcranial magnetic stimulation (TMS) holds promise as a tool for noninvasively facilitating plastic changes in cortical networks. However, highly resolved visualization of its modulatory effects remains elusive because current neuroimaging techniques applicable in humans are limited in spatiotemporal resolution. Here we used an imaging approach with voltage-sensitive dye and tracked, at submillimeter range, TMS-induced plastic changes across cat primary visual cortex. We show that high-frequency 10-Hz TMS induces a state where visual cortical maps are transiently “destabilized.” In turn, the cortex was sensitized to a bias in input—here imposed by prolonged exposure to a single visual orientation—and primed to relearn connectivity patterns. These findings implicate an early post-TMS time window for promising therapeutic interventions through TMS.

Transcranial magnetic stimulation (TMS) has become a popular clinical method to modify cortical processing. The events underlying TMS-induced functional changes remain, however, largely unknown because current noninvasive recording methods lack spatiotemporal resolution or are incompatible with the strong TMS-associated electrical field. In particular, an answer to the question of how the relatively unspecific nature of TMS stimulation leads to specific neuronal reorganization, as well as a detailed picture of TMS-triggered reorganization of functional brain modules, is missing. Here we used real-time optical imaging in an animal experimental setting to track, at submillimeter range, TMS-induced functional changes in visual feature maps over several square millimeters of the brain’s surface. We show that high-frequency TMS creates a transient cortical state with increased excitability and increased response variability, which opens a time window for enhanced plasticity. Visual stimulation (i.e., 30 min of passive exposure) with a single orientation applied during this TMS-induced permissive period led to enlarged imprinting of the chosen orientation on the visual map across visual cortex. This reorganization was stable for hours and was characterized by a systematic shift in orientation preference toward the trained orientation. Thus, TMS can noninvasively trigger a targeted large-scale remodeling of fundamentally mature functional architecture in early sensory cortex.

Modulating STDP Balance Impacts the Dendritic Mosaic

The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties. We have previously shown that spike timing-dependent plasticity (STDP) can lead to synaptic efficacies being arranged into spatially segregated clusters. This effectively partitions the dendritic tree into a tessellated imprint which we have called a dendritic mosaic. Here, using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and STDP learning, we investigated the impact of altered STDP balance on forming such a spatial organization. We show that cluster formation and extend depend on several factors, including the balance between potentiation and depression, the afferents' mean firing rate and crucially on the dendritic morphology. We find that STDP balance has an important role to play for this emergent mode of spatial organization since any imbalances lead to severe degradation- and in some case even destruction- of the mosaic. Our model suggests that, over a broad range of of STDP parameters, synaptic plasticity shapes the spatial arrangement of synapses, favoring the formation of clustered efficacy engrams.

Anisomorphic cortical reorganization in asymmetric sensorineural hearing loss

Acoustic trauma or inner ear disease may predominantly injure one ear, causing asymmetric sensorineural hearing loss (SNHL). While characteristic frequency (CF) map plasticity of primary auditory cortex (AI) contralateral to the injured ear has been detailed, there is no study that also evaluates ipsilateral AI to compare cortical reorganization across both hemispheres. We assess whether the normal isomorphic mirror-image relationship between the two hemispheres is maintained or disrupted in mild-to-moderate asymmetric SNHL of adult squirrel monkeys. At week 24 after induction of acoustic injury to the right ear, functional organization of the two hemispheres differs in direction and magnitude of interaural CF difference, percentage of recording sites with spectrally nonoverlapping binaural activation, and the concurrence of peripheral and central activation thresholds. The emergence of this anisomorphic cortical reorganization of the two hemispheres is replicated by simulation based on spike timing-dependent plasticity, where 1) AI input from the contralateral ear is dominant, 2) reestablishment of relatively shorter contralateral ear input timing drives reorganization, and 3) only AI contralateral to the injured ear undergoes major realignment of interaural frequency maps that evolve over months. Asymmetric SNHL disrupts isomorphic organization between the two hemispheres and results in relative local hemispheric autonomy, potentially impairing performance of tasks that require binaural input alignment or interhemispheric processing.NEW & NOTEWORTHY Mild-to-moderate hearing loss in one ear and essentially normal hearing in the other triggers cortical reorganization that is different in the two hemispheres. Asymmetry of cochlea sensitivities does not simply propagate to the two auditory cortices in mirror-image fashion. The resulting anisomorphic cortical reorganization may be a neurophysiological basis of clinical deficits in asymmetric hearing loss, such as difficulty with hearing in noise, impaired spatial hearing, and accelerated decline of the poorer ear.

Spike Timing-Dependent Plasticity in the Mouse Barrel Cortex Is Strongly Modulated by Sensory Learning and Depends on Activity of Matrix Metalloproteinase 9

Experience and learning in adult primary somatosensory cortex are known to affect neuronal circuits by modifying both excitatory and inhibitory transmission. Synaptic plasticity phenomena provide a key substrate for cognitive processes, but precise description of the cellular and molecular correlates of learning is hampered by multiplicity of these mechanisms in various projections and in different types of neurons. Herein, we investigated the impact of associative learning on neuronal plasticity in distinct types of postsynaptic neurons by checking the impact of classical conditioning (pairing whisker stroking with tail shock) on the spike timing-dependent plasticity (t-LTP and t-LTD) in the layer IV to II/III vertical pathway of the mouse barrel cortex. Learning in this paradigm practically prevented t-LTP measured in pyramidal neurons but had no effect on t-LTD. Since classical conditioning is known to affect inhibition in the barrel cortex, we examined its effect on tonic GABAergic currents and found a strong downregulation of these currents in the layer II/III interneurons but not in pyramidal cells. Matrix metalloproteinases emerged as crucial players in synaptic plasticity and learning. We report that the blockade of MMP-9 (but not MMP-3) abolished t-LTP having no effect on t-LTD. Moreover, associative learning resulted in an upregulation of gelatinolytic activity within the "trained" barrel. We conclude that LTP induced by spike timing-dependent plasticity (STDP) paradigm is strongly correlated with associative learning and critically depends on the activity of MMP-9.

Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware

Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it.

Models of Metaplasticity: A Review of Concepts

Part of hippocampal and cortical plasticity is characterized by synaptic modifications that depend on the joint activity of the pre- and post-synaptic neurons. To which extent those changes are determined by the exact timing and the average firing rates is still a matter of debate; this may vary from brain area to brain area, as well as across neuron types. However, it has been robustly observed both in vitro and in vivo that plasticity itself slowly adapts as a function of the dynamical context, a phenomena commonly referred to as metaplasticity. An alternative concept considers the regulation of groups of synapses with an objective at the neuronal level, for example, maintaining a given average firing rate. In that case, the change in the strength of a particular synapse of the group (e.g., due to Hebbian learning) affects others' strengths, which has been coined as heterosynaptic plasticity. Classically, Hebbian synaptic plasticity is paired in neuron network models with such mechanisms in order to stabilize the activity and/or the weight structure. Here, we present an oriented review that brings together various concepts from heterosynaptic plasticity to metaplasticity, and show how they interact with Hebbian-type learning. We focus on approaches that are nowadays used to incorporate those mechanisms to state-of-the-art models of spiking plasticity inspired by experimental observations in the hippocampus and cortex. Making the point that metaplasticity is an ubiquitous mechanism acting on top of classical Hebbian learning and promoting the stability of neural function over multiple timescales, we stress the need for incorporating it as a key element in the framework of plasticity models. Bridging theoretical and experimental results suggests a more functional role for metaplasticity mechanisms than simply stabilizing neural activity.

Adult plasticity and cortical reorganization after peripheral lesions

Following loss of input due to peripheral lesions, functional reorganization occurs in the deprived cortical region in adults. Over a period of hours to months, cells in the lesion projection zone (LPZ) begin to respond to novel stimuli. This reorganization is mediated by two processes: a reduction of inhibition in a gradient throughout the cortex and input remapping via sprouting of axonal arbors from cortical regions spatially adjacent to the LPZ, and strengthening of pre-existing subthreshold inputs. Together these inputs facilitate receptive field remapping of cells in the LPZ. Recent experiments have revealed time courses and potential interactions of the mechanisms associated with functional reorganization, suggesting that large scale reorganization in the adult may utilize plasticity mechanisms prominent during development.

Regulation of Local Ambient GABA Levels via Transporter-Mediated GABA Import and Export for Subliminal Learning

Perception of supraliminal stimuli might in general be reflected in bursts of action potentials (spikes), and their memory traces could be formed through spike-timing-dependent plasticity (STDP). Memory traces for subliminal stimuli might be formed in a different manner, because subliminal stimulation evokes a fraction (but not a burst) of spikes. Simulations of a cortical neural network model showed that a subliminal stimulus that was too brief (10 msec) to perceive transiently (more than about 500 msec) depolarized stimulus-relevant principal cells and hyperpolarized stimulus-irrelevant principal cells in a subthreshold manner. This led to a small increase or decrease in ongoing-spontaneous spiking activity frequency (less than 1 Hz). Synaptic modification based on STDP during this period effectively enhanced relevant synaptic weights, by which subliminal learning was improved. GABA transporters on GABAergic interneurons modulated local levels of ambient GABA. Ambient GABA molecules acted on extrasynaptic receptors, provided principal cells with tonic inhibitory currents, and contributed to achieving the subthreshold neuronal state. We suggest that ongoing-spontaneous synaptic alteration through STDP following subliminal stimulation may be a possible neuronal mechanism for leaving its memory trace in cortical circuitry. Regulation of local ambient GABA levels by transporter-mediated GABA import and export may be crucial for subliminal learning.

Plasticity in memristive devices for spiking neural networks

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Synaptic plasticity enables adaptive self-tuning critical networks

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity

Spike-timing-dependent plasticity (STDP) has been well established between excitatory neurons and several computational functions have been proposed in various neural systems. Despite some recent efforts, however, there is a significant lack of functional understanding of inhibitory STDP (iSTDP) and its interplay with excitatory STDP (eSTDP). Here, we demonstrate by analytical and numerical methods that iSTDP contributes crucially to the balance of excitatory and inhibitory weights for the selection of a specific signaling pathway among other pathways in a feedforward circuit. This pathway selection is based on the high sensitivity of STDP to correlations in spike times, which complements a recent proposal for the role of iSTDP in firing-rate based selection. Our model predicts that asymmetric anti-Hebbian iSTDP exceeds asymmetric Hebbian iSTDP for supporting pathway-specific balance, which we show is useful for propagating transient neuronal responses. Furthermore, we demonstrate how STDPs at excitatory-excitatory, excitatory-inhibitory, and inhibitory-excitatory synapses cooperate to improve the pathway selection. We propose that iSTDP is crucial for shaping the network structure that achieves efficient processing of synchronous spikes.

Stochastic variational learning in recurrent spiking networks

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about “novelty” on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Noise-rearing disrupts the maturation of multisensory integration

It is commonly believed that the ability to integrate information from different senses develops according to associative learning principles as neurons acquire experience with co-active cross-modal inputs. However, previous studies have not distinguished between requirements for co-activation versus co-variation. To determine whether cross-modal co-activation is sufficient for this purpose in visual-auditory superior colliculus (SC) neurons, animals were reared in constant omnidirectional noise. By masking most spatiotemporally discrete auditory experiences, the noise created a sensory landscape that decoupled stimulus co-activation and co-variance. Although a near-normal complement of visual-auditory SC neurons developed, the vast majority could not engage in multisensory integration, revealing that visual-auditory co-activation was insufficient for this purpose. That experience with co-varying stimuli is required for multisensory maturation is consistent with the role of the SC in detecting and locating biologically significant events, but it also seems likely that this is a general requirement for multisensory maturation throughout the brain.

A simple rule for dendritic spine and axonal bouton formation can account for cortical reorganization after focal retinal lesions

Lasting alterations in sensory input trigger massive structural and functional adaptations in cortical networks. The principles governing these experience-dependent changes are, however, poorly understood. Here, we examine whether a simple rule based on the neurons' need for homeostasis in electrical activity may serve as driving force for cortical reorganization. According to this rule, a neuron creates new spines and boutons when its level of electrical activity is below a homeostatic set-point and decreases the number of spines and boutons when its activity exceeds this set-point. In addition, neurons need a minimum level of activity to form spines and boutons. Spine and bouton formation depends solely on the neuron's own activity level, and synapses are formed by merging spines and boutons independently of activity. Using a novel computational model, we show that this simple growth rule produces neuron and network changes as observed in the visual cortex after focal retinal lesions. In the model, as in the cortex, the turnover of dendritic spines was increased strongest in the center of the lesion projection zone, while axonal boutons displayed a marked overshoot followed by pruning. Moreover, the decrease in external input was compensated for by the formation of new horizontal connections, which caused a retinotopic remapping. Homeostatic regulation may provide a unifying framework for understanding cortical reorganization, including network repair in degenerative diseases or following focal stroke.

The adult brain is less hard-wired than traditionally thought. About ten percent of synapses in the mature visual cortex is continually replaced by new ones (structural plasticity). This percentage greatly increases after lasting changes in visual input. Due to the topographically organized nerve connections from the retina in the eye to the primary visual cortex in the brain, a small circumscribed lesion in the retina leads to a defined area in the cortex that is deprived of input. Recent experimental studies have revealed that axonal sprouting and dendritic spine turnover are massively increased in and around the cortical area that is deprived of input. However, the driving forces for this structural plasticity remain unclear. Using a novel computational model, we examine whether the need for activity homeostasis of individual neurons may drive cortical reorganization after lasting changes in input activity. We show that homeostatic growth rules indeed give rise to structural and functional reorganization of neuronal networks similar to the cortical reorganization observed experimentally. Understanding the principles of structural plasticity may eventually lead to novel treatment strategies for stimulating functional reorganization after brain damage and neurodegeneration.

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales.

Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity

This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1). This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN) to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through ±180° along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex.

STDP and STDP variations with memristors for spiking neuromorphic learning systems

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original "moving wall" or to the "filament creation and annihilation" models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.

Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo

Action Potential (APs) patterns of sensory cortex neurons encode a variety of stimulus features, but how can a neuron change the feature to which it responds? Here, we show that in vivo a spike-timing-dependent plasticity (STDP) protocol—consisting of pairing a postsynaptic AP with visually driven presynaptic inputs—modifies a neurons' AP-response in a bidirectional way that depends on the relative AP-timing during pairing. Whereas postsynaptic APs repeatedly following presynaptic activation can convert subthreshold into suprathreshold responses, APs repeatedly preceding presynaptic activation reduce AP responses to visual stimulation. These changes were paralleled by restructuring of the neurons response to surround stimulus locations and membrane-potential time-course. Computational simulations could reproduce the observed subthreshold voltage changes only when presynaptic temporal jitter was included. Together this shows that STDP rules can modify output patterns of sensory neurons and the timing of single-APs plays a crucial role in sensory coding and plasticity.

DOI:http://dx.doi.org/10.7554/eLife.00012.001

Nerve cells, called neurons, are one of the core components of the brain and form complex networks by connecting to other neurons via long, thin ‘wire-like’ processes called axons. Axons can extend across the brain, enabling neurons to form connections—or synapses—with thousands of others. It is through these complex networks that incoming information from sensory organs, such as the eye, is propagated through the brain and encoded.

The basic unit of communication between neurons is the action potential, often called a ‘spike’, which propagates along the network of axons and, through a chemical process at synapses, communicates with the postsynaptic neurons that the axon is connected to. These action potentials excite the neuron that they arrive at, and this excitatory process can generate a new action potential that then propagates along the axon to excite additional target neurons. In the visual areas of the cortex, neurons respond with action potentials when they ‘recognize’ a particular feature in a scene—a process called tuning. How a neuron becomes tuned to certain features in the world and not to others is unclear, as are the rules that enable a neuron to change what it is tuned to. What is clear, however, is that to understand this process is to understand the basis of sensory perception.

Memory storage and formation is thought to occur at synapses. The efficiency of signal transmission between neurons can increase or decrease over time, and this process is often referred to as synaptic plasticity. But for these synaptic changes to be transmitted to target neurons, the changes must alter the number of action potentials. Although it has been shown in vitro that the efficiency of synaptic transmission—that is the strength of the synapse—can be altered by changing the order in which the pre- and postsynaptic cells are activated (referred to as ‘Spike-timing-dependent plasticity’), this has never been shown to have an effect on the number of action potentials generated in a single neuron in vivo. It is therefore unknown whether this process is functionally relevant.

Now Pawlak et al. report that spike-timing-dependent plasticity in the visual cortex of anaesthetized rats can change the spiking of neurons in the visual cortex. They used a visual stimulus (a bar flashed up for half a second) to activate a presynaptic cell, and triggered a single action potential in the postsynaptic cell a very short time later. By repeatedly activating the cells in this way, they increased the strength of the synaptic connection between the two neurons. After a small number of these pairing activations, presenting the visual stimulus alone to the presynaptic cell was enough to trigger an action potential (a suprathreshold response) in the postsynaptic neuron—even though this was not the case prior to the pairing.

This study shows that timing rules known to change the strength of synaptic connections—and proposed to underlie learning and memory—have functional relevance in vivo, and that the timing of single action potentials can change the functional status of a cortical neuron.

DOI:http://dx.doi.org/10.7554/eLife.00012.002

A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards

In this letter, a novel critic-like algorithm was developed to extend the synaptic plasticity rule described in Florian ( 2007 ) and Izhikevich ( 2007 ) in order to solve the problem of learning multiple distal rewards simultaneously. The system is augmented with short-term plasticity (STP) to stabilize the learning dynamics, thereby increasing the system's learning capacity. A theoretical threshold is estimated for the number of distal rewards that this system can learn. The validity of the novel algorithm was verified by computer simulations.

The spike-timing dependence of plasticity

In spike-timing-dependent plasticity (STDP), the order and precise temporal interval between presynaptic and postsynaptic spikes determine the sign and magnitude of long-term potentiation (LTP) or depression (LTD). STDP is widely utilized in models of circuit-level plasticity, development, and learning. However, spike timing is just one of several factors (including firing rate, synaptic cooperativity, and depolarization) that govern plasticity induction, and its relative importance varies across synapses and activity regimes. This review summarizes this broader view of plasticity, including the forms and cellular mechanisms for the spike-timing dependence of plasticity, and, the evidence that spike timing is an important determinant of plasticity in vivo.

Spike-timing-dependent potentiation of sensory surround in the somatosensory cortex is facilitated by deprivation-mediated disinhibition

Functional maps in the cerebral cortex reorganize in response to changes in experience, but the synaptic underpinnings remain uncertain. Here, we demonstrate that layer (L) 2/3 pyramidal cell synapses in mouse barrel cortex can be potentiated upon pairing of whisker-evoked postsynaptic potentials (PSPs) with action potentials (APs). This spike-timing-dependent long-term potentiation (STD-LTP) was only effective for PSPs evoked by deflections of a whisker in the neuron's receptive field center, and not its surround. Trimming of all except two whiskers rapidly opened the possibility to drive STD-LTP by the spared surround whisker. This facilitated STD-LTP was associated with a strong decrease in the surrounding whisker-evoked inhibitory conductance and partially occluded picrotoxin-mediated LTP facilitation. Taken together, our data demonstrate that sensory deprivation-mediated disinhibition facilitates STD-LTP from the sensory surround, which may promote correlation- and experience-dependent expansion of receptive fields.

Quantification of early stages of cortical reorganization of the topographic map of V1 following retinal lesions in monkeys

We quantified the capacity for reorganization of the topographic representation of area V1 in adult monkeys. Bias-free automated mapping methods were used to delineate receptive fields (RFs) of an array of neuronal clusters prior to, and up to 6 h following retinal lesions. Monocular lesions caused a significant reorganization of the topographic map in this area, both inside and outside the cortical lesion projection zone (LPZ). Small flashed stimuli revealed responses up to 0.85 mm inside the boundaries of the LPZ, with RFs representing regions of undamaged retina immediately surrounding the lesion. In contrast, long moving bars that spanned the scotoma resulting from the lesion revealed responsive units up to 1.87 mm inside the LPZ, with RFs representing interpolated responses in this region. This reorganization is present immediately after monocular retinal lesioning. Both stimuli showed a similar and significant (5-fold) increase of the RF scatter in the LPZ, 0.56 mm (median), compared with the undamaged retina, 0.12 mm. Our results reveal an array of preexisting subthreshold functional connections of up to 2 mm in V1, which can be rapidly mobilized independently from the differential qualitative reorganization elicited by each stimulus.

Spike-timing-dependent plasticity with weight dependence evoked from physical constraints

Analogue and mixed-signal VLSI implementations of Spike-Timing-Dependent Plasticity (STDP) are reviewed. A circuit is presented with a compact implementation of STDP suitable for parallel integration in large synaptic arrays. In contrast to previously published circuits, it uses the limitations of the silicon substrate to achieve various forms and degrees of weight dependence of STDP. It also uses reverse-biased transistors to reduce leakage from a capacitance representing weight. Chip results are presented showing: various ways in which the learning rule may be shaped; how synaptic weights may retain some indication of their learned values over periods of minutes; and how distributions of weights for synapses convergent on single neurons may shift between more or less extreme bimodality according to the strength of correlational cues in their inputs.

Computational models of neurophysiological correlates of tinnitus

The understanding of tinnitus has progressed considerably in the past decade, but the details of the mechanisms that give rise to this phantom perception of sound without a corresponding acoustic stimulus have not yet been pinpointed. It is now clear that tinnitus is generated in the brain, not in the ear, and that it is correlated with pathologically altered spontaneous activity of neurons in the central auditory system. Both increased spontaneous firing rates and increased neuronal synchrony have been identified as putative neuronal correlates of phantom sounds in animal models, and both phenomena can be triggered by damage to the cochlea. Various mechanisms could underlie the generation of such aberrant activity. At the cellular level, decreased synaptic inhibition and increased neuronal excitability, which may be related to homeostatic plasticity, could lead to an over-amplification of natural spontaneous activity. At the network level, lateral inhibition could amplify differences in spontaneous activity, and structural changes such as reorganization of tonotopic maps could lead to self-sustained activity in recurrently connected neurons. However, it is difficult to disentangle the contributions of different mechanisms in experiments, especially since not all changes observed in animal models of tinnitus are necessarily related to tinnitus. Computational modeling presents an opportunity of evaluating these mechanisms and their relation to tinnitus. Here we review the computational models for the generation of neurophysiological correlates of tinnitus that have been proposed so far, and evaluate predictions and compare them to available data. We also assess the limits of their explanatory power, thus demonstrating where an understanding is still lacking and where further research may be needed. Identifying appropriate models is important for finding therapies, and we therefore, also summarize the implications of the models for approaches to treat tinnitus.

Selectionist and evolutionary approaches to brain function: a critical appraisal

We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse, and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price’s covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise.

Subthreshold Membrane Depolarization as Memory Trace for Perceptual Learning

Experience-dependent synaptic plasticity characterizes the adaptable brain and is believed to be the cellular substrate for perceptual learning. A chemical agent such as gamma-aminobutyric acid (GABA) is known to affect synaptic alteration, perhaps gating perceptual learning. We examined whether and how ambient (extrasynaptic) GABA affects experience-dependent synaptic alteration. A cortical neural network model was simulated. Transporters on GABAergic interneurons regulate ambient GABA levels around their axonal target neurons by removing GABA from (forward transport) or releasing it into (reverse transport) the extracellular space. The ambient GABA provides neurons with tonic inhibitory currents by activating extrasynaptic GABAa receptors. During repeated exposures to the same stimulus, we modified the synaptic connection strength between principal cells in a spike-timing-dependent manner. This modulated the activity of GABAergic interneurons, and reduced or augmented ambient GABA concentration. Reduction in ambient GABA concentration led to slight depolarization (less than several millivolts) in ongoing-spontaneous membrane potential. This was a subthreshold neuronal behavior because ongoing-spontaneous spiking activity remained almost unchanged. The ongoing-spontaneous subthreshold depolarization improved a suprathreshold neuronal response. If the stimulus was long absent for perceptual learning, augmentation of ambient GABA concentration took place and the ongoing-spontaneous subthreshold depolarization was depressed. We suggest that a perceptual memory trace could be left in neuronal circuitry as an ongoing-spontaneous subthreshold membrane depolarization, which would allow that memory to be accessed easily afterward, whereas a trace of a memory that has not recently been retrieved fades away when the ongoing-spontaneous subthreshold membrane depolarization built by previous perceptual learning is depressed. This would lead that memory to be accessed with some difficulty. In the brain, ambient GABA, whose level could be regulated by transporter may have an important role in leaving memory trace for perceptual learning.

On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex

In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices. All files used for the simulations are made available through the journal web site.

Vision restoration after brain and retina damage: the "residual vision activation theory"

Vision loss after retinal or cerebral visual injury (CVI) was long considered to be irreversible. However, there is considerable potential for vision restoration and recovery even in adulthood. Here, we propose the "residual vision activation theory" of how visual functions can be reactivated and restored. CVI is usually not complete, but some structures are typically spared by the damage. They include (i) areas of partial damage at the visual field border, (ii) "islands" of surviving tissue inside the blind field, (iii) extrastriate pathways unaffected by the damage, and (iv) downstream, higher-level neuronal networks. However, residual structures have a triple handicap to be fully functional: (i) fewer neurons, (ii) lack of sufficient attentional resources because of the dominant intact hemisphere caused by excitation/inhibition dysbalance, and (iii) disturbance in their temporal processing. Because of this resulting activation loss, residual structures are unable to contribute much to everyday vision, and their "non-use" further impairs synaptic strength. However, residual structures can be reactivated by engaging them in repetitive stimulation by different means: (i) visual experience, (ii) visual training, or (iii) noninvasive electrical brain current stimulation. These methods lead to strengthening of synaptic transmission and synchronization of partially damaged structures (within-systems plasticity) and downstream neuronal networks (network plasticity). Just as in normal perceptual learning, synaptic plasticity can improve vision and lead to vision restoration. This can be induced at any time after the lesion, at all ages and in all types of visual field impairments after retinal or brain damage (stroke, neurotrauma, glaucoma, amblyopia, age-related macular degeneration). If and to what extent vision restoration can be achieved is a function of the amount of residual tissue and its activation state. However, sustained improvements require repetitive stimulation which, depending on the method, may take days (noninvasive brain stimulation) or months (behavioral training). By becoming again engaged in everyday vision, (re)activation of areas of residual vision outlasts the stimulation period, thus contributing to lasting vision restoration and improvements in quality of life.

A Re-Examination of Hebbian-Covariance Rules and Spike Timing-Dependent Plasticity in Cat Visual Cortex in vivo

Spike timing-dependent plasticity (STDP) is considered as an ubiquitous rule for associative plasticity in cortical networks in vitro. However, limited supporting evidence for its functional role has been provided in vivo. In particular, there are very few studies demonstrating the co-occurrence of synaptic efficiency changes and alteration of sensory responses in adult cortex during Hebbian or STDP protocols. We addressed this issue by reviewing and comparing the functional effects of two types of cellular conditioning in cat visual cortex. The first one, referred to as the “covariance” protocol, obeys a generalized Hebbian framework, by imposing, for different stimuli, supervised positive and negative changes in covariance between postsynaptic and presynaptic activity rates. The second protocol, based on intracellular recordings, replicated in vivo variants of the theta-burst paradigm (TBS), proven successful in inducing long-term potentiation in vitro. Since it was shown to impose a precise correlation delay between the electrically activated thalamic input and the TBS-induced postsynaptic spike, this protocol can be seen as a probe of causal (“pre-before-post”) STDP. By choosing a thalamic region where the visual field representation was in retinotopic overlap with the intracellularly recorded cortical receptive field as the afferent site for supervised electrical stimulation, this protocol allowed to look for possible correlates between STDP and functional reorganization of the conditioned cortical receptive field. The rate-based “covariance protocol” induced significant and large amplitude changes in receptive field properties, in both kitten and adult V1 cortex. The TBS STDP-like protocol produced in the adult significant changes in the synaptic gain of the electrically activated thalamic pathway, but the statistical significance of the functional correlates was detectable mostly at the population level. Comparison of our observations with the literature leads us to re-examine the experimental status of spike timing-dependent potentiation in adult cortex. We propose the existence of a correlation-based threshold in vivo, limiting the expression of STDP-induced changes outside the critical period, and which accounts for the stability of synaptic weights during sensory cortical processing in the absence of attention or reward-gated supervision.

Dendritic synapse location and neocortical spike-timing-dependent plasticity

While it has been appreciated for decades that synapse location in the dendritic tree has a powerful influence on signal processing in neurons, the role of dendritic synapse location on the induction of long-term synaptic plasticity has only recently been explored. Here, we review recent work revealing how learning rules for spike-timing-dependent plasticity (STDP) in cortical neurons vary with the spatial location of synaptic input. A common principle appears to be that proximal synapses show conventional STDP, whereas distal inputs undergo plasticity according to novel learning rules. One crucial factor determining location-dependent STDP is the backpropagating action potential, which tends to decrease in amplitude and increase in width as it propagates into the dendritic tree of cortical neurons. We discuss additional location-dependent mechanisms as well as the functional implications of heterogeneous learning rules at different dendritic locations for the organization of synaptic inputs.

The applicability of spike time dependent plasticity to development

Spike time dependent plasticity (STDP) has been observed in both developing and adult animals. Theoretical studies suggest that it implicitly leads to both competition and homeostasis in addition to correlation-based plasticity, making it a good candidate to explain developmental refinement and plasticity in a number of systems. However, it has only been observed to play a clear role in development in a small number of cases. Because the fast time scales necessary to elicit STDP, it would likely be inefficient in governing synaptic modifications in the absence of fast correlations in neural activity. In contrast, later stages of development often depend on sensory inputs that can drive activity on much faster time scales, suggesting a role in STDP in many sensory systems after opening of the eyes and ear canals. Correlations on fast time scales can be also be present earlier in developing microcircuits, such as those produced by specific transient “teacher” circuits in the cerebral cortex. By reviewing examples of each case, we suggest that STDP is not a universal rule, but rather might be masked or phased in, depending on the information available to instruct refinement in different developing circuits. Thus, this review describes selected cases where STDP has been studied in developmental contexts, and uses these examples to suggest a more general framework for understanding where it could be playing a role in development.

Initiating the development of multisensory integration by manipulating sensory experience

The multisensory integration capabilities of superior colliculus neurons emerge gradually during early postnatal life as a consequence of experience with cross-modal stimuli. Without such experience neurons become responsive to multiple sensory modalities but are unable to integrate their inputs. The present study demonstrates that neurons retain sensitivity to cross-modal experience well past the normal developmental period for acquiring multisensory integration capabilities. Experience surprisingly late in life was found to rapidly initiate the development of multisensory integration, even more rapidly than expected based on its normal developmental time course. Furthermore, the requisite experience was acquired by the anesthetized brain and in the absence of any of the stimulus-response contingencies generally associated with learning. The key experiential factor was repeated exposure to the relevant stimuli, and this required that the multiple receptive fields of a multisensory neuron encompassed the cross-modal exposure site. Simple exposure to the individual components of a cross-modal stimulus was ineffective in this regard. Furthermore, once a neuron acquired multisensory integration capabilities at the exposure site, it generalized this experience to other locations, albeit with lowered effectiveness. These observations suggest that the prolonged period during which multisensory integration normally appears is due to developmental factors in neural circuitry in addition to those required for incorporating the statistics of cross-modal events; that neurons learn a multisensory principle based on the specifics of experience and can then apply it to other stimulus conditions; and that the incorporation of this multisensory information does not depend on an alert brain.

Adult plasticity in multisensory neurons: short-term experience-dependent changes in the superior colliculus

Multisensory neurons in the superior colliculus (SC) have the capability to integrate signals that belong to the same event, despite being conveyed by different senses. They develop this capability during early life as experience is gained with the statistics of cross-modal events. These adaptations prepare the SC to deal with the cross-modal events that are likely to be encountered throughout life. Here, we found that neurons in the adult SC can also adapt to experience with sequentially ordered cross-modal (visual-auditory or auditory-visual) cues, and that they do so over short periods of time (minutes), as if adapting to a particular stimulus configuration. This short-term plasticity was evident as a rapid increase in the magnitude and duration of responses to the first stimulus, and a shortening of the latency and increase in magnitude of the responses to the second stimulus when they are presented in sequence. The result was that the two responses appeared to merge. These changes were stable in the absence of experience with competing stimulus configurations, outlasted the exposure period, and could not be induced by equivalent experience with sequential within-modal (visual-visual or auditory-auditory) stimuli. A parsimonious interpretation is that the additional SC activity provided by the second stimulus became associated with, and increased the potency of, the afferents responding to the preceding stimulus. This interpretation is consistent with the principle of spike-timing-dependent plasticity, which may provide the basic mechanism for short term or long term plasticity and be operative in both the adult and neonatal SC.

Synaptic mechanisms for plasticity in neocortex

Sensory experience and learning alter sensory representations in cerebral cortex. The synaptic mechanisms underlying sensory cortical plasticity have long been sought. Recent work indicates that long-term cortical plasticity is a complex, multicomponent process involving multiple synaptic and cellular mechanisms. Sensory use, disuse, and training drive long-term potentiation and depression (LTP and LTD), homeostatic synaptic plasticity and plasticity of intrinsic excitability, and structural changes including formation, removal, and morphological remodeling of cortical synapses and dendritic spines. Both excitatory and inhibitory circuits are strongly regulated by experience. This review summarizes these findings and proposes that these mechanisms map onto specific functional components of plasticity, which occur in common across the primary somatosensory, visual, and auditory cortices.

A model for cortical rewiring following deafferentation and focal stroke

It is still unclear to what extent structural plasticity in terms of synaptic rewiring is the cause for cortical remapping after a lesion. Recent two-photon laser imaging studies demonstrate that synaptic rewiring is persistent in the adult brain and is dramatically increased following brain lesions or after a loss of sensory input (cortical deafferentation). We use a recurrent neural network model to study the time course of synaptic rewiring following a peripheral lesion. For this, we represent axonal and dendritic elements of cortical neurons to model synapse formation, pruning and synaptic rewiring. Neurons increase and decrease the number of axonal and dendritic elements in an activity-dependent fashion in order to maintain their activity in a homeostatic equilibrium. In this study we demonstrate that synaptic rewiring contributes to neuronal homeostasis during normal development as well as following lesions. We show that networks in homeostasis, which can therefore be considered as adult networks, are much less able to compensate for a loss of input. Interestingly, we found that paused stimulation of the networks are much more effective promoting reorganization than continuous stimulation. This can be explained as neurons quickly adapt to this stimulation whereas pauses prevents a saturation of the positive stimulation effect. These findings may suggest strategies for improving therapies in neurologic rehabilitation.

Theoretical models of neural circuit development

Proper wiring up of the nervous system is critical to the development of organisms capable of complex and adaptable behaviors. Besides the many experimental advances in determining the cellular and molecular machinery that carries out this remarkable task precisely and robustly, theoretical approaches have also proven to be useful tools in analyzing this machinery. A quantitative understanding of these processes can allow us to make predictions, test hypotheses, and appraise established concepts in a new light. Three areas that have been fruitful in this regard are axon guidance, retinotectal mapping, and activity-dependent development. This chapter reviews some of the contributions made by mathematical modeling in these areas, illustrated by important examples of models in each section. For axon guidance, we discuss models of how growth cones respond to their environment, and how this environment can place constraints on growth cone behavior. Retinotectal mapping looks at computational models for how topography can be generated in populations of neurons based on molecular gradients and other mechanisms such as competition. In activity-dependent development, we discuss theoretical approaches largely based on Hebbian synaptic plasticity rules, and how they can generate maps in the visual cortex very similar to those seen in vivo. We show how theoretical approaches have substantially contributed to the advancement of developmental neuroscience, and discuss future directions for mathematical modeling in the field.

Strengthening of lateral activation in adult rat visual cortex after retinal lesions captured with voltage-sensitive dye imaging in vivo

Sensory deprivation caused by peripheral injury can trigger functional cortical reorganization across the initially silenced cortical area. It is proposed that intracortical connectivity enables recovery of function within such a lesion projection zone (LPZ), thus substituting lost subcortical input. Here, we investigated retinal lesion-induced changes in the function of lateral connections in the primary visual cortex of the adult rat. Using voltage-sensitive dye recordings, we visualized in millisecond-time resolution spreading synaptic activity across the LPZ. Shortly after lesion, the majority of neurons within the LPZ were subthresholdly activated by delayed propagation of activity that originated from unaffected cortical regions. With longer recovery time, latencies within the LPZ gradually decreased, and activation reached suprathreshold levels. Targeted electrode recordings confirmed that receptive fields of intra-LPZ neurons were displaced to the retinal lesion border while displaying normal orientation and direction selectivity. These results corroborate the view that cortical horizontal connections have a central role in functional reorganization, as revealed here by progressive facilitation of synaptic activity and the traveling wave of excitation that propagates horizontally into the deprived cortical region.

Competitive STDP-Based Spike Pattern Learning

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern.

Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges.

Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.