Reversible associative depression and nonassociative potentiation at a parallel fiber synapse

The electrosensory lobe (ELL) of mormyrid electric fish is one of several cerebellum-like sensory structures in fish that remove predictable features of the sensory inflow. This adaptive process obeys anti-Hebbian rules and appears to be mediated by associative depression at the synapses between parallel fibers and Purkinje-like cells of ELL. We show here that there is also a nonassociative potentiation at this synapse that depends only on the repeated occurrence of the EPSP. The depression can be reversed by the potentiation and vice versa. Finally, we show that the associative depression requires NMDA receptor activation, changes in postsynaptic calcium, and the occurrence of a postsynaptic dendritic spike within a few milliseconds following EPSP onset.     

Sensory expectations and anti-Hebbian synaptic plasticity in cerebellum-like structures

Cerebellum-like sensory structures in different groups of fish have been shown to generate a negative image of predictable features of the sensory input. We show here that anti-Hebbian plasticity is present at the synapse between parallel fibers and Purkinje-like cells which could mediate the generation of these negative images. We also show that this synapse is capable of bidirectional changes in synaptic efficacy with the direction of change depending on the precise temporal relation of presynaptic input and postsynaptic spike during pairing. Parallel fiber-evoked EPSPs are depressed after pairings in which the EPSP begins between 0 and 60 ms before the postsynaptic spike but are enhanced at other delays, including those in which the postsysnaptic spike occurs just before the EPSP.     

Natural patterns of activity and long-term synaptic plasticity

Long-term potentiation (LTP) of synaptic transmission is traditionally elicited by massively synchronous, high-frequency inputs, which rarely occur naturally. Recent in vitro experiments have revealed that both LTP and long-term depression (LTD) can arise by appropriately pairing weak synaptic inputs with action potentials in the postsynaptic cell. This discovery has generated new insights into the conditions under which synaptic modification may occur in pyramidal neurons in vivo. First, it has been shown that the temporal order of the synaptic input and the postsynaptic spike within a narrow temporal window determines whether LTP or LTD is elicited, according to a temporally asymmetric Hebbian learning rule. Second, backpropagating action potentials are able to serve as a global signal for synaptic plasticity in a neuron compared with local associative interactions between synaptic inputs on dendrites. Third, a specific temporal pattern of activity--postsynaptic bursting--accompanies synaptic potentiation in adults.     

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.     

Active control of spike-timing dependent synaptic plasticity in an electrosensory system.

A spike timing dependent learning rule is present at the synapse between parallel fibers and Purkinje-like medium ganglion cells in the electrosensory lobe of mormyrid electric fish. The synapse is depressed when a postsynaptic dendritic spike occurs within 50 ms of the onset of a parallel fiber excitatory postsynaptic potential, but is enhanced at all other timing relations. Operation of this learning rule results in the cancellation of predictable membrane potential changes, driving the cell towards a constant output frequency. But medium ganglion cells show a strong and predictable response to corollary discharge signals associated with the motor command that initiates the electric organ discharge. The modeling study presented here resolves this conflict by proposing an active control of dendritic spike threshold during the brief period of medium ganglion cell response.     

Spatiotemporal specificity of neuronal activity directs the modification of receptive fields in the developing retinotectal system

The precise temporal relation between pre- and postsynaptic activity modulates the strength of synaptic connections. Despite its extensive characterization in vivo and in vitro, the degree to which spike timing-dependent plasticity can shape receptive field properties is unclear. We use in vivo patch-clamp recordings of tectal neurons in developing Xenopus tadpoles to control the precise timing of action potentials with respect to the arrival of a subset of visual inputs evoked by local light stimulation on the retina. The pattern of visual inputs onto a tectal neuron was tracked over time by rapid reverse correlation mapping of receptive fields. Spike timing-dependent potentiation or depression of a subset of synapses reliably shifts the spatial receptive fields toward or away from the trained subregion of visual space, respectively. These results demonstrate that natural patterns of activity evoked by sensory stimuli play an instructive role in the developing nervous system.     

Analysis of Slow (Theta) Oscillations as a Potential Temporal Reference Frame for Information Coding in Sensory Cortices

While sensory neurons carry behaviorally relevant information in responses that often extend over hundreds of milliseconds, the key units of neural information likely consist of much shorter and temporally precise spike patterns. The mechanisms and temporal reference frames by which sensory networks partition responses into these shorter units of information remain unknown. One hypothesis holds that slow oscillations provide a network-intrinsic reference to temporally partitioned spike trains without exploiting the millisecond-precise alignment of spikes to sensory stimuli. We tested this hypothesis on neural responses recorded in visual and auditory cortices of macaque monkeys in response to natural stimuli. Comparing different schemes for response partitioning revealed that theta band oscillations provide a temporal reference that permits extracting significantly more information than can be obtained from spike counts, and sometimes almost as much information as obtained by partitioning spike trains using precisely stimulus-locked time bins. We further tested the robustness of these partitioning schemes to temporal uncertainty in the decoding process and to noise in the sensory input. This revealed that partitioning using an oscillatory reference provides greater robustness than partitioning using precisely stimulus-locked time bins. Overall, these results provide a computational proof of concept for the hypothesis that slow rhythmic network activity may serve as internal reference frame for information coding in sensory cortices and they foster the notion that slow oscillations serve as key elements for the computations underlying perception.     

Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex

Experience-dependent plasticity in somatosensory (S1) and visual (V1) cortex involves rapid depression of responses to a deprived sensory input (a closed eye or a trimmed whisker). Such depression occurs first in layer II/III and may reflect plasticity at vertical inputs from layer IV to layer II/III pyramids. Here, I describe a timing-based, associative form of long-term potentiation and depression (LTP/LTD) at this synapse in S1. LTP occurred when excitatory postsynaptic potentials (EPSPs) led single postsynaptic action potentials (APs) within a narrow temporal window, and LTD occurred when APs led EPSPs within a significantly broader window. This long LTD window is unusual among timing-based learning rules and causes EPSPs that are uncorrelated with postsynaptic APs to become depressed. This behavior suggests a simple model for depression of deprived sensory responses in S1 and V1.     

Computational Consequences of Temporally Asymmetric Learning Rules: I. Differential Hebbian Learning

Temporally asymetric learning rules governing plastic changes in synaptic efficacy have recently been identified in physiological studies. In these rules, the exact timing of pre- and postsynaptic spikes is critical to the induced change of synaptic efficacy. The temporal learning rules treated in this article are approximately antisymmetric; the synaptic efficacy is enhanced if the postsynaptic spike follows the presynaptic spike by a few milliseconds, but the efficacy is depressed if the postsynaptic spike precedes the presynaptic spike. The learning dynamics of this rule are studied using a stochastic model neuron receiving a set of serially delayed inputs. The average change of synaptic efficacy due to the temporally antisymmetric learning rule is shown to yield differential Hebbian learning. These results are demonstrated with both mathematical analyses and computer simulations, and connections with theories of classical conditioning are discussed.     

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.     

The cellular mechanisms of learning in Aplysia: of blind men and elephants

Until recently, investigations of the neurobiological substrates of simple forms of learning and memory in the marine snail Aplysia have focused mostly on plastic changes that occur within the presynaptic sensory neurons. Here, I summarize the results of recent studies that indicate that exclusively presynaptic processes cannot account for simple forms of learning in Aplysia. In particular, I present evidence that postsynaptic mechanisms play a far more important role in nonassociative learning in Aplysia than has been appreciated before now. Moreover, I describe recent data that suggests the intriguing hypothesis that the persistent, learning-induced changes in Aplysia sensory neurons might depend critically on postsynaptic signals for their induction. Finally, I discuss the potential applicability of this hypothesis to learning-related synaptic plasticity in the mammalian brain.     

Activity-dependent regulation of receptive field properties of cat area 17 by supervised Hebbian learning.

Most algorithms currently used to model synaptic plasticity in self-organizing cortical networks suppose that the change in synaptic efficacy is governed by the same structuring factor, i.e., the temporal correlation of activity between pre- and postsynaptic neurons. Functional predictions generated by such algorithms have been tested electrophysiologically in the visual cortex of anesthetized and paralyzed cats. Supervised learning procedures were applied at the cellular level to change receptive field (RF) properties during the time of recording of an individual functionally identified cell. The protocols were devised as cellular analogs of the plasticity of RF properties, which is normally expressed during a critical period of postnatal development. We summarize here evidence demonstrating that changes in covariance between afferent input and postsynaptic response imposed during extracellular and intracellular conditioning can acutely induce selective long-lasting up- and down-regulations of visual responses. The functional properties that could be modified in 40% of cells submitted to differential pairing protocols include ocular dominance, orientation selectivity and orientation preference, interocular orientation disparity, and the relative dominance of ON and OFF responses. Since changes in RF properties can be induced in the adult as well, our findings also suggest that similar activity-dependent processes may occur during development and during active phases of learning under the supervision of behavioral attention or contextual signals. Such potential for plasticity in primary visual cortical neurons suggests the existence of a hidden connectivity expressing a wider functional competence than the one revealed at the spiking level. In particular, in the spatial domain the sensory synaptic integration field is larger than the classical discharge field. It can be shaped by supervised learning and its subthreshold extent can be unmasked by the pharmacological blockade of intracortical inhibition.     

Storage of correlated patterns in standard and bistable Purkinje cell models

The cerebellum has long been considered to undergo supervised learning, with climbing fibers acting as a 'teaching' or 'error' signal. Purkinje cells (PCs), the sole output of the cerebellar cortex, have been considered as analogs of perceptrons storing input/output associations. In support of this hypothesis, a recent study found that the distribution of synaptic weights of a perceptron at maximal capacity is in striking agreement with experimental data in adult rats. However, the calculation was performed using random uncorrelated inputs and outputs. This is a clearly unrealistic assumption since sensory inputs and motor outputs carry a substantial degree of temporal correlations. In this paper, we consider a binary output neuron with a large number of inputs, which is required to store associations between temporally correlated sequences of binary inputs and outputs, modelled as Markov chains. Storage capacity is found to increase with both input and output correlations, and diverges in the limit where both go to unity. We also investigate the capacity of a bistable output unit, since PCs have been shown to be bistable in some experimental conditions. Bistability is shown to enhance storage capacity whenever the output correlation is stronger than the input correlation. Distribution of synaptic weights at maximal capacity is shown to be independent on correlations, and is also unaffected by the presence of bistability.     

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 plasticity: common themes and divergent vistas

Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.     

The chronotron: a neuron that learns to fire temporally precise spike patterns

In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.     

Time-Warp–Invariant Neuronal Processing

Fluctuations in the temporal durations of sensory signals constitute a major source of variability within natural stimulus ensembles. The neuronal mechanisms through which sensory systems can stabilize perception against such fluctuations are largely unknown. An intriguing instantiation of such robustness occurs in human speech perception, which relies critically on temporal acoustic cues that are embedded in signals with highly variable duration. Across different instances of natural speech, auditory cues can undergo temporal warping that ranges from 2-fold compression to 2-fold dilation without significant perceptual impairment. Here, we report that time-warp–invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons. We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task. Applying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp–invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task. Our results demonstrate the important functional role of synaptic conductances in spike-based neuronal information processing and learning. The biophysics of temporal integration at neuronal membranes can endow sensory pathways with powerful time-warp–invariant computational capabilities.     

Time-Warp–Invariant Neuronal Processing

Fluctuations in the temporal durations of sensory signals constitute a major source of variability within natural stimulus ensembles. The neuronal mechanisms through which sensory systems can stabilize perception against such fluctuations are largely unknown. An intriguing instantiation of such robustness occurs in human speech perception, which relies critically on temporal acoustic cues that are embedded in signals with highly variable duration. Across different instances of natural speech, auditory cues can undergo temporal warping that ranges from 2-fold compression to 2-fold dilation without significant perceptual impairment. Here, we report that time-warp–invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons. We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task. Applying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp–invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task. Our results demonstrate the important functional role of synaptic conductances in spike-based neuronal information processing and learning. The biophysics of temporal integration at neuronal membranes can endow sensory pathways with powerful time-warp–invariant computational capabilities.     

Adaptive processing in electrosensory systems: Links to cerebellar plasticity and learning

The first central stage of electrosensory processing in fish takes place in structures with local circuitry that resembles the cerebellum. Cerebellum-like structures and the cerebellum itself share common patterns of gene expression and may also share developmental and evolutionary origins. Given these similarities it is natural to ask whether insights gleaned from the study of cerebellum-like structures might be useful for understanding aspects of cerebellar function and vice versa. Work from electrosensory systems has shown that cerebellum-like circuitry acts to generate learned predictions about the sensory consequences of the animals’ own behavior through a process of associative plasticity at parallel fiber synapses. Subtraction of these predictions from the actual sensory input serves to highlight unexpected and hence behaviorally relevant features. Learning and prediction are also central to many current ideas regarding the function of the cerebellum itself. The present review draws comparisons between cerebellum-like structures and the cerebellum focusing on the properties and sites of synaptic plasticity in these structures and on connections between plasticity and learning. Examples are drawn mainly from the electrosensory lobe (ELL) of mormyrid fish and from extensive work characterizing the role of the cerebellum in Pavlovian eyelid conditioning and vestibulo-ocular reflex (VOR) modification. Parallels with other cerebellum-like structures, including the gymnotid ELL, the elasmobranch dorsal octavolateral nucleus (DON), and the mammalian dorsal cochlear nucleus (DCN) are also discussed.     

Comparison of microglomerular structures in the mushroom body calyx of neopteran insects

Mushroom bodies (MBs) are prominent neuropils in the insect brain involved in higher order processing such as sensory integration, learning and memory, and spatial orientation. The size and general morphology of MBs are diverse across insects. In this study we comparatively investigated the microstructure of synaptic complexes (microglomeruli) in major sensory input regions of the MBs, the calyces, across various neopteran insect species. Pre- and postsynaptic compartments of microglomeruli were analyzed using anti-synapsin immunocytochemistry, f-actin-phalloidin labeling and high-resolution confocal microscopy. Our results suggest that calycal microglomeruli are present across all investigated neopteran insect species, but differences are found in the distribution of synapsin and f-actin within their pre- and postsynaptic compartments. Hymenopteran MBs contain the highest number and packing density of microglomeruli compared to all other species from the different insect orders we investigated. We conclude that the evolution of high numbers of microglomeruli in Hymenoptera may reflect an increase in synaptic microcircuits, which could enhance the computational capacities of the MBs.