Reversible associative depression and nonassociative potentiation at a parallel fiber synapse

A mechanism for differential control of axonal and dendritic spiking underlying learning in a cerebellum-like circuit

In addition to the action potentials used for axonal signaling, many neurons generate dendritic "spikes" associated with synaptic plasticity. However, in order to control both plasticity and signaling, synaptic inputs must be able to differentially modulate the firing of these two spike types. Here, we investigate this issue in the electrosensory lobe (ELL) of weakly electric mormyrid fish, where separate control over axonal and dendritic spikes is essential for the transmission of learned predictive signals from inhibitory interneurons to the output stage of the circuit. Through a combination of experimental and modeling studies, we uncover a novel mechanism by which sensory input selectively modulates the rate of dendritic spiking by adjusting the amplitude of backpropagating axonal action potentials. Interestingly, this mechanism does not require spatially segregated synaptic inputs or dendritic compartmentalization but relies instead on an electrotonically distant spike initiation site in the axon-a common biophysical feature of neurons.

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

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

Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks

A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning processes using the Hausdorff dimension of the trajectories of the learning algorithm. The paper analyzes the effects of STDP learning models and associated hyper-parameters on the generalizability properties of an SNN. The analysis is used to develop a Bayesian optimization approach to optimize the hyper-parameters for an STDP model for improving the generalizability properties of an SNN.

Alternation of Neuronal Feature Selectivity Induced by Paired Optogenetic-Mechanical Stimulation in the Barrel Cortex

Paired stimulation has been applied to modulate neuronal functions in the primary somatosensory cortex but its utility in the alternation of tuning function, such as direction tuning for whisker stimuli, remains unclear. In the present study, we attempted to manipulate feature preferences in barrel cortical neurons using repetitive paired whisker deflection combined with optogenetic stimulation and to obtain optimal parameters that can induce neuroplasticity. We found no significant response changes across stimulus parameters, such as onset asynchronies and paired directions. Only when paired stimulation was applied in the nonpreferred direction of the principal whisker of a neuron, were the neuron’s responses enhanced in that direction. Importantly, this effect was only observed when the optogenetic stimulus preceded the mechanical stimulus. Our findings indicate that repetitive paired optogenetic-mechanical stimulation can induce in vivo neuroplasticity of feature selectivity in limited situations.

50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.

Continual Learning in a Multi-Layer Network of an Electric Fish

Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems.

Internally Generated Predictions Enhance Neural and Behavioral Detection of Sensory Stimuli in an Electric Fish

Studies of cerebellum-like circuits in fish have demonstrated that synaptic plasticity shapes the motor corollary discharge responses of granule cells into highly-specific predictions of self-generated sensory input. However, the functional significance of such predictions, known as negative images, has not been directly tested. Here we provide evidence for improvements in neural coding and behavioral detection of prey-like stimuli due to negative images. In addition, we find that manipulating synaptic plasticity leads to specific changes in circuit output that disrupt neural coding and detection of prey-like stimuli. These results link synaptic plasticity, neural coding, and behavior and also provide a circuit-level account of how combining external sensory input with internally generated predictions enhances sensory processing.

A Hypothetical Model Concerning How Spike-Timing-Dependent Plasticity Contributes to Neural Circuit Formation and Initiation of the Critical Period in Barrel Cortex

Spike timing is an important factor in the modification of synaptic strength. Various forms of spike timing-dependent plasticity (STDP) occur in the brains of diverse species, from insects to humans. In unimodal STDP, only LTP or LTD occurs at the synapse, regardless of which neuron spikes first; the magnitude of potentiation or depression increases as the time between presynaptic and postsynaptic spikes decreases. This from of STDP may promote developmental strengthening or weakening of early projections. In bidirectional Hebbian STDP, the magnitude and the sign (potentiation or depression) of plasticity depend, respectively, on the timing and the order of presynaptic and postsynaptic spikes. In the rodent barrel cortex, multiple forms of STDP appear sequentially during development, and they contribute to network formation, retraction, or fine-scale functional reorganization. Hebbian STDP appears at L4-L2/3 synapses starting at postnatal day (P) 15; the synapses exhibit unimodal "all-LTP STDP" before that age. The appearance of Hebbian STDP at L4-L2/3 synapses coincides with the maturation of parvalbumin-containing GABA interneurons in L4, which contributes to the generation of L4-before-L2/3 spiking in response to thalamic input by producing fast feedforward suppression of both L4 and L2/3 cells. After P15, L4-L2/3 STDP mediates fine-scale circuit refinement, essential for the critical period in the barrel cortex. In this review, we first briefly describe the relevance of STDP to map plasticity in the barrel cortex, then look over roles of distinct forms of STDP during development. Finally, we propose a hypothesis that explains the transition from network formation to the initiation of the critical period in the barrel cortex.

Generalization of learned responses in the mormyrid electrosensory lobe

Appropriate generalization of learned responses to new situations is vital for adaptive behavior. We provide a circuit-level account of generalization in the electrosensory lobe (ELL) of weakly electric mormyrid fish. Much is already known in this system about a form of learning in which motor corollary discharge signals cancel responses to the uninformative input evoked by the fish's own electric pulses. However, for this cancellation to be useful under natural circumstances, it must generalize accurately across behavioral regimes, specifically different electric pulse rates. We show that such generalization indeed occurs in ELL neurons, and develop a circuit-level model explaining how this may be achieved. The mechanism involves regularized synaptic plasticity and an approximate matching of the temporal dynamics of motor corollary discharge and electrosensory inputs. Recordings of motor corollary discharge signals in mossy fibers and granule cells provide direct evidence for such matching.

Dorsal Cochlear Nucleus Fusiform-cell Plasticity is Altered in Salicylate-induced Tinnitus

Following noise overexposure and tinnitus-induction, fusiform cells of the dorsal cochlear nucleus (DCN) show increased spontaneous firing rates (SFR), increased spontaneous synchrony and altered stimulus-timing-dependent plasticity (StDP), which correlate with behavioral measures of tinnitus. Sodium salicylate, the active ingredient in aspirin, which is commonly used to induce tinnitus, increases SFR and activates NMDA receptors in the ascending auditory pathway. NMDA receptor activation is required for StDP in many brain regions, including the DCN. Blocking NMDA receptors can alter StDP timing rules and decrease synchrony in DCN fusiform cells. Thus, systemic activation of NMDA receptors with sodium salicylate should elicit pathological changes to StDP, thereby increasing SFR and synchrony and induce tinnitus. Herein, we examined the action of salicylate in tinnitus generation in guinea pigs in vivo by measuring tinnitus using two behavioral measures and recording single-unit responses from DCN fusiform cells pre- and post-salicylate administration in the same animals. First, we show that animals administered salicylate show evidence of tinnitus using both behavioral paradigms, cross-validating the tests. Second, fusiform cells in animals with tinnitus showed increased SFR, synchrony and altered StDP timing rules, like animals with noise-induced tinnitus. These findings suggest that alterations to fusiform-cell plasticity are an essential component of tinnitus, regardless of induction technique.

Interplay of multiple pathways and activity-dependent rules in STDP

Hebbian plasticity describes a basic mechanism for synaptic plasticity whereby synaptic weights evolve depending on the relative timing of paired activity of the pre- and postsynaptic neurons. Spike-timing-dependent plasticity (STDP) constitutes a central experimental and theoretical synaptic Hebbian learning rule. Various mechanisms, mostly calcium-based, account for the induction and maintenance of STDP. Classically STDP is assumed to gradually emerge in a monotonic way as the number of pairings increases. However, non-monotonic STDP accounting for fast associative learning led us to challenge this monotonicity hypothesis and explore how the existence of multiple plasticity pathways affects the dynamical establishment of plasticity. To account for distinct forms of STDP emerging from increasing numbers of pairings and the variety of signaling pathways involved, we developed a general class of simple mathematical models of plasticity based on calcium transients and accommodating various calcium-based plasticity mechanisms. These mechanisms can either compete or cooperate for the establishment of long-term potentiation (LTP) and depression (LTD), that emerge depending on past calcium activity. Our model reproduces accurately the striatal STDP that involves endocannabinoid and NMDAR signaling pathways. Moreover, we predict how stimulus frequency alters plasticity, and how triplet rules are affected by the number of pairings. We further investigate the general model with an arbitrary number of pathways and show that depending on those pathways and their properties, a variety of plasticities may emerge upon variation of the number and/or the frequency of pairings, even when the outcome after large numbers of pairings is identical. These findings, built upon a biologically realistic example and generalized to other applications, argue that in order to fully describe synaptic plasticity it is not sufficient to record STDP curves at fixed pairing numbers and frequencies. In fact, considering the whole spectrum of activity-dependent parameters could have a great impact on the description of plasticity, and a better understanding of the engram.

The brain’s capacity to treat information, learn and store memory relies on synaptic connectivity patterns, which are altered through synaptic plasticity mechanisms. Experimentally, such plasticities were evidenced through protocols involving numerous repetitive stimulations of a given synapse, and were shown to be supported by multiple pathways. Using a simple biologically grounded mathematical model, we show how activation timescales and inactivation levels of each pathway interact and alter plasticity in an intricate manner as stimuli are presented. Building upon data from the synapse between cortex and striatum, we show that synaptic changes may revert or re-emerge as stimuli are presented, and predict specific responses to changes in stimulus frequency or to distinct simulation patterns. Our general model shows that a given plasticity profile emerging in response to a repetitive stimulation protocol can unfold into various scenarii upon variations of the number of stimulus presentations or patterns, which tightly depends on the underlying activated pathways. Altogether, these results argue that in order to better understand learning and memory, single plasticity responses obtained through intensive stimulations do not reveal the complexity of the responses for smaller number of presentations, which may have a strong impact in fast learning of stimuli with low numbers of presentations.

Cell type-specific plasticity at parallel fiber synapses onto Purkinje cells in the posterior caudal lobe of the mormyrid fish cerebellum

It has been demonstrated that there are two morphological subtypes of Purkinje cells (PCs)-fan-shaped Purkinje cells (fPCs) and multipolar Purkinje cells (mPCs)-in the posterior caudal lobe of the mormyrid fish cerebellum, but whether these cell types are also functionally distinct is unknown. Here, we have used electrophysiological and pharmacological tools in a slice preparation to demonstrate that pairing parallel fiber (PF) and climbing fiber (CF) inputs at a low frequency induces long-term depression (LTD) in fPCs but long-term potentiation (LTP) in mPCs. The induction of plasticity in both cell types required postsynaptic Ca²⁺ and type 1α metabotropic glutamate receptors. However, the LTD in fPCs was inducted via a calcium/calmodulin-dependent protein kinase II cascade, whereas LTP induction in mPCs required calcineurin. Moreover, the LTD in fPCs and LTP in mPCs were accompanied by changes to the corresponding paired-pulse ratios and their coefficients of variation, suggesting presynaptic modes of expression for the plasticity at PF terminals for both cell types. Hence, the synaptic plasticity at PF synapses onto PCs in the posterior caudal lobe of the mormyrid cerebellum is cell type specific, with both pre- and postsynaptic mechanisms contributing to its induction and expression. NEW & NOTEWORTHY Much has been learnt about the cerebellar long-term depression (LTD) in the cortex. More recent work has shown that long-term potentiation (LTP) is equally important for cerebellar motor learning. Here we report for the first time that plasticity in the mormyrid cerebellum is cell type specific, e.g., following the conventional pairing of parallel and climbing fiber inputs in an in vitro preparation leads to LTD in one Purkinje cell subtype and LTP in another.

Synaptic plasticity in human cortical circuits: cellular mechanisms of learning and memory in the human brain?

Synaptic plasticity is the cellular basis of learning and memory, but to what extent this holds for the adult human brain is not known. To study synaptic plasticity in human neuronal circuits poses a huge challenge, since live human neurons and synapses are not readily accessible. Despite this, various lines of research have provided insights in properties of adult human synapses and their plasticity both in vitro and in vivo, with some unexpected surprises. We first discuss the experimental approaches to study activity-dependent plasticity of adult human synapses, and then highlight rules and mechanisms of Hebbian spike timing-dependent plasticity (STDP) found in these synapses. Finally, we conclude with thoughts on how these synaptic principles can underlie human learning and memory.

Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models

In spike-timing dependent plasticity (STDP) change in synaptic strength depends on the timing of pre- vs. postsynaptic spiking activity. Since STDP is in compliance with Hebb's postulate, it is considered one of the major mechanisms of memory storage and recall. STDP comprises a system of two coincidence detectors with N-methyl-D-aspartate receptor (NMDAR) activation often posited as one of the main components. Numerous studies have unveiled a third component of this coincidence detection system, namely neuromodulation and glia activity shaping STDP. Even though dopaminergic control of STDP has most often been reported, acetylcholine, noradrenaline, nitric oxide (NO), brain-derived neurotrophic factor (BDNF) or gamma-aminobutyric acid (GABA) also has been shown to effectively modulate STDP. Furthermore, it has been demonstrated that astrocytes, via the release or uptake of glutamate, gate STDP expression. At the most fundamental level, the timing properties of STDP are expected to depend on the spatiotemporal dynamics of the underlying signaling pathways. However in most cases, due to technical limitations experiments grant only indirect access to these pathways. Computational models carefully constrained by experiments, allow for a better qualitative understanding of the molecular basis of STDP and its regulation by neuromodulators. Recently, computational models of calcium dynamics and signaling pathway molecules have started to explore STDP emergence in ex and in vivo-like conditions. These models are expected to reproduce better at least part of the complex modulation of STDP as an emergent property of the underlying molecular pathways. Elucidation of the mechanisms underlying STDP modulation and its consequences on network dynamics is of critical importance and will allow better understanding of the major mechanisms of memory storage and recall both in health and disease.

Neural Mechanisms for Predicting the Sensory Consequences of Behavior: Insights from Electrosensory Systems

Perception of the environment requires differentiating between external sensory inputs and those that are self-generated. Some of the clearest insights into the neural mechanisms underlying this process have come from studies of the electrosensory systems of fish. Neurons at the first stage of electrosensory processing generate negative images of the electrosensory consequences of the animal's own behavior. By canceling out the effects of predictable, self-generated inputs, negative images allow for the selective encoding of unpredictable, externally generated stimuli. Combined experimental and theoretical studies of electrosensory systems have led to detailed accounts of how negative images are formed at the level of synaptic plasticity rules, cells, and circuits. Here, I review these accounts and discuss their implications for understanding how predictions of the sensory consequences of behavior may be generated in other sensory structures and the cerebellum.

Auditory Golgi cells are interconnected predominantly by electrical synapses

The mossy fiber-granule cell-parallel fiber system conveys proprioceptive and corollary discharge information to principal cells in cerebellum-like systems. In the dorsal cochlear nucleus (DCN), Golgi cells inhibit granule cells and thus regulate information transfer along the mossy fiber-granule cell-parallel fiber pathway. Whereas excitatory synaptic inputs to Golgi cells are well understood, inhibitory and electrical synaptic inputs to Golgi cells have not been examined. Using paired recordings in a mouse brain slice preparation, we find that Golgi cells of the cochlear nucleus reliably form electrical synapses onto one another. Golgi cells were only rarely electrically coupled to superficial stellate cells, which form a separate network of electrically coupled interneurons in the DCN. Spikelets had a biphasic effect on the excitability of postjunctional Golgi cells, with a brief excitatory phase and a prolonged inhibitory phase due to the propagation of the prejunctional afterhyperpolarization through gap junctions. Golgi cells and stellate cells made weak inhibitory chemical synapses onto Golgi cells with low probability. Electrical synapses are therefore the predominant form of synaptic communication between auditory Golgi cells. We propose that electrical synapses between Golgi cells may function to regulate the synchrony of Golgi cell firing when electrically coupled Golgi cells receive temporally correlated excitatory synaptic input.

Symmetric spike timing-dependent plasticity at CA3-CA3 synapses optimizes storage and recall in autoassociative networks

CA3–CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3–CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms). Consistent with these STDP induction properties, AP–EPSP sequences lead to supralinear summation of spine [Ca²⁺] transients. Furthermore, afterdepolarizations (ADPs) following APs efficiently propagate into dendrites of CA3 pyramidal neurons, and EPSPs summate with dendritic ADPs. In autoassociative network models, storage and recall are more robust with symmetric than with asymmetric STDP rules. Thus, a specialized STDP induction rule allows reliable storage and recall of information in the hippocampal CA3 network.

STDP is dependent on the timing of pre- and post-synaptic activity. Here, the authors describe a symmetric STDP induction rule at CA3-CA3 synapses, which induces LTP over a broad range of paring intervals. Modelling suggests that this STDP rule may enhance storage capacity and pattern completion in the CA3 cell network.

Climbing fibers in spinocerebellar ataxia: A mechanism for the loss of motor control

The spinocerebellar ataxias (SCAs) form an ever-growing group of neurodegenerative disorders causing dysfunction of the cerebellum and loss of motor control in patients. Currently, 41 different genetic causes have been identified, with each mutation affecting a different gene. Interestingly, these diverse genetic causes all disrupt cerebellar function and produce similar symptoms in patients. In order to understand the disease better, and define possible therapeutic targets for multiple SCAs, the field has been searching for common ground among the SCAs. In this review, we discuss the physiology of climbing fibers and the possibility that climbing fiber dysfunction is a point of convergence for at least a subset of SCAs.

A biologically plausible learning rule for the Infomax on recurrent neural networks

A fundamental issue in neuroscience is to understand how neuronal circuits in the cerebral cortex play their functional roles through their characteristic firing activity. Several characteristics of spontaneous and sensory-evoked cortical activity have been reproduced by Infomax learning of neural networks in computational studies. There are, however, still few models of the underlying learning mechanisms that allow cortical circuits to maximize information and produce the characteristics of spontaneous and sensory-evoked cortical activity. In the present article, we derive a biologically plausible learning rule for the maximization of information retained through time in dynamics of simple recurrent neural networks. Applying the derived learning rule in a numerical simulation, we reproduce the characteristics of spontaneous and sensory-evoked cortical activity: cell-assembly-like repeats of precise firing sequences, neuronal avalanches, spontaneous replays of learned firing sequences and orientation selectivity observed in the primary visual cortex. We further discuss the similarity between the derived learning rule and the spike timing-dependent plasticity of cortical neurons.

The slow pathway in the electrosensory lobe of Gymnotus omarorum: field potentials and unitary activity

This is a first communication on the self-activation pattern of the electrosensory lobe in the pulse weakly electric fish Gymnotus omarorum. Field potentials in response to the fish's own electric organ discharge (EOD) were recorded along vertical tracks (50μm step) and on a transversal lattice array across the electrosensory lobe (resolution 50μm×100μm). The unitary activity of 82 neurons was recorded in the same experiments. Field potential analysis indicates that the slow electrosensory path shows a characteristic post-EOD pattern of activity marked by three main events: (i) a small and early component at about 7ms, (ii) an intermediate peak about 13ms and (iii) a late broad component peaking after 20ms. Unit firing rate showed a wide range of latencies between 3 and 30ms and a variable number of spikes (median 0.28units/EOD). Conditional probability analysis showed monomodal and multimodal post-EOD histograms, with the peaks of unit activity histograms often matching the timing of the main components of the field potentials. Monomodal responses were sub-classified as phase locked monomodal (variance smaller than 1ms), early monomodal (intermediate variance, often firing in doublets, peaking range 10-17ms) and late monomodal (large variance, often firing two spikes separated about 10ms, peaking beyond 17ms). The responses of multimodal units showed that their firing probability was either enhanced, or depressed just after the EOD. In this last (depressed) subtype of unit the probability stepped down just after the EOD. Early inhibition and the presence of early phase locked units suggest that the observed pattern may be influenced by a fast feed forward inhibition. We conclude that the ELL in pulse gymnotiformes is activated in a complex sequence of events that reflects the ELL network connectivity.

Plastic corollary discharge predicts sensory consequences of movements in a cerebellum-like circuit

The capacity to predict the sensory consequences of movements is critical for sensory, motor, and cognitive function. Though it is hypothesized that internal signals related to motor commands, known as corollary discharge, serve to generate such predictions, this process remains poorly understood at the neural circuit level. Here we demonstrate that neurons in the electrosensory lobe (ELL) of weakly electric mormyrid fish generate negative images of the sensory consequences of the fish's own movements based on ascending spinal corollary discharge signals. These results generalize previous findings describing mechanisms for generating negative images of the effects of the fish's specialized electric organ discharge (EOD) and suggest that a cerebellum-like circuit endowed with associative synaptic plasticity acting on corollary discharge can solve the complex and ubiquitous problem of predicting sensory consequences of movements.

The cerebellum for jocks and nerds alike

Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains.

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.

Dopamine-enabled anti-Hebbian timing-dependent plasticity in prefrontal circuitry

Spike timing-dependent plasticity (STDP) of glutamatergic synapses is a Hebbian associative plasticity that may underlie certain forms of learning. A cardinal feature of STDP is its dependence on the temporal order of presynaptic and postsynaptic spikes during induction: pre–post (positive) pairings induce t-LTP (timing-dependent long-term potentiation) whereas post–pre (negative) pairings induce t-LTD (timing-dependent long-term depression). Dopamine (DA), a reward signal for behavioral learning, is believed to exert powerful modulations on synapse strength and plasticity, but its influence on STDP has remained incompletely understood. We previously showed that DA extends the temporal window of t-LTP in the prefrontal cortex (PFC) from +10 to +30 ms, gating Hebbian t-LTP. Here, we examined DA modulation of synaptic plasticity induced at negative timings in layer V pyramidal neurons on mouse medial PFC slices. Using a negative timing STDP protocol (60 post–pre pairings at 0.1 Hz, δt = -30 ms), we found that DA applied during post–pre pairings did not produce LTD, but instead enabled robust LTP. This anti-Hebbian t-LTP depended on GluN2B-containing NMDA receptors. Blocking D1- (D1Rs), but not D2- (D2Rs) class DA receptors or disrupting cAMP/PKA signaling in pyramidal neurons also abolished this atypical t-LTP, indicating that it was mediated by postsynaptic D1R-cAMP/PKA signaling in excitatory synapses. Unlike DA-enabled Hebbian t-LTP that requires suppression of GABAergic inhibition and cooperative actions of both D1Rs and D2Rs in separate PFC excitatory and inhibitory circuits, DA-enabled anti-Hebbian t-LTP occurred under intact inhibitory transmission and only required D1R activation in excitatory circuit. Our results establish DA as a potent modulator of coincidence detection during associative synaptic plasticity and suggest a mechanism by which DA facilitates input-target association during reward learning and top-down information processing in PFC circuits.

Long-term potentiation at cerebellar parallel fiber-Purkinje cell synapses requires presynaptic and postsynaptic signaling cascades

Long-term depression (LTD) and long-term potentiation (LTP) at cerebellar parallel fiber-Purkinje cell (PF-PC) synapses play critical roles in motor learning. The 1 Hz stimulation at PF-PC synapses induces a postsynaptically expressed LTP that requires a postsynaptic Ca(2+) transient, phosphatases, and nitric oxide (NO). However, the mechanism underlying 1 Hz PF-LTP remains unclear because none of the known events is related to each other. Here, we demonstrated that 1 Hz PF-LTP requires postsynaptic cytosolic phospholipase A2 α (cPLA2α)/arachidonic acid (AA) signaling and presynaptic endocannabinoid receptors. Using patch-clamp recording in cerebellar slices, we found that 1 Hz PF-LTP was abolished in cPLA2α-knock-out mice. This deficit was effectively rescued by the conjunction of 1 Hz PF stimulation and the local application of AA. 2-Arachidonoylglycerol and the retrograde activation of cannabinoid receptor 1 (CB1R) were also involved in 1 Hz LTP because it was blocked by the hydrolysis of 2-AG or by inhibiting CB1Rs. The amount of NO released was detected using an NO electrode in cultured granule cells and PF terminals. Our results showed that the activation of CB1Rs at PF terminals activated NO synthetase and promoted NO production. The 1 Hz PF-stimuli evoked limited NO, but 100 Hz PF stimulation generated a large amount. Therefore, 1 Hz PF-LTP, distinct from classical postsynaptically expressed plasticity, requires concurrent presynaptic and postsynaptic activity. In addition, NO of sufficient amplitude decides between the weakening and strengthening of PF-PC synapses.

Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays

The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry-also known as "open-loop feedback"-, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different regimes of gain control: subtractive, divisive, and non-monotonic. Subtractive gain control was obtained when noise is very low in the network. Also, it was possible to change from divisive to non-monotonic gain control by simply modulating the strength of the feedforward inhibition, which may be achieved via long-term synaptic plasticity. The particular case of divisive gain control has been previously observed in vivo in weakly electric fish. These gain control regimes were robust to the presence of temporal delays in the inhibitory feedforward pathway, which were found to linearize the input-to-output mappings (or f-I curves) via a novel variability-increasing mechanism. Our findings highlight the feedforward-induced gain control analyzed here as a highly versatile mechanism of information gating in the brain.

A temporal basis for predicting the sensory consequences of motor commands in an electric fish

Mormyrid electric fish are a model system for understanding how neural circuits predict the sensory consequences of motor acts. Medium ganglion cells in the electrosensory lobe create negative images that predict sensory input resulting from the fish's electric organ discharge (EOD). Previous studies have shown that negative images can be created through plasticity at granule cell-medium ganglion cell synapses, provided that granule cell responses to the brief EOD command are sufficiently varied and prolonged. Here we show that granule cells indeed provide such a temporal basis and that it is well-matched to the temporal structure of self-generated sensory inputs, allowing rapid and accurate sensory cancellation and explaining paradoxical features of negative images. We also demonstrate an unexpected and critical role of unipolar brush cells (UBCs) in generating the required delayed responses. These results provide a mechanistic account of how copies of motor commands are transformed into sensory predictions.

Regulation of interneuron excitability by gap junction coupling with principal cells

Electrical coupling of inhibitory interneurons can synchronize activity across multiple neurons, thereby enhancing the reliability of inhibition onto principal cell targets. It is unclear whether downstream activity in principal cells controls the excitability of such inhibitory networks. Using paired patch-clamp recordings, we show that excitatory projection neurons (fusiform cells) and inhibitory stellate interneurons of the dorsal cochlear nucleus form an electrically coupled network through gap junctions containing connexin36 (Cxc36, also called Gjd2). Remarkably, stellate cells were more strongly coupled to fusiform cells than to other stellate cells. This heterologous coupling was functionally asymmetric, biasing electrical transmission from the principal cell to the interneuron. Optogenetically activated populations of fusiform cells reliably enhanced interneuron excitability and generated GABAergic inhibition onto the postsynaptic targets of stellate cells, whereas deep afterhyperpolarizations following fusiform cell spike trains potently inhibited stellate cells over several hundred milliseconds. Thus, the excitability of an interneuron network is bidirectionally controlled by distinct epochs of activity in principal cells.

Learning contrast-invariant cancellation of redundant signals in neural systems

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.

Spiking neural network model for memorizing sequences with forward and backward recall

We present an oscillatory network of conductance based spiking neurons of Hodgkin-Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively.

Non-Hebbian spike-timing-dependent plasticity in cerebellar circuits

Spike-timing-dependent plasticity (STDP) provides a cellular implementation of the Hebb postulate, which states that synapses, whose activity repeatedly drives action potential firing in target cells, are potentiated. At glutamatergic synapses onto hippocampal and neocortical pyramidal cells, synaptic activation followed by spike firing in the target cell causes long-term potentiation (LTP)—as predicted by Hebb—whereas excitatory postsynaptic potentials (EPSPs) evoked after a spike elicit long-term depression (LTD)—a phenomenon that was not specifically addressed by Hebb. In both instances the action potential in the postsynaptic target neuron is an instructive signal that is capable of supporting synaptic plasticity. STDP generally relies on the propagation of Na⁺ action potentials that are initiated in the axon hillhock back into the dendrite, where they cause depolarization and boost local calcium influx. However, recent studies in CA1 hippocampal pyramidal neurons have suggested that local calcium spikes might provide a more efficient trigger for LTP induction than backpropagating action potentials. Dendritic calcium spikes also play a role in an entirely different type of STDP that can be observed in cerebellar Purkinje cells. These neurons lack backpropagating Na⁺ spikes. Instead, plasticity at parallel fiber (PF) to Purkinje cell synapses depends on the relative timing of PF-EPSPs and activation of the glutamatergic climbing fiber (CF) input that causes dendritic calcium spikes. Thus, the instructive signal in this system is externalized. Importantly when EPSPs are elicited before CF activity, PF-LTD is induced rather than LTP. Thus, STDP in the cerebellum follows a timing rule that is opposite to its hippocampal/neocortical counterparts. Regardless, a common motif in plasticity is that LTD/LTP induction depends on the relative timing of synaptic activity and regenerative dendritic spikes which are driven by the instructive signal.

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.

The cerebellum and cerebellum-like structures of cartilaginous fishes

The cerebellum is well developed in cartilaginous fishes, with the same cell types (barring basket cells) and organizational features found in other vertebrate groups, including mammals. In particular, the lattice-like organization of cerebellar cortex (with a molecular layer of parallel fibers, interneurons, spiny Purkinje cell dendrites, and climbing fibers) is a defining characteristic. In addition to the cerebellum, cartilaginous fishes have cerebellum-like structures in the dorsolateral wall of the hindbrain. These structures are adjacent to and, in part, contiguous with the cerebellum. They are cerebellum-like in that they have a molecular layer of parallel fibers and inhibitory interneurons that has striking organizational similarities to the molecular layer of the cerebellar cortex. However, these structures also have characteristics that differ from the cerebellum. For example, cerebellum-like structures do not have climbing fibers and are clearly sensory. They receive direct afferent input from peripheral sensory receptors and relay their outputs to midbrain sensory areas. As a consequence of this close sensory association and the ability of researchers to characterize signal processing in these structures in a behaviorally relevant context, good progress has been made in determining the fundamental processing algorithm of the cerebellum-like structures. This algorithm enables the molecular layer to act as an adaptive filter that cancels self-generated noise in electrosensory and lateral line systems. Given the fundamental similarities of the molecular layer across these structures and the phylogeny of these structures across basal vertebrates, it is clear that these structures share a common genetic-developmental program. Syngeny is a term that has been used to describe similarity of structure due to a shared genetic-developmental program, whether the structures are phylogenetically homologous or not. Given that the cerebellum and cerebellum-like structures are physically adjacent, we propose that cerebellum-like structures were the evolutionary antecedent of the cerebellum and that the cerebellum arose through a change in the genetic-developmental program, amounting to a duplication of existing structure. Such duplication to form adjacent structures can be considered a special case of syngeny. On this view, the cerebellum is an evolutionary innovation in gnathostomes that is literally superimposed on pre-existing underlying brain structures and pathways. From this perspective, the cerebellum can be considered an example of 'subsumption architecture', a term that describes the addition of modules that add computational power while maintaining existing fundamental functionality. This addition is reflected in the finding that in elasmobranchs with relatively large brains, the size of the telencephalon and cerebellum enlarge disproportionately, while those parts of the brain that contain more direct sensory and motor connections do not. Added 'computational' power in the chondrichthyan brain and the comparative function and evolution of the cerebellum and cerebellum-like structures across the cartilaginous fishes supports the idea of the cerebellum as an example of subsumption architecture.

Distributed synergistic plasticity and cerebellar learning

Studies on synaptic plasticity in the context of learning have been dominated by the view that a single, particular type of plasticity forms the underlying mechanism for a particular type of learning. However, emerging evidence shows that many forms of synaptic and intrinsic plasticity at different sites are induced conjunctively during procedural memory formation in the cerebellum. Here, we review the main forms of long-term plasticity in the cerebellar cortex that underlie motor learning. We propose that the different forms of plasticity in the granular layer and the molecular layer operate synergistically in a temporally and spatially distributed manner, so as to ultimately create optimal output for behaviour.

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.

Identifying self- and nonself-generated signals: lessons from electrosensory systems

This chapter provides a short review of the mechanisms used by electroreceptive fish to discriminate self- from nonself-generated signals. Electroreception is used by animals to detect objects of electric impedance different from the water, to detect natural electrogenic sources and to communicate signals between conspecifics. Electroreceptive animals may generate electric fields either with the purpose of electrically illuminating the neighborhood or as an epiphenomenon of other functions. In addition, the presence of the fish body as a conductive object in a scene funnels the current flow and, consequently, animal movements also generate signals by changing the body shape or the spatial relationship of the body with the surrounding objects. Therefore, mechanisms for discrimination between self and externally generated signals are very important for constructing a coherent representation of the environment. Some mechanisms facilitate and stream the flow of signals carried by the self-generated electric field. Others are designed to reject unwanted interference coming from self-generated movements or even the self-generated electric field. Finally, more complex operations involving sensory motor integration are used for discriminating between self- and conspecific- generated communication signals. Despite the evolutionary distance between animals endowed with electric sense, mechanisms for self-identification reappear with few differences between species. This suggests that many of the possible strategies are present in vertebrates may be found in these fish. Therefore, we have much to learn about self recognition from the study of electroreception.

Neuroecology of cartilaginous fishes: the functional implications of brain scaling

It is a widely accepted view that neural development can reflect morphological adaptations and sensory specializations. The aim of this review is to give a broad overview of the current status of brain data available for cartilaginous fishes and examine how perspectives on allometric scaling of brain size across this group of fishes has changed within the last 50 years with the addition of new data and more rigorous statistical analyses. The current knowledge of neuroanatomy in cartilaginous fishes is reviewed and data on brain size (encephalization, n = 151) and interspecific variation in brain organization (n = 84) has been explored to ascertain scaling relationships across this clade. It is determined whether similar patterns of brain organization, termed cerebrotypes, exist in species that share certain lifestyle characteristics. Clear patterns of brain organization exist across cartilaginous fishes, irrespective of phylogenetic grouping and, although this study was not a functional analysis, it provides further evidence that chondrichthyan brain structures might have developed in conjunction with specific behaviours or enhanced cognitive capabilities. Larger brains, with well-developed telencephala and large, highly foliated cerebella are reported in species that occupy complex reef or oceanic habitats, potentially identifying a reef-associated cerebrotype. In contrast, benthic and benthopelagic demersal species comprise the group with the smallest brains, with a relatively reduced telencephalon and a smooth cerebellar corpus. There is also evidence herein of a bathyal cerebrotype; deep-sea benthopelagic sharks possess relatively small brains and show a clear relative hypertrophy of the medulla oblongata. Despite the patterns observed and documented, significant gaps in the literature have been highlighted. Brain mass data are only currently available on c. 16% of all chondrichthyan species, and only 8% of species have data available on their brain organization, with far less on subsections of major brain areas that receive distinct sensory input. The interspecific variability in brain organization further stresses the importance of performing functional studies on a greater range of species. Only an expansive data set, comprised of species that span a variety of habitats and taxonomic groups, with widely disparate behavioural repertoires, combined with further functional analyses, will help shed light on the extent to which chondrichthyan brains have evolved as a consequence of behaviour, habitat and lifestyle in addition to phylogeny.

Stochastic perturbation methods for spike-timing-dependent plasticity

Online machine learning rules and many biological spike-timing-dependent plasticity (STDP) learning rules generate jump process Markov chains for the synaptic weights. We give a perturbation expansion for the dynamics that, unlike the usual approximation by a Fokker-Planck equation (FPE), is well justified. Our approach extends the related system size expansion by giving an expansion for the probability density as well as its moments. We apply the approach to two observed STDP learning rules and show that in regimes where the FPE breaks down, the new perturbation expansion agrees well with Monte Carlo simulations. The methods are also applicable to the dynamics of stochastic neural activity. Like previous ensemble analyses of STDP, we focus on equilibrium solutions, although the methods can in principle be applied to transients as well.

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.

A history of spike-timing-dependent plasticity

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today's research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm - known as spike-timing-dependent plasticity (STDP) - has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today's STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro's plasticità and Rosenblatt's perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks.

Are mushroom bodies cerebellum-like structures?

The mushroom bodies are distinctive neuropils in the protocerebral brain segments of many protostomes. A defining feature of mushroom bodies is their intrinsic neurons, masses of cytoplasm-poor globuli cells that form a system of lobes with their densely-packed, parallel-projecting axon-like processes. In insects, the role of the mushroom bodies in olfactory processing and associative learning and memory has been studied in depth, but several lines of evidence suggest that the function of these higher brain centers cannot be restricted to these roles. The present account considers whether insight into an underlying function of mushroom bodies may be provided by cerebellum-like structures in vertebrates, which are similarly defined by the presence of masses of tiny granule cells that emit thin parallel fibers forming a dense molecular layer. In vertebrates, the shared neuroarchitecture of cerebellum-like structures has been suggested to underlie a common functional role as adaptive filters for the removal of predictable sensory elements, such as those arising from reafference, from the total sensory input. Cerebellum-like structures include the vertebrate cerebellum, the electrosensory lateral line lobe, dorsal and medial octavolateral nuclei of fish, and the dorsal cochlear nucleus of mammals. The many architectural and physiological features that the insect mushroom bodies share with cerebellum-like structures suggest that it might be fruitful to consider mushroom body function in light of a possible role as adaptive sensory filters. The present account thus presents a detailed comparison of the insect mushroom bodies with vertebrate cerebellum-like structures.

Functional circuitry of a unique cerebellar specialization: the valvula cerebelli of a mormyrid fish

The valvula cerebelli of the mormyrid electric fish is a useful site for the study of cerebellar function. The valvula forms a part of the electrosensory-electromotor system of this fish, a system that offers many possibilities for the study of sensory-motor integration. The valvula also has a number of histological features not present in mammals which facilitate investigation of cerebellar circuitry and its plasticity. This initial study characterizes the basic physiology and pharmacology of cells in the valvula using an in vitro slice preparation. Intrinsic properties and synaptic responses of Purkinje cells and other cell types were examined. We found that Purkinje cells fire a small narrow Na(+) spike and a large broad Ca(2+) spike, generated in the axon initial segment and dendritic-soma region, respectively. Purkinje cells respond to parallel fiber inputs with graded excitatory postsynaptic potentials (EPSPs) and to climbing fiber inputs with all-or-none EPSPs. Efferent cells, Golgi cells, and deep stellate cells all fire a single type of large narrow spike and respond only to parallel fiber inputs. Both parallel fiber and climbing fiber responses in Purkinje cells appear to be entirely mediated by AMPA-type glutamate receptors, whereas parallel fiber responses in efferent cells and stellate cells include AMPA and NMDA components. In addition, a strong synaptic inhibition was uncovered in both Purkinje cells and efferent cells in response to the focal stimulation of parallel fibers. Dual cell recordings indicate that deep stellate cells contribute at least partially to this inhibition. We conclude that despite its unique histology, the local functional circuitry of the mormyrid valvula cerebelli is largely similar to that of the mammalian cerebellum. Thus, what is learned concerning the functioning of the mormyrid valvula cerebelli may be expected to be informative about cerebellar function in general.

Electrophysiological characteristics of cells in the anterior caudal lobe of the mormyrid cerebellum

We have examined the basic electrophysiology and pharmacology of cells in the anterior caudal lobe (CLa) of the mormyrid cerebellum. Intracellular recordings were performed in an in vitro slice preparation using the whole-cell patch recording method. The responses of cells to parallel fiber (PF) and climbing fiber (CF) stimulation and to somatic current injection were recorded, and then characterized by bath application of receptor and ion channel blockers. Using biocytin or neurobiotin, these cells were also morphologically identified after recording to ensure their classification. Efferent cells and two subtypes of Purkinje cells were identified on the basis of their physiology and morphology. While the majority of Purkinje cells fire a single type of spike that is mediated by Na(+), some fire a large broad spike mediated by Ca(2+) and a narrow spike mediated by Na(+) at resting potential levels. By patching one recording electrode to the soma and another to one of the proximal dendrites of the same cell simultaneously, it was found that the Na(+) spike has an axonal origin and the Ca(2+) spike is generated in the soma-dendritic region of Purkinje cells. Efferent cells fire a single type of Na(+) spike only. Despite variations in their physiology and morphology, all cell types responded to PF stimulation with graded excitatory postsynaptic potentials (EPSPs) mediated by AMPA receptors. However, none of the efferent cells and only some of the Purkinje cells responded to CF activation with a large, AMPA receptor-mediated all-or-none EPSPs. We conclude that the functional circuitry of the CLa resembles that of other regions of the mormyrid cerebellum and is largely similar to that of the mammalian cerebellum.

The importance of N-methyl-d-aspartate (NMDA) receptors in subtraction of electrosensory reafference in the dorsal nucleus of skates

The dorsal nucleus of the little skate is a cerebellum-like sensory structure that adaptively filters out predictable electrosensory inputs. The filter's plasticity is mediated by anti-Hebbian associative depression at the synapses between parallel fibers and ascending efferent neurons (AENs). Changes in synaptic strength are indicated by the formation of a cancellation signal which is initiated by co-activation of parallel fibers and AENs, and can be reversed by parallel fiber activity in the absence of AEN activation. In other cerebellum-like sensory structures, the formation of the cancellation signal requires activation of postsynaptic NMDA receptors on the principal neurons. We demonstrate here by immunohistochemistry that the somas and the initial portion of both apical and basal dendrites of the AENs are labeled with antibodies raised against the NR1 subunit of NMDA receptors from a South American electric fish. In in vivo physiological experiments, we show that the formation of the cancellation signal induced by coupling an electrosensory stimulus to ventilatory movements or direct parallel fiber stimulation is blocked when either of the NMDA receptor antagonists 2-amino-5-phosphonovaleric acid (APV) or MK801 is injected into the molecular layer above the recorded AEN. Blocking NMDA receptors prevented formation of a cancellation signal in 79% (15/19; APV) and 60% (3/5; MK801) of the AENs. This blockage was reversible in 40% (6/15) of the AENs after APV removal. Thus, in the dorsal nucleus, the activity-dependent, long-lasting but reversible change in synaptic strength of the parallel fiber–AEN synapses appears to be an NMDA receptor-dependent process.

Multimodal integration in granule cells as a basis for associative plasticity and sensory prediction in a cerebellum-like circuit

The recoding of diverse sensory and motor signals by granule cells (GCs) is probably critical for the function of cerebellar circuits, yet the nature of these transformations and their significance for cerebellar information processing remain poorly understood. In cerebellum-like structures in fish, anti-Hebbian plasticity at parallel fiber synapses generates "negative images" that act to cancel predictable patterns of electrosensory input. Here I test the hypothesis that GCs enhance the capacity of Purkinje-like cells to generate specific negative images by selectively encoding combinations of sensory and motor signals. Using in vivo whole-cell recordings, I show (1) that a subset of GCs integrate sensory and motor signals conveyed by distinct mossy fiber classes and (2) that Purkinje-like cells exhibit plastic changes specific to the combinations of signals that individual GCs encode. Consistent with influential theories of cerebellar function, these findings suggest that selective GC output enhances the capacity of Purkinje-like cells to acquire selectivity through associative plasticity.

Burst-induced anti-Hebbian depression acts through short-term synaptic dynamics to cancel redundant sensory signals

Weakly electric fish can enhance the detection and localization of important signals such as those of prey in part by cancellation of redundant spatially diffuse electric signals due to, e.g., their tail bending. The cancellation mechanism is based on descending input, conveyed by parallel fibers emanating from cerebellar granule cells, that produces a negative image of the global low-frequency signals in pyramidal cells within the first-order electrosensory region, the electrosensory lateral line lobe (ELL). Here we demonstrate that the parallel fiber synaptic input to ELL pyramidal cell undergoes long-term depression (LTD) whenever both parallel fiber afferents and their target cells are stimulated to produce paired burst discharges. Paired large bursts (4-4) induce robust LTD over pre-post delays of up to +/-50 ms, whereas smaller bursts (2-2) induce weaker LTD. Single spikes (either presynaptic or postsynaptic) paired with bursts did not induce LTD. Tetanic presynaptic stimulation was also ineffective in inducing LTD. Thus, we have demonstrated a form of anti-Hebbian LTD that depends on the temporal correlation of burst discharge. We then demonstrated that the burst-induced LTD is postsynaptic and requires the NR2B subunit of the NMDA receptor, elevation of postsynaptic Ca(2+), and activation of CaMKIIbeta. A model incorporating local inhibitory circuitry and previously identified short-term presynaptic potentiation of the parallel fiber synapses further suggests that the combination of burst-induced LTD, presynaptic potentiation, and local inhibition may be sufficient to explain the generation of the negative image and cancellation of redundant sensory input by ELL pyramidal cells.