Storage of a sensory pattern by anti-Hebbian synaptic plasticity in an electric fish

Packet information encoding in a cerebellum-like circuit

Packet information encoding of neural signals was proposed for vision about 50 years ago and has recently been revived as a plausible strategy generalizable to natural and artificial sensory systems. It involves discrete image segmentation controlled by feedback and the ability to store and compare packets of information. This article shows that neurons of the cerebellum-like electrosensory lobe (EL) of the electric fish Gymnotus omarorum use spike-count and spike-timing distribution as constitutive variables of packets of information that encode one-by-one the electrosensory images generated by a self-timed series of electric organ discharges (EODs). To evaluate this hypothesis, extracellular unitary activity was recorded from the centro-medial map of the EL. Units recorded in high-decerebrate preparations were classified into six types using hierarchical cluster analysis of post-EOD spiking histograms. Cross-correlation analysis indicated that each EOD strongly influences the unit firing probability within the next inter-EOD interval. Units of the same type were similarly located in the laminar organization of the EL and showed similar stimulus-specific changes in spike count and spike timing after the EOD when a metal object was moved close by, along the fish's body parallel to the skin, or when the longitudinal impedance of a static cylindrical probe placed at the center of the receptive field was incremented in a stepwise manner in repetitive trials. These last experiments showed that spike-counts and the relative entropy, expressing a comparative measure of information before and after the step, were systematically increased with respect to a control in all unit types. The post-EOD spike-timing probability distribution and the relatively independent contribution of spike-timing and number to the content of information in the transmitted packet suggest that these are the constitutive image-encoding variables of the packets. Comparative analysis suggests that packet information transmission is a general principle for processing superposition images in cerebellum-like networks.

Living life with an electric touch

The electric organ discharges (EODs) produced by weakly electric fish have long been a source of scientific intrigue and inspiration. The study of these species has contributed to our understanding of the organization of fixed action patterns, as well as enriching general imaging theory by unveiling the dual impact of an agent's actions on the environment and its own sensory system during the imaging process. This Centenary Review firstly compares how weakly electric fish generate species- and sex-specific stereotyped electric fields by considering: (1) peripheral mechanisms, including the geometry, channel repertoire and innervation of the electrogenic units; (2) the organization of the electric organs (EOs); and (3) neural coordination mechanisms. Secondly, the Review discusses the threefold function of the fish-centered electric fields: (1) to generate electric signals that encode the material, geometry and distance of nearby objects, serving as a short-range sensory modality or 'electric touch'; (2) to mark emitter identity and location; and (3) to convey social messages encoded in stereotypical modulations of the electric field that might be considered as species-specific communication symbols. Finally, this Review considers a range of potential research directions that are likely to be productive in the future.

Hormonal coordination of motor output and internal prediction of sensory consequences in an electric fish

Steroid hormones remodel neural networks to induce seasonal or developmental changes in behavior. Hormonal changes in behavior likely require coordinated changes in sensorimotor integration. Here, we investigate hormonal effects on a predictive motor signal, termed corollary discharge, that modulates sensory processing in weakly electric mormyrid fish. In the electrosensory pathway mediating communication behavior, inhibition activated by a corollary discharge blocks sensory responses to self-generated electric pulses, allowing the downstream circuit to selectively analyze communication signals from nearby fish. These pulses are elongated by increasing testosterone levels in males during the breeding season. We induced electric-pulse elongation using testosterone treatment and found that the timing of electroreceptor responses to self-generated pulses was delayed as electric-pulse duration increased. Simultaneous recordings from an electrosensory nucleus and electromotor neurons revealed that the timing of corollary discharge inhibition was delayed and elongated by testosterone. Furthermore, this shift in the timing of corollary discharge inhibition was precisely matched to the shift in timing of receptor responses to self-generated pulses. We then asked whether the shift in inhibition timing was caused by direct action of testosterone on the corollary discharge circuit or by plasticity acting on the circuit in response to altered sensory feedback. We surgically silenced the electric organ of fish and found similar hormonal modulation of corollary discharge timing between intact and silent fish, suggesting that sensory feedback was not required for this shift. Our findings demonstrate that testosterone directly regulates motor output and internal prediction of the resulting sensory consequences in a coordinated manner.

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.

A simple model of the electrosensory electromotor loop in Gymnotus omarorum

This article introduces and tests a simple model that describes a neural network found in nature, the electrosensory control of an electromotor pacemaker. The cornerstone of the model is an early-stage filter based on the subtraction of a feedforward integrated version of the recent sensory past from the present input signal. The output of this filter governs the modulation of a premotor pacemaker command driving the sensory signal carrier generation and, in consequence, the timing of subsequent electrosensory input. This early filter has a biological parallel in the known connectivity of the first electrosensory relay within the brain stem of the weakly electric fish Gymnotus omarorum. Our biomimetic model of this active, perception-driven action-sensation cycle was contrasted with previously published and here provided new data. When the amplitude of the electrosensory input was manipulated to mimic previous experiments on the novelty detection characteristics, the model reproduces them rather faithfully. In addition, when we applied continuous variations to the input it shows that increases in stimulus amplitudes are followed by increases in the EOD rate, but decreases do not cause rate modulation suggesting a rectification in some stage of the loop. These behavioral experiments confirmed results generated the simulations suggesting that beyond explaining the novelty detection process this simple model is a good description of the electrosensory -electromotor loop in pulse weakly electric fish.

Getting the news in milliseconds: The role of early novelty detection in active electrosensory exploration

The pulse emitting weakly electric fish Gymnotus omarorum shows stereotyped "novelty responses" consisting of a transient acceleration of the rhythm of a self-emitted electric organ discharge that carries electrosensory signals. Here we show that rapid increases in electric image amplitude cause a "novelty detection potential" in the first electrosensory relay. This sign precedes and its amplitude predicts, the amplitude of the subsequent behavioral novelty response. Current source density analyses indicates its origin ar the layers of the electrosensory lobe where the main output neurons occur. Two types of units, referred to as "ON" and "OFF". Were recorded there in decerebrated fish. Firing probability of "OFF" units drastically decreased after a stepwise increase in electric image. By contrast, the very first novel stimuli after the increase evoked a sharp peak in firing rate of "ON" units followed by a very fast adaptation phase that contrasted with the slow adaptation observed in previous recordings of primary afferents. The amplitudes of this peak, the novelty detection potential, and the behavioral novelty responses, show the same dependence on the departure of the newest stimulus intensity from the weighted average of preceding ones suggesting that the signals encoded by "ON" neurons underlay the novelty detection potential, propagates through the hierarchical organization of the electromotor control, and finally contribute to accelerate the electric organ discharge rate. This suggests that detecting novelty at the very early processing stage of electrosensory signals is essential to adapt the electrosensory sampling rate to exploration requirements as they change dynamically.

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.

Precise movement-based predictions in the mouse auditory cortex

Many of the sensations experienced by an organism are caused by their own actions, and accurately anticipating both the sensory features and timing of self-generated stimuli is crucial to a variety of behaviors. In the auditory cortex, neural responses to self-generated sounds exhibit frequency-specific suppression, suggesting that movement-based predictions may be implemented early in sensory processing. However, it remains unknown whether this modulation results from a behaviorally specific and temporally precise prediction, nor is it known whether corresponding expectation signals are present locally in the auditory cortex. To address these questions, we trained mice to expect the precise acoustic outcome of a forelimb movement using a closed-loop sound-generating lever. Dense neuronal recordings in the auditory cortex revealed suppression of responses to self-generated sounds that was specific to the expected acoustic features, to a precise position within the movement, and to the movement that was coupled to sound during training. Prediction-based suppression was concentrated in L2/3 and L5, where deviations from expectation also recruited a population of prediction-error neurons that was otherwise unresponsive. Recording in the absence of sound revealed abundant movement signals in deep layers that were biased toward neurons tuned to the expected sound, as well as expectation signals that were present throughout the cortex and peaked at the time of expected auditory feedback. Together, these findings identify distinct populations of auditory cortical neurons with movement, expectation, and error signals consistent with a learned internal model linking an action to its specific acoustic outcome.

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

Multiple behavior-specific cancellation signals contribute to suppressing predictable sensory reafference in a cerebellum-like structure

Movement induces sensory stimulation of an animal's own sensory receptors, termed reafference. With a few exceptions, notably vestibular and proprioception, this reafference is unwanted sensory noise and must be selectively filtered in order to detect relevant external sensory signals. In the cerebellum-like electrosensory nucleus of elasmobranch fish, an adaptive filter preserves novel signals by generating cancellation signals that suppress predictable reafference. A parallel fiber network supplies the principal Purkinje-like neurons (called ascending efferent neurons, AENs) with behavior-associated internal reference signals, including motor corollary discharge and sensory feedback, from which predictive cancellation signals are formed. How distinct behavior-specific cancellation signals interact within AENs when multiple behaviors co-occur and produce complex, changing patterns of reafference is unknown. Here, we show that when multiple streams of internal reference signals are available, cancellation signals form that are specific to parallel fiber inputs temporally correlated with, and therefore predictive of, sensory reafference. A single AEN has the capacity to form more than one cancellation signal, and AENs form multiple cancellation signals simultaneously and modify them independently during co-occurring behaviors. Cancellation signals update incrementally during continuous behaviors, as well as episodic bouts of behavior that last minutes to hours. Finally, individual AENs, independently of their neighbors, form unique AEN-specific cancellation signals that depend on the particular sensory reafferent input it receives. Together, these results demonstrate the capacity of the adaptive filter to utilize multiple cancellation signals to suppress dynamic patterns of reafference arising from complex co-occurring and intermittently performed behaviors.

A History of Corollary Discharge: Contributions of Mormyrid Weakly Electric Fish

Corollary discharge is an important brain function that allows animals to distinguish external from self-generated signals, which is critical to sensorimotor coordination. Since discovery of the concept of corollary discharge in 1950, neuroscientists have sought to elucidate underlying neural circuits and mechanisms. Here, we review a history of neurophysiological studies on corollary discharge and highlight significant contributions from studies using African mormyrid weakly electric fish. Mormyrid fish generate brief electric pulses to communicate with other fish and to sense their surroundings. In addition, mormyrids can passively locate weak, external electric signals. These three behaviors are mediated by different corollary discharge functions including inhibition, enhancement, and predictive “negative image” generation. Owing to several experimental advantages of mormyrids, investigations of these mechanisms have led to important general principles that have proven applicable to a wide diversity of animal species.

A cerebellum-like circuit in the lateral line system of fish cancels mechanosensory input associated with its own movements

An animal's own movement exerts a profound impact on sensory input to its nervous system. Peripheral sensory receptors do not distinguish externally generated stimuli from stimuli generated by an animal's own behavior (reafference) - although the animal often must. One way that nervous systems can solve this problem is to provide movement-related signals (copies of motor commands and sensory feedback) to sensory systems, which can then be used to generate predictions that oppose or cancel out sensory responses to reafference. Here, we studied the use of movement-related signals to generate sensory predictions in the lateral line medial octavolateralis nucleus (MON) of the little skate. In the MON, mechanoreceptive afferents synapse on output neurons that also receive movement-related signals from central sources, via a granule cell parallel fiber system. This parallel fiber system organization is characteristic of a set of so-called cerebellum-like structures. Cerebellum-like structures have been shown to support predictive cancellation of reafference in the electrosensory systems of fish and the auditory system of mice. Here, we provide evidence that the parallel fiber system in the MON can generate predictions that are negative images of (and therefore cancel) sensory input associated with respiratory and fin movements. The MON, found in most aquatic vertebrates, is probably one of the most primitive cerebellum-like structures and a starting point for cerebellar evolution. The results of this study contribute to a growing body of work that uses an evolutionary perspective on the vertebrate cerebellum to understand its functional diversity in animal behavior.

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.

Classes of dendritic information processing

Dendrites are much more than passive neuronal components. Mounting experimental evidence and decades of computational work have decisively shown that dendrites leverage a host of nonlinear biophysical phenomena and actively participate in sophisticated computations, at the level of the single neuron and at the level of the network. However, a coherent view of their processing power is still lacking and dendrites are largely neglected in neural network models. Here, we describe four classes of dendritic information processing and delineate their implications at the algorithmic level. We propose that beyond the well-known spatiotemporal filtering of their inputs, dendrites are capable of selecting, routing and multiplexing information. By separating dendritic processing from axonal outputs, neuron networks gain a degree of freedom with implications for perception and learning.

Learning to generalize

Electric fish are able to take what they have learnt about sensory processing in certain situations and apply it in other situations.

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.

Current-Efficient Preamplifier Architecture for CMRR Sensitive Neural Recording Applications

There are neural recording applications in which the amplitude of common-mode interfering signals is several orders of magnitude higher than the amplitude of the signals of interest. This challenging situation for neural amplifiers occurs, among other applications, in neural recordings of weakly electric fish or nerve activity recordings made with cuff electrodes. This paper reports an integrated neural amplifier architecture targeting in-vivo recording of local field potentials and unitary signals from the brain stem of a weakly electric fish Gymnotus omarorum. The proposed architecture offers low noise, high common-mode rejection ratio (CMRR), current-efficiency, and a high-pass frequency fixed without MOS pseudoresistors. The main contributions of this work are the overall architecture coupled with an efficient and simple single-stage circuit for the amplifier main transconductor, and the ability of the amplifier to acquire biopotential signals from high-amplitude common-mode interference in an unshielded environment. A fully-integrated neural preamplifier, which performs well in line with the state-of-the-art of the field while providing enhanced CMRR performance, was fabricated in a 0.5  m CMOS process. Results from measurements show that the gain is 49.5 dB, the bandwidth ranges from 13 Hz to 9.8 kHz, the equivalent input noise is 1.88  V, the CMRR is 87 dB and the Noise Efficiency Factor is 2.1. In addition, in-vivo recordings of weakly electric fish neural activity performed by the proposed amplifier are introduced and favorably compared with those of a commercial laboratory instrumentation system.

The bioinspiring potential of weakly electric fish

Electric fish are privileged animals for bio-inspiring man-built autonomous systems since they have a multimodal sense that allows underwater navigation, object classification and intraspecific communication. Although there are taxon dependent variations adapted to different environments, this multimodal system can be schematically described as having four main components: active electroreception, passive electroreception, lateral line sense and, proprioception. Amongst these sensory modalities, proprioception and electroreception show 'active' systems that extrct information carried by self generated forms of energy. This ensemble of four sensory modalities is present in African mormyriformes and American gymnotiformes. The convergent evolution of similar imaging, peripheral encoding, and central processing mechanisms suggests that these mechanisms may be the most suitable for dealing with electric images in the context of the other and self generated actions. This review deals with the way in which biological organisms address three of the problems that are faced when designing a bioinspired electroreceptive agent: (a) body shape, material and mobility, (b) peripheral encoding of electric images, and (c) early processing of electrosensory signals. Taking into account biological solutions I propose that the new generation of underwater agents should have electroreceptive arms, use complex peripheral sensors for encoding the images and cerebellum like architecture for image feature extraction and implementing sensory-motor transformations.

Neural plasticity and network remodeling: From concepts to pathology

Neuroplasticity has been subject to a great deal of research in the last century. Recently, significant emphasis has been placed on the global effect of localized plastic changes throughout the central nervous system, and on how these changes integrate in a pathological context. Specifically, alterations of network functionality have been described in various pathological contexts to which corresponding structural alterations have been proposed. However, considering the amount of literature and the different pathological contexts, an integration of this information is still lacking. In this paper we will review the concepts of neural plasticity as well as their repercussions on network remodeling and provide a possible explanation to how these two concepts relate to each other. We will further examine how alterations in different pathological contexts may relate to each other and will discuss the concept of plasticity diseases, its models and implications.

Electric organ discharges and near-field spatiotemporal patterns of the electromotive force in a sympatric assemblage of Neotropical electric knifefish

Descriptions of the head-to-tail electric organ discharge (ht-EOD) waveform - typically recorded with electrodes at a distance of approximately 1-2 body lengths from the center of the subject - have traditionally been used to characterize species diversity in gymnotiform electric fish. However, even taxa with relatively simple ht-EODs show spatiotemporally complex fields near the body surface that are determined by site-specific electrogenic properties of the electric organ and electric filtering properties of adjacent tissues and skin. In Brachyhypopomus, a pulse-discharging genus in the family Hypopomidae, the regional characteristics of the electric organ and the role that the complex 'near field' plays in communication and/or electrolocation are not well known. Here we describe, compare, and discuss the functional significance of diversity in the ht-EOD waveforms and near-field spatiotemporal patterns of the electromotive force (emf-EODs) among a species-rich sympatric community of Brachyhypopomus from the upper Amazon.

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.

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.

Sensory processing and corollary discharge effects in posterior caudal lobe Purkinje cells in a weakly electric mormyrid fish

Although it has been suggested that the cerebellum functions to predict the sensory consequences of motor commands, how such predictions are implemented in cerebellar circuitry remains largely unknown. A detailed and relatively complete account of predictive mechanisms has emerged from studies of cerebellum-like sensory structures in fish, suggesting that comparisons of the cerebellum and cerebellum-like structures may be useful. Here we characterize electrophysiological response properties of Purkinje cells in a region of the cerebellum proper of weakly electric mormyrid fish, the posterior caudal lobe (LCp), which receives the same mossy fiber inputs and projects to the same target structures as the electrosensory lobe (ELL), a well-studied cerebellum-like structure. We describe patterns of simple spike and climbing fiber activation in LCp Purkinje cells in response to motor corollary discharge, electrosensory, and proprioceptive inputs and provide evidence for two functionally distinct Purkinje cell subtypes within LCp. Protocols that induce rapid associative plasticity in ELL fail to induce plasticity in LCp, suggesting differences in the adaptive functions of the two structures. Similarities and differences between LCp and ELL are discussed in light of these results.

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.

A biophysical model of adaptive noise filtering in the shark brain

Sharks detect their prey using an extremely sensitive electrosensory system that is capable of distinguishing weak external stimuli from a relatively strong background noise generated by the animal itself. Experiments indicate that part of the shark's hindbrain, the dorsal octavolateralis nucleus (DON), is responsible for extracting the external stimulus using an adaptive filter mechanism to suppress signals correlated with the shark's breathing motion. The DON's principal neuron integrates input from afferents as well as many thousands of parallel fibres transmitting, inter alia, breathing-correlated motor command signals. There are a number of models in the literature, studying how this adaptive filtering mechanisms occurs, but most of them are based on a spike-train model approach.This paper presents a biophysically based computational simulation which demonstrates a mechanism for adaptive noise filtering in the DON. A spatial model of the neuron uses the Hodgkin-Huxley equations to simulate the propagation of action potentials along the dendrites. Synaptic inputs are modelled by applied currents at various positions along the dendrites, whose input conductances are varied according to a simple learning rule.Simulation results show that the model is able to demonstrate adaptive filtering in agreement with previous experimental and modelling studies. Furthermore, the spatial nature of the model does not greatly affect its learning properties, and in its present form is effectively equivalent to an isopotential model which does not incorporate a spatial element.

From the intrinsic properties to the functional role of a neuron phenotype: an example from electric fish during signal trade-off

This review deals with the question: what is the relationship between the properties of a neuron and the role that the neuron plays within a given neural circuit? Answering this kind of question requires collecting evidence from multiple neuron phenotypes and comparing the role of each type in circuits that perform well-defined computational tasks. The focus here is on the spherical neurons in the electrosensory lobe of the electric fish Gymnotus omarorum. They belong to the one-spike-onset phenotype expressed at the early stages of signal processing in various sensory modalities and diverse taxa. First, we refer to the one-spike neuron intrinsic properties, their foundation on a low-threshold K(+) conductance, and the potential roles of this phenotype in different circuits within a comparative framework. Second, we present a brief description of the active electric sense of weakly electric fish and the particularities of spherical one-spike-onset neurons in the electrosensory lobe of G. omarorum. Third, we introduce one of the specific tasks in which these neurons are involved: the trade-off between self- and allo-generated signals. Fourth, we discuss recent evidence indicating a still-undescribed role for the one-spike phenotype. This role deals with the blockage of the pathway after being activated by the self-generated electric organ discharge and how this blockage favors self-generated electrosensory information in the context of allo-generated interference. Based on comparative analysis we conclude that one-spike-onset neurons may play several functional roles in animal sensory behavior. There are specific adaptations of the neuron's 'response function' to the circuit and task. Conversely, the way in which a task is accomplished depends on the intrinsic properties of the neurons involved. In short, the role of a neuron within a circuit depends on the neuron and its functional context.

Learning multisensory integration and coordinate transformation via density estimation

Sensory processing in the brain includes three key operations: multisensory integration-the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations-the change of reference frame for a stimulus (e.g., retinotopic to body-centered) effected through knowledge about an intervening variable (e.g., gaze position); and the incorporation of prior information. Statistically optimal sensory processing requires that each of these operations maintains the correct posterior distribution over the stimulus. Elements of this optimality have been demonstrated in many behavioral contexts in humans and other animals, suggesting that the neural computations are indeed optimal. That the relationships between sensory modalities are complex and plastic further suggests that these computations are learned-but how? We provide a principled answer, by treating the acquisition of these mappings as a case of density estimation, a well-studied problem in machine learning and statistics, in which the distribution of observed data is modeled in terms of a set of fixed parameters and a set of latent variables. In our case, the observed data are unisensory-population activities, the fixed parameters are synaptic connections, and the latent variables are multisensory-population activities. In particular, we train a restricted Boltzmann machine with the biologically plausible contrastive-divergence rule to learn a range of neural computations not previously demonstrated under a single approach: optimal integration; encoding of priors; hierarchical integration of cues; learning when not to integrate; and coordinate transformation. The model makes testable predictions about the nature of multisensory representations.

Reversed timing-dependent associative plasticity in the human brain through interhemispheric interactions

Spike timing-dependent plasticity (STDP) has been proposed as one of the key mechanisms underlying learning and memory. Repetitive median nerve stimulation, followed by transcranial magnetic stimulation (TMS) of the contralateral primary motor cortex (M1), defined as paired-associative stimulation (PAS), has been used as an in vivo model of STDP in humans. PAS-induced excitability changes in M1 have been repeatedly shown to be time-dependent in a STDP-like fashion, since synchronous arrival of inputs within M1 induces long-term potentiation-like effects, whereas an asynchronous arrival induces long-term depression (LTD)-like effects. Here, we show that interhemispheric inhibition of the sensorimotor network during PAS, with the peripheral stimulation over the hand ipsilateral to the motor cortex receiving TMS, results in a LTD-like effect, as opposed to the standard STDP-like effect seen for contralateral PAS. Furthermore, we could show that this reversed-associative plasticity critically depends on the timing interval between afferent and cortical stimulation. These results indicate that the outcome of associative stimulation in the human brain depends on functional network interactions (inhibition or facilitation) at a systems level and can either follow standard or reversed STDP-like mechanisms.

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.

Figure-ground separation during active electrolocation in the weakly electric fish, Gnathonemus petersii

The weakly electric fish Gnathonemus petersii uses active electrolocation to detect and discriminate between objects in its environment. Objects are recognised by analysing the electric images, which they project onto the fish's skin. In this study, we determined whether different types of large backgrounds interfere with the fishes' ability to discriminate between objects. Fish were trained in a food-rewarded two-alternative forced-choice procedure to discriminate between two objects. In subsequent tests, structured and non-structured as well as stationary and moving backgrounds were positioned behind the objects and discrimination performance between objects was measured at different object distances. To define the electrosensory stimuli during the tests, the electric images of the objects and backgrounds used were measured. Without a background G. petersii was able to discriminate between objects up to distances of about 3-4 cm. Even though the electric images of background and object superimposed in a complex way, the addition of stationary structured or plain backgrounds had only minor effects on the range of object discrimination. However, two types of moving backgrounds improved electrolocation by extending the range of object discrimination up to a distance of almost 5 cm. This suggests that movements in the environment plays an important role for object identification and improves figure-ground separation during active electrolocation.

Timing actions to avoid refractoriness: a simple solution for streaming sensory signals

Segmenting self- from allo-generated signals is crucial for active sensory processing. We report a dynamic filter used by South American pulse electric fish to distinguish active electro-sensory signals carried by their own electric discharges from other concomitant electrical stimuli (i.e. communication signals). The filter has a sensory component, consisting of an onset type central electro-sensory neuron, and a motor component, consisting of a change in the fish's discharge rate when allo-generated electrical events occur in temporal proximity to the fish's own discharge. We investigated the sensory component of the filter by in vitro mimicking synaptic inputs occurring during behavioral responses to allo-generated interfering signals. We found that active control of the discharge enhances self-generated over allo-generated responses by forcing allo-generated signals into a central refractory period. This hypothesis was confirmed by field potential recordings in freely discharging fish. Similar sensory-motor mechanisms may also contribute to signal segmentation in other sensory systems.

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.

Anti-hebbian spike-timing-dependent plasticity and adaptive sensory processing

Adaptive sensory processing influences the central nervous system's interpretation of incoming sensory information. One of the functions of this adaptive sensory processing is to allow the nervous system to ignore predictable sensory information so that it may focus on important novel information needed to improve performance of specific tasks. The mechanism of spike-timing-dependent plasticity (STDP) has proven to be intriguing in this context because of its dual role in long-term memory and ongoing adaptation to maintain optimal tuning of neural responses. Some of the clearest links between STDP and adaptive sensory processing have come from in vitro, in vivo, and modeling studies of the electrosensory systems of weakly electric fish. Plasticity in these systems is anti-Hebbian, so that presynaptic inputs that repeatedly precede, and possibly could contribute to, a postsynaptic neuron's firing are weakened. The learning dynamics of anti-Hebbian STDP learning rules are stable if the timing relations obey strict constraints. The stability of these learning rules leads to clear predictions of how functional consequences can arise from the detailed structure of the plasticity. Here we review the connection between theoretical predictions and functional consequences of anti-Hebbian STDP, focusing on adaptive processing in the electrosensory system of weakly electric fish. After introducing electrosensory adaptive processing and the dynamics of anti-Hebbian STDP learning rules, we address issues of predictive sensory cancelation and novelty detection, descending control of plasticity, synaptic scaling, and optimal sensory tuning. We conclude with examples in other systems where these principles may apply.

Imaging in electrosensory systems

This review addresses the biophysical mechanisms of image formation in electrosensory systems. These electrical images are used for navigation and object detection by many species of fish, some amphibians, and some mammals. In the active electrosensory systems of fish these images are formed by the fish's own electric organ discharge. In the passive electrosensory systems of fish, amphibians and mammals the images are formed by external electrical sources. In this review we describe the biophysics of image formation, the effects of the organism's passive electrical properties, the role of exploration, and the influence of context on electroreception. We suggest that the basic principles established in these specialized systems be useful for understanding other more common sensory systems.

Spike-timing-dependent plasticity in the intact brain: counteracting spurious spike coincidences

A computationally rich algorithm of synaptic plasticity has been proposed based on the experimental observation that the sign and amplitude of the change in synaptic weight is dictated by the temporal order and temporal contiguity between pre- and postsynaptic activities. For more than a decade, this spike-timing-dependent plasticity (STDP) has been studied mainly in brain slices of different brain structures and cultured neurons. Although not yet compelling, evidences for the STDP rule in the intact brain, including primary sensory cortices, have been provided lastly. From insects to mammals, the presentation of precisely timed sensory inputs drives synaptic and functional plasticity in the intact central nervous system, with similar timing requirements than the in vitro defined STDP rule. The convergent evolution of this plasticity rule in species belonging to so distant phylogenic groups points to the efficiency of STDP, as a mechanism for modifying synaptic weights, as the basis of activity-dependent development, learning and memory. In spite of the ubiquity of STDP phenomena, a number of significant variations of the rule are observed in different structures, neuronal types and even synapses on the same neuron, as well as between in vitro and in vivo conditions. In addition, the state of the neuronal network, its ongoing activity and the activation of ascending neuromodulatory systems in different behavioral conditions have dramatic consequences on the expression of spike-timing-dependent synaptic plasticity, and should be further explored.

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.

Evolution of time-coding systems in weakly electric fishes

Weakly electric fishes emit electric organ discharges (EODs) from their tail electric organs and sense feedback signals from their EODs by electroreceptors in the skin. The electric sense is utilized for various behaviors, including electrolocation, electrocommunication, and the Jamming avoidance response (JAR). For each behavior, various types of sensory Information are embedded in the transient electrical signals produced by the fish. These temporal signals are sampled, encoded, and further processed by peripheral and central neurons specialized for time coding. There are time codes for the sex or species Identities of other fish or the resistance and capacitance of objects. In the central nervous system, specialized neural elements exist for decoding time codes for different behavioral functions. Comparative studies allow phylogenetic comparison of time-coding neural systems among weakly electric fishes.

Plasticity in a cerebellar-like structure: suppressing reafference during episodic behaviors

Detection of relevant sensory signals requires the filtering out of irrelevant noise, including noise created by the animal's own movements(reafference). This is accomplished in the electrosense of little skates(Raja erinacea) by an adaptive filter in the cerebellar-like electrosensory nucleus (dorsal nucleus) in the medulla. We have shown that electrosensory inputs reliably coupled to the regularly recurring movements of breathing over time are eliminated selectively in the principal neurons(ascending efferent neurons, AENs) by a cancellation signal that is a negative of the reafference and is supplied by a parallel fiber system. Similarly,electrosensory inputs repeatedly linked to passive fin movements are eliminated suggesting that the filter also functions in relation to other behaviors besides breathing. To determine whether this adaptive filter can eliminate reafference created by brief and infrequent episodic behaviors like swimming in skates, we initiated a series of coupling tests in which an external electrosensory stimulus was coupled to short bouts of either parallel fiber stimulation or passive fin movements, and then measured the ability of AENs to generate a cancellation signal. Following five brief coupling periods(30–60 s) separated by long rest periods (1–9 min), 38.5% of the AENs developed a cancellation signal when the coupling was to parallel fiber stimulation, and 73% when the coupling was to passive fin movement. We demonstrate that the cancellation signals can be developed incrementally,persist for at least a 3 h rest period without reinforcement, and are extinguished within minutes when the association of sensory stimulus and fin movement or parallel fiber stimulation no longer exists. The results indicate that the adaptive filter has the properties necessary to cancel reafference associated with even brief and infrequent behaviors.

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.

Active electroreception in Gymnotus omari: imaging, object discrimination, and early processing of actively generated signals

Weakly electric fishes "electrically illuminate" the environment in two forms: pulse fishes emit a succession of discrete electric discharges while wave fishes emit a continuous wave. These strategies are present in both taxonomic groups of weakly electric fishes, mormyrids and gymnotids. As a consequence one can distinguish four major types of active electrosensory strategies evolving in parallel. Pulse gymnotids have an electrolocating strategy common with pulse mormyrids, but brains of pulse and wave gymnotids are alike. The beating strategy associated to other differences in the electrogenic system and electrosensory responses suggests that similar hardware might work in a different mode for processing actively generated electrosensory images. In this review we summarize our findings in pulse gymnotids' active electroreception and outline a primary agenda for the next research.

Towards a general theory of neural computation based on prediction by single neurons

Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information (“prediction error” or “surprise”). A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most “new” information about future reward. To minimize the error in its predictions and to respond only when excitation is “new and surprising,” the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of undifferentiated neurons, each implementing similar learning algorithms.

Dendritic excitability and synaptic plasticity

Most synaptic inputs are made onto the dendritic tree. Recent work has shown that dendrites play an active role in transforming synaptic input into neuronal output and in defining the relationships between active synapses. In this review, we discuss how these dendritic properties influence the rules governing the induction of synaptic plasticity. We argue that the location of synapses in the dendritic tree, and the type of dendritic excitability associated with each synapse, play decisive roles in determining the plastic properties of that synapse. Furthermore, since the electrical properties of the dendritic tree are not static, but can be altered by neuromodulators and by synaptic activity itself, we discuss how learning rules may be dynamically shaped by tuning dendritic function. We conclude by describing how this reciprocal relationship between plasticity of dendritic excitability and synaptic plasticity has changed our view of information processing and memory storage in neuronal networks.

Design principles of sensory processing in cerebellum-like structures. Early stage processing of electrosensory and auditory objects

Cerebellum-like structures are compared for two sensory systems: electrosensory and auditory. The electrosensory lateral line lobe of mormyrid electric fish is reviewed and the neural representation of electrosensory objects in this structure is modeled and discussed. The dorsal cochlear nucleus in the auditory brainstem of mammals is reviewed and new data are presented that characterize the responses of neurons in this structure in the mouse. Similarities between the electrosensory and auditory cerebellum-like structures are shown, in particular adaptive processes that may reduce responses to predictable stimuli. We suggest that the differences in the types of sensory objects may drive the differences in the anatomical and physiological characteristics of these two cerebellum-like structures.

Transformations of electrosensory encoding associated with an adaptive filter

Sensory information is often acquired through active exploration. However, an animal's own movements may result in changes in patterns of sensory input that could interfere with the detection and processing of behaviorally relevant sensory signals. Neural mechanisms for predicting the sensory consequences of movements are thus likely to be of general importance for sensory systems. Such mechanisms have been identified in cerebellum-like structures associated with electrosensory processing in fish. These structures are hypothesized to act as adaptive filters, removing correlations between incoming sensory input and central predictive signals through associative plasticity at parallel fiber synapses. The present study tests the adaptive filter hypothesis in the electrosensory lobe (ELL) of weakly electric mormyrid fish. We compared the ability of electroreceptors and ELL efferent neurons to encode the position of moving objects in the presence and absence of self-generated electrosensory signals caused by tail movements. Tail movements had strong effects on the responses of electroreceptors, substantially reducing the amount of information they conveyed about object position. In contrast, responses of efferent neurons were relatively unaffected by tail movements, and the information they conveyed about object position was preserved. We provide evidence that the electrosensory consequences of tail bending are opposed by proprioceptive inputs conveyed by parallel fibers and that the effects of proprioceptive inputs to efferent cells are plastic. These results support the idea that cerebellum-like structures learn and remove the predictable sensory consequences of behavior and link mechanisms of adaptive filtering to selective encoding of behaviorally relevant sensory information.

Synaptic plasticity and calcium signaling in Purkinje cells of the central cerebellar lobes of mormyrid fish

Climbing fiber (CF)-evoked calcium transients play a key role in plasticity at parallel fiber (PF) to Purkinje cell synapses in the mammalian cerebellum. Whereas PF activation alone causes long-term potentiation (LTP), coactivation of the heterosynaptic CF input, which evokes large dendritic calcium transients, induces long-term depression (LTD). This unique type of heterosynaptic interaction is a hallmark feature of synaptic plasticity in mammalian Purkinje cells. Purkinje cells in the cerebellum of mormyrid electric fish are characterized by a different architecture of their dendritic trees and by a more pronounced separation of CF and PF synaptic contact sites. We therefore examined the conditions for bidirectional plasticity at PF synapses onto Purkinje cells in the mormyrid cerebellum in vitro. PF stimulation at elevated frequencies induces LTP, whereas LTD results from PF stimulation at enhanced intensities and depends on dendritic calcium influx and metabotropic glutamate receptor type 1 activation. LTD can also be observed after pairing of low intensity PF stimulation with CF stimulation. Using a combination of whole-cell patch-clamp recordings and fluorometric calcium imaging, we characterized calcium transients in Purkinje cell dendrites. CF activation elicits calcium transients not only within the CF input territory (smooth proximal dendrites) but also within the PF input territory (spiny palisade dendrites). Paired PF and CF activation elicits larger calcium transients than stimulation of either input alone. A major source for dendritic calcium signaling is provided by P/Q-type calcium channels. Our data show that despite the spatial separation between the two inputs CF activity facilitates LTD induction at PF synapses.