Rate, timing, and cooperativity jointly determine cortical synaptic plasticity.

Cortical long-term plasticity depends on firing rate, spike timing, and cooperativity among inputs, but how these factors interact during realistic patterns of activity is unknown. Here we monitored plasticity while systematically varying the rate, spike timing, and number of coincident afferents. These experiments demonstrate a novel form of cooperativity operating even when postsynaptic firing is evoked by current injection, and reveal a complex dependence of LTP and LTD on rate and timing. Based on these data, we constructed and tested three quantitative models of cortical plasticity. One of these models, in which spike-timing relationships causing LTP "win" out over those favoring LTD, closely fits the data and accurately predicts the build-up of plasticity during random firing. This provides a quantitative framework for predicting the impact of in vivo firing patterns on synaptic strength.     

Neuromodulators control the polarity of spike-timing-dependent synaptic plasticity

Near coincidental pre- and postsynaptic action potentials induce associative long-term potentiation (LTP) or long-term depression (LTD), depending on the order of their timing. Here, we show that in visual cortex the rules of this spike-timing-dependent plasticity are not rigid, but shaped by neuromodulator receptors coupled to adenylyl cyclase (AC) and phospholipase C (PLC) signaling cascades. Activation of the AC and PLC cascades results in phosphorylation of postsynaptic glutamate receptors at sites that serve as specific "tags" for LTP and LTD. As a consequence, the outcome (i.e., whether LTP or LTD) of a given pattern of pre- and postsynaptic firing depends not only on the order of the timing, but also on the relative activation of neuromodulator receptors coupled to AC and PLC. These findings indicate that cholinergic and adrenergic neuromodulation associated with the behavioral state of the animal can control the gating and the polarity of cortical plasticity.     

Balanced networks under spike-time dependent plasticity

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.     

Stimulus timing-dependent plasticity in cortical processing of orientation.

The relative timing of presynaptic and postsynaptic spikes plays a critical role in activity-induced synaptic modification. Here we examined whether plasticity of orientation selectivity in the visual cortex depends on stimulus timing. Repetitive pairing of visual stimuli at two orientations induced a shift in orientation tuning of cat cortical neurons, with the direction of the shift depending on the temporal order of the pair. Induction of a significant shift required that the interval between the pair fall within +/-40 ms, reminiscent of the temporal window for spike timing-dependent synaptic plasticity. Mirroring the plasticity found in cat visual cortex, similar conditioning also induced a shift in perceived orientation by human subjects, further suggesting functional relevance of this phenomenon. Thus, relative timing of visual stimuli can play a critical role in dynamic modulation of adult cortical function, perhaps through spike timing-dependent synaptic plasticity.     

Balancing feed-forward excitation and inhibition via Hebbian inhibitory synaptic plasticity

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates 'negative feedback' that balances excitation and inhibition, which contrasts with the 'positive feedback' of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs.     

Calcium signal-dependent plasticity of neuronal excitability developed postnatally

Neuronal plasticity and its development were investigated at pyramidal neurons in the cortical slices of rats. The threshold and probability of firing spikes were measured by using whole-cell recording to assess neuronal excitability. Postsynaptic high frequency activity (HFA) at the pyramidal neurons, evoked by 20 trains (250-ms interval) of five depolarization-pulses (1 ms) at 100 Hz, persistently lowered the threshold and increased the probability of firing spikes. After long-term enhancement of neuronal excitability by HFA was stable, another HFA induced further enhancement. Infusing 1 mM 1,2-bis(2-aminophenoxy)-ethane-N, N,N',N'-tetraacetic acid or 100 microM CaMKII(281-301) into the recording neurons prevented HFA-induced long-term enhancement of neuronal excitability. The infusion of 40 microM calcineurin autoinhibitory peptide enhanced neuronal excitability, which occluded HFA effect. HFA-induced long-term enhancement of intrinsic excitability expressed at most pyramidal neurons after postnatal day (PND) 14, but not at those before PND 9. Our results show a new type of neuronal plasticity induced by physiological activity at cortical neurons, which requires calcium-dependent protein phosphorylation and develops during postnatal period. An upregulation of intrinsic excitability at cortical neurons facilitates their activity and broadens signal codes; consequently, their computational ability is upgraded.     

Enhancement of spike-timing precision by autaptic transmission in neocortical inhibitory interneurons

In vivo studies suggest that precise firing of neurons is important for correct sensory representation. Principal neocortical neurons fire imprecisely when repeatedly activated by fixed sensory stimuli or current depolarizations. Here we show that in contrast to pyramidal neurons, firing in neocortical GABAergic fast-spiking (FS) interneurons is quite precise. FS interneurons are self-innervated by powerful GABAergic autaptic connections reliably activated after each spike, suggesting that autapses strongly regulate FS-cell spike timing. Indeed, blockade of autaptic transmission degraded temporal precision in multiple ways. Under these conditions, realistic dynamic-clamp hyperpolarizing autapses restored precision of spike timing, even in the presence of synaptic noise. Furthermore, firing precision was increased in pyramidal neurons by artificial GABAergic autaptic conductances, suggesting that tightly coupled synaptic feedback inhibition regulates spike timing in principal cells. Thus, well-timed inhibition, whether autaptic or synaptic, facilitates precise spike timing and promotes synchronized cortical network oscillations relevant to several behaviors.     

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.     

Satb1 ablation alters temporal expression of immediate early genes and reduces dendritic spine density during postnatal brain development

Complex behaviors, such as learning and memory, are associated with rapid changes in gene expression of neurons and subsequent formation of new synaptic connections. However, how external signals are processed to drive specific changes in gene expression is largely unknown. We found that the genome organizer protein Satb1 is highly expressed in mature neurons, primarily in the cerebral cortex, dentate hilus, and amygdala. In Satb1-null mice, cortical layer morphology was normal. However, in postnatal Satb1-null cortical pyramidal neurons, we found a substantial decrease in the density of dendritic spines, which play critical roles in synaptic transmission and plasticity. Further, we found that in the cerebral cortex, Satb1 binds to genomic loci of multiple immediate early genes (IEGs) (Fos, Fosb, Egr1, Egr2, Arc, and Bdnf) and other key neuronal genes, many of which have been implicated in synaptic plasticity. Loss of Satb1 resulted in greatly alters timing and expression levels of these IEGs during early postnatal cerebral cortical development and also upon stimulation in cortical organotypic cultures. These data indicate that Satb1 is required for proper temporal dynamics of IEG expression. Based on these findings, we propose that Satb1 plays a critical role in cortical neurons to facilitate neuronal plasticity.     

Maturation of GABAergic Inhibition Promotes Strengthening of Temporally Coherent Inputs among Convergent Pathways

Spike-timing-dependent plasticity (STDP), a form of Hebbian plasticity, is inherently stabilizing. Whether and how GABAergic inhibition influences STDP is not well understood. Using a model neuron driven by converging inputs modifiable by STDP, we determined that a sufficient level of inhibition was critical to ensure that temporal coherence (correlation among presynaptic spike times) of synaptic inputs, rather than initial strength or number of inputs within a pathway, controlled postsynaptic spike timing. Inhibition exerted this effect by preferentially reducing synaptic efficacy, the ability of inputs to evoke postsynaptic action potentials, of the less coherent inputs. In visual cortical slices, inhibition potently reduced synaptic efficacy at ages during but not before the critical period of ocular dominance (OD) plasticity. Whole-cell recordings revealed that the amplitude of unitary IPSCs from parvalbumin positive (Pv+) interneurons to pyramidal neurons increased during the critical period, while the synaptic decay time-constant decreased. In addition, intrinsic properties of Pv+ interneurons matured, resulting in an increase in instantaneous firing rate. Our results suggest that maturation of inhibition in visual cortex ensures that the temporally coherent inputs (e.g. those from the open eye during monocular deprivation) control postsynaptic spike times of binocular neurons, a prerequisite for Hebbian mechanisms to induce OD plasticity.     

Effect of the topology and delayed interactions in neuronal networks synchronization

As important as the intrinsic properties of an individual nervous cell stands the network of neurons in which it is embedded and by virtue of which it acquires great part of its responsiveness and functionality. In this study we have explored how the topological properties and conduction delays of several classes of neural networks affect the capacity of their constituent cells to establish well-defined temporal relations among firing of their action potentials. This ability of a population of neurons to produce and maintain a millisecond-precise coordinated firing (either evoked by external stimuli or internally generated) is central to neural codes exploiting precise spike timing for the representation and communication of information. Our results, based on extensive simulations of conductance-based type of neurons in an oscillatory regime, indicate that only certain topologies of networks allow for a coordinated firing at a local and long-range scale simultaneously. Besides network architecture, axonal conduction delays are also observed to be another important factor in the generation of coherent spiking. We report that such communication latencies not only set the phase difference between the oscillatory activity of remote neural populations but determine whether the interconnected cells can set in any coherent firing at all. In this context, we have also investigated how the balance between the network synchronizing effects and the dispersive drift caused by inhomogeneities in natural firing frequencies across neurons is resolved. Finally, we show that the observed roles of conduction delays and frequency dispersion are not particular to canonical networks but experimentally measured anatomical networks such as the macaque cortical network can display the same type of behavior.     

The Role of Thalamic Population Synchrony in the Emergence of Cortical Feature Selectivity

In a wide range of studies, the emergence of orientation selectivity in primary visual cortex has been attributed to a complex interaction between feed-forward thalamic input and inhibitory mechanisms at the level of cortex. Although it is well known that layer 4 cortical neurons are highly sensitive to the timing of thalamic inputs, the role of the stimulus-driven timing of thalamic inputs in cortical orientation selectivity is not well understood. Here we show that the synchronization of thalamic firing contributes directly to the orientation tuned responses of primary visual cortex in a way that optimizes the stimulus information per cortical spike. From the recorded responses of geniculate X-cells in the anesthetized cat, we synthesized thalamic sub-populations that would likely serve as the synaptic input to a common layer 4 cortical neuron based on anatomical constraints. We used this synchronized input as the driving input to an integrate-and-fire model of cortical responses and demonstrated that the tuning properties match closely to those measured in primary visual cortex. By modulating the overall level of synchronization at the preferred orientation, we show that efficiency of information transmission in the cortex is maximized for levels of synchronization which match those reported in thalamic recordings in response to naturalistic stimuli, a property which is relatively invariant to the orientation tuning width. These findings indicate evidence for a more prominent role of the feed-forward thalamic input in cortical feature selectivity based on thalamic synchronization.     

Pairing-induced changes of orientation maps in cat visual cortex.

We have studied the precise temporal requirements for plasticity of orientation preference maps in kitten visual cortex. Pairing a brief visual stimulus with electrical stimulation in the cortex, we found that the relative timing determines the direction of plasticity: a shift in orientation preference toward the paired orientation occurs if the cortex is activated first visually and then electrically; the cortical response to the paired orientation is diminished if the sequence of visual and electrical activation is reversed. We furthermore show that pinwheel centers are less affected by the pairing than the pinwheel surround. Thus, plasticity is not uniformly distributed across the cortex, and, most importantly, the same spike time-dependent learning rules that have been found in single-cell in vitro studies are also valid on the level of cortical maps.     

Top-Down Inputs Enhance Orientation Selectivity in Neurons of the Primary Visual Cortex during Perceptual Learning

Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections.     

Coding of deflection velocity and amplitude by whisker primary afferent neurons: implications for higher level processing

Within the rat whisker-to-barrel pathway, local circuits in cortical layer IV are more sensitive to the initial timing of deflection-evoked thalamic responses than to the total number of spikes comprising them. Because thalamic response timing better reflects whisker deflection velocity than amplitude, cortical neurons are more responsive to the former than the latter. The aim of this study is to determine how deflection velocity and amplitude may be encoded by the primary afferent neurons innervating the vibrissae. Responses of 81 extracellularly recorded trigeminal ganglion neurons (60 slowly and 21 rapidly adapting) were studied using controlled whisker stimuli identical to those used previously to investigate the velocity and amplitude sensitivities of thalamic and cortical neurons. For either slowly (SA) or rapidly adapting (RA) neurons, velocity is reflected by both response magnitude, measured as the total number of evoked spikes/stimulus, and initial firing rate, measured as the number of spikes discharged during the first 2 ms of the response. Deflection amplitude, on the other hand, is represented only by the SA population in their response magnitudes. Thus, in both populations initial firing rates unambiguously reflect deflection velocity. Together with previous findings, results demonstrate that information about deflection velocity is preserved throughout the whisker-to-barrel pathway by central circuits sensitive to initial response timing.     

Balancing homeostasis and learning in neural circuits

Neural circuits are remarkably adaptable, providing animals with the ability to modify their behavior on the basis of experience. At the same time, they are extremely robust and maintain stability despite the changes associated with adaptation. This combination of adaptability and stability is difficult to achieve, and it provides a strong constraint on any models of plasticity in neural circuits. New evidence suggests that the effect of action potential timing on synaptic plasticity may be an important element in reconciling homeostasis with adaptability. In particular, spike-timing dependent plasticity can act as both an adaptive and a homeostatic mechanism, controlling overall firing rates and distributions of synaptic efficacies while making neurons selective for certain aspects of their inputs. It can also cause networks that initially represent the present state of a stimulus to predict its future state on the basis of experience, a theoretical result supported by experimental data in behaving rats.     

Translation-invariant pattern recognition based on Synfire chains

Most of current neural network architectures are not suited to recognize a pattern at various displaced positions. This lack seems due to the prevailing neuron model which reduces a neuron's information transmission to its firing rate. With this information code, a neuronal assembly cannot distinguish between different combinations of its entities and therefore fails to represent the fine structure within a pattern. In our approach, the main idea of the correlation theory is accepted that spatial relationships in a pattern should be coded by temporal relations in the timing of action potentials. However, we do not assume that synchronized spikes are a sign for strong synapses between the neurons concerned. Instead, the synchronization of Synfire chains can be exploited to produce the relevant timing relationships between the neuronal signals. Therefore, we do not require fast synaptic plasticity to account for the precise timing of action potentials. In order to illustrate this claim, we propose a model for translation-invariant pattern recognition which does not depend on any changes in synaptic efficacies. Received: 14 June 1998 / Accepted in revised form: 9 January 1999     

A Calcium-Dependent Plasticity Rule for HCN Channels Maintains Activity Homeostasis and Stable Synaptic Learning

Theoretical and computational frameworks for synaptic plasticity and learning have a long and cherished history, with few parallels within the well-established literature for plasticity of voltage-gated ion channels. In this study, we derive rules for plasticity in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and assess the synergy between synaptic and HCN channel plasticity in establishing stability during synaptic learning. To do this, we employ a conductance-based model for the hippocampal pyramidal neuron, and incorporate synaptic plasticity through the well-established Bienenstock-Cooper-Munro (BCM)-like rule for synaptic plasticity, wherein the direction and strength of the plasticity is dependent on the concentration of calcium influx. Under this framework, we derive a rule for HCN channel plasticity to establish homeostasis in synaptically-driven firing rate, and incorporate such plasticity into our model. In demonstrating that this rule for HCN channel plasticity helps maintain firing rate homeostasis after bidirectional synaptic plasticity, we observe a linear relationship between synaptic plasticity and HCN channel plasticity for maintaining firing rate homeostasis. Motivated by this linear relationship, we derive a calcium-dependent rule for HCN-channel plasticity, and demonstrate that firing rate homeostasis is maintained in the face of synaptic plasticity when moderate and high levels of cytosolic calcium influx induced depression and potentiation of the HCN-channel conductance, respectively. Additionally, we show that such synergy between synaptic and HCN-channel plasticity enhances the stability of synaptic learning through metaplasticity in the BCM-like synaptic plasticity profile. Finally, we demonstrate that the synergistic interaction between synaptic and HCN-channel plasticity preserves robustness of information transfer across the neuron under a rate-coding schema. Our results establish specific physiological roles for experimentally observed plasticity in HCN channels accompanying synaptic plasticity in hippocampal neurons, and uncover potential links between HCN-channel plasticity and calcium influx, dynamic gain control and stable synaptic learning.     

Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity

Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.