Transformations of electrosensory encoding associated with an adaptive filter

An internal model for canceling self-generated sensory input in freely behaving electric fish

Internal models that predict the sensory consequences of motor actions are vital for sensory, motor, and cognitive functions. However, the relationship between motor action and sensory input is complex, often varying from one moment to another depending on the state of the animal and the environment. The neural mechanisms for generating predictions under such challenging, real-world conditions remain largely unknown. Using novel methods for underwater neural recording, a quantitative analysis of unconstrained behavior, and computational modeling, we provide evidence for an unexpectedly sophisticated internal model at the first stage of active electrosensory processing in mormyrid fish. Closed-loop manipulations reveal that electrosensory lobe neurons are capable of simultaneously learning and storing multiple predictions of the sensory consequences of motor commands specific to different sensory states. These results provide mechanistic insights into how internal motor signals and information about the sensory environment are combined within a cerebellum-like circuitry to predict the sensory consequences of natural behavior.

Being active over one's own motion: Considering predictive mechanisms in self-motion perception

Self-motion perception is a key element guiding pilots' behavior. Its importance is mostly revealed when impaired, leading in most cases to spatial disorientation which is still today a major factor of accidents occurrence. Self-motion perception is known as mainly based on visuo-vestibular integration and can be modulated by the physical properties of the environment with which humans interact. For instance, several studies have shown that the respective weight of visual and vestibular information depends on their reliability. More recently, it has been suggested that the internal state of an operator can also modulate multisensory integration. Interestingly, the systems' automation can interfere with this internal state through the loss of the intentional nature of movements (i.e., loss of agency) and the modulation of associated predictive mechanisms. In this context, one of the new challenges is to better understand the relationship between automation and self-motion perception. The present review explains how linking the concepts of agency and self-motion is a first approach to address this issue.

A Dense Conformal Electrode Array for High Spatial Resolution Stimulation of Electrosensory Systems

Studies of electrosensory systems have led to insights into to a number of general issues in biology. However, investigations of these systems have been limited by the inability to precisely control spatial patterns of electrosensory input. In this paper, an electrode array and a system to selectively stimulate spatially restricted regions of an electroreceptor array is presented. The array has 96 channels consisting of chrome/gold electrodes patterned on a flexible parylene-C substrate and encapsulated with another parylene-C layer. The conformability of the electrode array allows for optimal current driving and surface interface conditions. Recordings of neural activity at the first central processing stage in weakly electric mormyrid fish support the potential of this system for high spatial resolution stimulation and mapping of electrosensory systems.

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.

Neural readout of a latency code in the active electrosensory system

The latency of spikes relative to a stimulus conveys sensory information across modalities. However, in most cases, it remains unclear whether and how such latency codes are utilized by postsynaptic neurons. In the active electrosensory system of mormyrid fish, a latency code for stimulus amplitude in electroreceptor afferent nerve fibers (EAs) is hypothesized to be read out by a central reference provided by motor corollary discharge (CD). Here, we demonstrate that CD enhances sensory responses in postsynaptic granular cells of the electrosensory lobe but is not required for reading out EA input. Instead, diverse latency and spike count tuning across the EA population give rise to graded information about stimulus amplitude that can be read out by standard integration of converging excitatory synaptic inputs. Inhibitory control over the temporal window of integration renders two granular cell subclasses differentially sensitive to information derived from relative spike latency versus spike count.

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.

Strategies of object polarization and their role in electrosensory information gathering

Weakly electric fish polarize the nearby environment with a stereotyped electric field and gain information by detecting the changes imposed by objects with tuned sensors. Here we focus on polarization strategies as paradigmatic bioinspiring mechanisms for sensing devices. We begin this research developing a toy model that describes three polarization strategies exhibited by three different groups of fish. We then report an experimental analysis which confirmed predictions of the model and in turn predicted functional consequences that were explored in behavioral experiments in the pulse fish Gymnotus omarorum. In the experiments, polarization was evaluated by estimating the object's stamp (i.e. the electric source that produces the same electric image as the object) as a function of object impedance, orientation, and position. Signal detection and discrimination was explored in G. omarorum by provoking novelty responses, which are known to reflect the increment in the electric image provoked by a change in nearby impedance. To achieve this, we stepped the longitudinal impedance of a cylindrical object between two impedances (either capacitive or resistive). Object polarization and novelty responses indicate that G. omarorum has two functional regions in the electrosensory field. At the front of the fish, there is a foveal field where object position and orientation are encoded in signal intensity, while the qualia associated with impedance is encoded in signal time course. On the side of the fish there is a peripheral field where the complexity of the polarizing field facilitates detection of objects oriented in any angle with respect to the fish´s longitudinal axis. These findings emphasize the importance of articulating field generation, sensor tuning and the repertoire of exploratory movements to optimize performance of artificial active electrosensory systems.

The Macaque Cerebellar Flocculus Outputs a Forward Model of Eye Movement

The central nervous system (CNS) achieves fine motor control by generating predictions of the consequences of the motor command, often called forward models of the movement. These predictions are used centrally to detect not-self generated sensations, to modify ongoing movements, and to induce motor learning. However, finding a neuronal correlate of forward models has proven difficult. In the oculomotor system, we can identify neuronal correlates of forward models vs. neuronal correlates of motor commands by examining neuronal responses during smooth pursuit at eccentric eye positions. During pursuit, torsional eye movement information is not present in the motor command, but it is generated by the mechanic of the orbit. Importantly, the directionality and approximate magnitude of torsional eye movement follow the half angle rule. We use this rule to investigate the role of the cerebellar flocculus complex (FL, flocculus and ventral paraflocculus) in the generation of forward models of the eye. We found that mossy fibers (input elements to the FL) did not change their response to pursuit with eccentricity. Thus, they do not carry torsional eye movement information. However, vertical Purkinje cells (PCs; output elements of the FL) showed a preference for counter-clockwise (CCW) eye velocity [corresponding to extorsion (outward rotation) of the ipsilateral eye]. We hypothesize that FL computes an estimate of torsional eye movement since torsion is present in PCs but not in mossy fibers. Overall, our results add to those of other laboratories in supporting the existence in the CNS of a predictive signal constructed from motor command information.

Sensory Flow as a Basis for a Novel Distance Cue in Freely Behaving Electric Fish

The sensory input that an animal receives is directly linked to its motor activity. Behavior thus enables animals to influence their sensory input, a concept referred to as active sensing. How such behavior can serve as a scaffold for generating sensory information is of general scientific interest. In this article, we investigate how behavior can shape sensory information by using some unique features of the sensorimotor system of the weakly electric fish. Based on quantitative behavioral characterizations and computational reconstruction of sensory input, we show how electrosensory flow is actively created during highly patterned, spontaneous behavior in Gnathonemus petersii. The spatiotemporal structure of the sensory input provides information for the computation of a novel distance cue, which allows for a continuous estimation of distance. This has significant advantages over previously known nondynamic distance estimators as determined from electric image blur. Our investigation of the sensorimotor interactions in pulsatile electrolocation shows, for the first time, that the electrosensory flow contains behaviorally relevant information accessible only through active behavior. As patterned sensory behaviors are a shared feature of (active) sensory systems, our results have general implications for the understanding of (active) sensing, with the proposed sensory flow-based measure being potentially pertinent to a broad range of sensory modalities.

SIGNIFICANCE STATEMENT

Acquisition of sensory information depends on motion, as either an animal or its sensors move. Behavior can thus actively influence the sensory flow; and in this way, behavior can be seen as a manifestation of the brain's integrative functions. The properties of the active pulsatile electrolocation system in Gnathonemus petersii allow for the sensory input to be computationally reconstructed, enabling us to link the informational content of spatiotemporal sensory dynamics to behavior. Our study reveals a novel sensory cue for estimating depth that is actively generated by the fishes' behavior. The physical and behavioral similarities between electrolocation and other active sensory systems suggest that this may be a mechanism shared by (active) sensory systems.

A comparative approach to cerebellar function: insights from electrosensory systems

Despite its simple and highly-ordered circuitry the function of the cerebellum remains a topic of vigorous debate. This review explores connections between the cerebellum and sensory processing structures that closely resemble the cerebellum in terms of their evolution, development, patterns of gene expression, and circuitry. Recent studies of cerebellum-like structures involved in electrosensory processing in fish have provided insights into the functions of granule cells and unipolar brush cells-cell types shared with the cerebellum. We also discuss the possibility, supported by recent studies, that generating and subtracting predictions of the sensory consequences of motor commands may be core functions shared by both cerebellum-like structures and the cerebellum.

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.

Modeling latency code processing in the electric sense: from the biological template to its VLSI implementation

Understanding the coding of sensory information under the temporal constraints of natural behavior is not yet well resolved. There is a growing consensus that spike timing or latency coding can maximally exploit the timing of neural events to make fast computing elements and that such mechanisms are essential to information processing functions in the brain. The electric sense of mormyrid fish provides a convenient biological model where this coding scheme can be studied. The sensory input is a physically ordered spatial pattern of current densities, which is coded in the precise timing of primary afferent spikes. The neural circuits of the processing pathway are well known and the system exhibits the best known illustration of corollary discharge, which provides the reference to decoding the sensory afferent latency pattern. A theoretical model has been constructed from available electrophysiological and neuroanatomical data to integrate the principal traits of the neural processing structure and to study sensory interaction with motor-command-driven corollary discharge signals. This has been used to explore neural coding strategies at successive stages in the network and to examine the simulated network capacity to reproduce output neuron responses. The model shows that the network has the ability to resolve primary afferent spike timing differences in the sub-millisecond range, and that this depends on the coincidence of sensory and corollary discharge-driven gating signals. In the integrative and output stages of the network, corollary discharge sets up a proactive background filter, providing temporally structured excitation and inhibition within the network whose balance is then modulated locally by sensory input. This complements the initial gating mechanism and contributes to amplification of the input pattern of latencies, conferring network hyperacuity. These mechanisms give the system a robust capacity to extract behaviorally meaningful features of the electric image with high sensitivity over a broad working range. Since the network largely depends on spike timing, we finally discuss its suitability for implementation in robotic applications based on neuromorphic hardware.

The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unimodal or monotonic tuning curves

Tuning curves and receptive fields are widely used to describe the selectivity of sensory neurons, but the relationship between firing rates and information is not always intuitive. Neither high firing rates nor high tuning curve gradients necessarily mean that stimuli at that part of the tuning curve are well represented by a neuron. Recent research has shown that trial-to-trial variability (noise) and population size can strongly affect which stimuli are most precisely represented by a neuron in the context of a population code (the best-encoded stimulus), and that different measures of information can give conflicting indications. Specifically, the Fisher information is greatest where the tuning curve gradient is greatest, such as on the flanks of peaked tuning curves, but the stimulus-specific information (SSI) is greatest at the tuning curve peak for small populations with high trial-to-trial variability. Previous research in this area has focussed upon unimodal (peaked) tuning curves, and in this article we extend these analyses to monotonic tuning curves. In addition, we examine how stimulus spacing in forced choice tasks affects the best-encoded stimulus. Our results show that, regardless of the tuning curve, Fisher information correctly predicts the best-encoded stimulus for large populations and where the stimuli are closely spaced in forced choice tasks. In smaller populations with high variability, or in forced choice tasks with widely-spaced choices, the best-encoded stimulus falls at the peak of unimodal tuning curves, but is more variable for monotonic tuning curves. Task, population size and variability all need to be considered when assessing which stimuli a neuron represents, but the best-encoded stimulus can be estimated on a case-by case basis using commonly available computing facilities.

A role for mixed corollary discharge and proprioceptive signals in predicting the sensory consequences of movements

Animals must distinguish behaviorally relevant patterns of sensory stimulation from those that are attributable to their own movements. In principle, this distinction could be made based on internal signals related to motor commands, known as corollary discharge (CD), sensory feedback, or some combination of both. Here we use an advantageous model system--the electrosensory lobe (ELL) of weakly electric mormyrid fish--to directly examine how CD and proprioceptive feedback signals are transformed into negative images of the predictable electrosensory consequences of the fish's motor commands and/or movements. In vivo recordings from ELL neurons and theoretical modeling suggest that negative images are formed via anti-Hebbian plasticity acting on random, nonlinear mixtures of CD and proprioception. In support of this, we find that CD and proprioception are randomly mixed in spinal mossy fibers and that properties of granule cells are consistent with a nonlinear recoding of these signals. The mechanistic account provided here may be relevant to understanding how internal models of movement consequences are implemented in other systems in which similar components (e.g., mixed sensory and motor signals and synaptic plasticity) are found.

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.

Early vestibular processing does not discriminate active from passive self-motion if there is a discrepancy between predicted and actual proprioceptive feedback

Most of our sensory experiences are gained by active exploration of the world. While the ability to distinguish sensory inputs resulting of our own actions (termed reafference) from those produced externally (termed exafference) is well established, the neural mechanisms underlying this distinction are not fully understood. We have previously proposed that vestibular signals arising from self-generated movements are inhibited by a mechanism that compares the internal prediction of the proprioceptive consequences of self-motion to the actual feedback. Here we directly tested this proposal by recording from single neurons in monkey during vestibular stimulation that was externally produced and/or self-generated. We show for the first time that vestibular reafference is equivalently canceled for self-generated sensory stimulation produced by activation of the neck musculature (head-on-body motion), or axial musculature (combined head and body motion), when there is no discrepancy between the predicted and actual proprioceptive consequences of self-motion. However, if a discrepancy does exist, central vestibular neurons no longer preferentially encode vestibular exafference. Specifically, when simultaneous active and passive motion resulted in activation of the same muscle proprioceptors, neurons robustly encoded the total vestibular input (i.e., responses to vestibular reafference and exafference were equally strong), rather than exafference alone. Taken together, our results show that the cancellation of vestibular reafference in early vestibular processing requires an explicit match between expected and actual proprioceptive feedback. We propose that this vital neuronal computation, necessary for both accurate sensory perception and motor control, has important implications for a variety of sensory systems that suppress self-generated signals.

Decorrelation learning in the cerebellum: computational analysis and experimental questions

Many cerebellar models use a form of synaptic plasticity that implements decorrelation learning. Parallel fibers carrying signals positively correlated with climbing-fiber input have their synapses weakened (long-term depression), whereas those carrying signals negatively correlated with climbing input have their synapses strengthened (long-term potentiation). Learning therefore ceases when all parallel-fiber signals have been decorrelated from climbing-fiber input. This is a computationally powerful rule for supervised learning and can be cast in a spike-timing dependent plasticity form for comparison with experimental evidence. Decorrelation learning is particularly well suited to sensory prediction, for example, in the reafference problem where external sensory signals are interfered with by reafferent signals from the organism's own movements, and the required circuit appears similar to the one found to mediate classical eye blink conditioning. However, for certain stimuli, avoidance is a much better option than simple prediction, and decorrelation learning can also be used to acquire appropriate avoidance movements. One example of a stimulus to be avoided is retinal slip that degrades visual processing, and decorrelation learning appears to play a role in the vestibulo-ocular reflex that stabilizes gaze in the face of unpredicted head movements. Decorrelation learning is thus suitable for both sensory prediction and motor control. It may also be well suited for generic spatial and temporal coordination, because of its ability to remove the unwanted side effects of movement. Finally, because it can be used with any kind of time-varying signal, the cerebellum could play a role in cognitive processing.

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.

Modeling cancelation of periodic inputs with burst-STDP and feedback

Prediction and cancelation of redundant information is an important feature that many neural systems must display in order to efficiently code external signals. We develop an analytic framework for such cancelation in sensory neurons produced by a cerebellar-like structure in wave-type electric fish. Our biologically plausible mechanism is motivated by experimental evidence of cancelation of periodic input arising from the proximity of conspecifics as well as tail motion. This mechanism involves elements present in a wide range of systems: (1) stimulus-driven feedback to the neurons acting as detectors, (2) a large variety of temporal delays in the pathways transmitting such feedback, responsible for producing frequency channels, and (3) burst-induced long-term plasticity. The bursting arises from back-propagating action potentials. Bursting events drive the input frequency-dependent learning rule, which in turn affects the feedback input and thus the burst rate. We show how the mean firing rate and the rate of production of 2- and 4-spike bursts (the main learning events) can be estimated analytically for a leaky integrate-and-fire model driven by (slow) sinusoidal, back-propagating and feedback inputs as well as rectified filtered noise. The effect of bursts on the average synaptic strength is also derived. Our results shed light on why bursts rather than single spikes can drive learning in such networks "online", i.e. in the absence of a correlative discharge. Phase locked spiking in frequency specific channels together with a frequency-dependent STDP window size regulate burst probability and duration self-consistently to implement cancelation.

Adaptive filters and internal models: multilevel description of cerebellar function

Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles.

An internal model architecture for novelty detection: implications for cerebellar and collicular roles in sensory processing

The cerebellum is thought to implement internal models for sensory prediction, but details of the underlying circuitry are currently obscure. We therefore investigated a specific example of internal-model based sensory prediction, namely detection of whisker contacts during whisking. Inputs from the vibrissae in rats can be affected by signals generated by whisker movement, a phenomenon also observable in whisking robots. Robot novelty-detection can be improved by adaptive noise-cancellation, in which an adaptive filter learns a forward model of the whisker plant that allows the sensory effects of whisking to be predicted and thus subtracted from the noisy sensory input. However, the forward model only uses information from an efference copy of the whisking commands. Here we show that the addition of sensory information from the whiskers allows the adaptive filter to learn a more complex internal model that performs more robustly than the forward model, particularly when the whisking-induced interference has a periodic structure. We then propose a neural equivalent of the circuitry required for adaptive novelty-detection in the robot, in which the role of the adaptive filter is carried out by the cerebellum, with the comparison of its output (an estimate of the self-induced interference) and the original vibrissal signal occurring in the superior colliculus, a structure noted for its central role in novelty detection. This proposal makes a specific prediction concerning the whisker-related functions of a region in cerebellar cortical zone A₂ that in rats receives climbing fibre input from the superior colliculus (via the inferior olive). This region has not been observed in non-whisking animals such as cats and primates, and its functional role in vibrissal processing has hitherto remained mysterious. Further investigation of this system may throw light on how cerebellar-based internal models could be used in broader sensory, motor and cognitive contexts.

Fisher and Shannon Information in Finite Neural Populations

The precision of the neural code is commonly investigated using two families of statistical measures: Shannon mutual information and derived quantities when investigating very small populations of neurons and Fisher information when studying large populations. These statistical tools are no longer the preserve of theorists and are being applied by experimental research groups in the analysis of empirical data. Although the relationship between information-theoretic and Fisher-based measures in the limit of infinite populations is relatively well understood, how these measures compare in finite-size populations has not yet been systematically explored. We aim to close this gap. We are particularly interested in understanding which stimuli are best encoded by a given neuron within a population and how this depends on the chosen measure. We use a novel Monte Carlo approach to compute a stimulus-specific decomposition of the mutual information (the SSI) for populations of up to 256 neurons and show that Fisher information can be used to accurately estimate both mutual information and SSI for populations of the order of 100 neurons, even in the presence of biologically realistic variability, noise correlations, and experimentally relevant integration times. According to both measures, the stimuli that are best encoded are those falling at the flanks of the neuron's tuning curve. In populations of fewer than around 50 neurons, however, Fisher information can be misleading.

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.

Frequency-tuned cerebellar channels and burst-induced LTD lead to the cancellation of redundant sensory inputs

For optimal sensory processing, neural circuits must extract novel, unpredictable signals from the redundant sensory input in which they are embedded, but the detailed cellular and network mechanisms that implement such selective cancellation are presently unknown. Using a combination of modeling and experiment, we characterize in detail a cerebellar circuit in weakly electric fish, showing how it can carry out this computation. We use a model incorporating the wide range of experimentally estimated parallel fiber feedback delays and a burst-induced LTD rule derived from in vitro experiments to explain the precise cancellation of redundant signals observed in vivo. Our model demonstrates how the backpropagation-dependent burst dynamics adjusts the temporal pairing width of the plasticity mechanism to precisely match the frequency of the redundant signal. The model also makes the prediction that this cerebellar feedback pathway must be composed of frequency-tuned channels; this prediction is subsequently verified in vivo, highlighting a novel and general capability of cerebellar circuitry.

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.

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.

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.

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.

In vitro studies of closed-loop feedback and electrosensory processing in Apteronotus leptorhynchus

Electrosensory systems comprise extensive feedback pathways. It is also well known that these pathways exhibit synaptic plasticity on a wide-range of time scales. Recent in vitro brain slice studies have characterized synaptic plasticity in the two main feedback pathways to the electrosensory lateral line lobe (ELL), a primary electrosensory nucleus in Apteronotus leptorhynchus. Currently-used slice preparations, involving networks in open-loop conditions, allow feedback inputs to be studied in isolation, a critical step in determining their synaptic properties. However, to fully understand electrosensory processing, we must understand how dynamic feedback modulates neuronal responses under closed-loop conditions. To bridge the gap between current in vitro approaches and more complex in vivo work, we present two new in vitro approaches for studying the roles of closed-loop feedback in electrosensory processing. The first involves a hybrid-network approach using dynamic clamp, and the second involves a new slice preparation that preserves one of the feedback pathways to ELL in a closed-loop condition.

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.

Active sensing: Pre-receptor mechanisms and behavior in electric fish

Weakly electric fish perceive their actively generated electrical field with cutaneous electroreceptors. This active sensory system is used both for orientation and for communication. In a recent paper1 we focussed on how anatomical adaptations (pre-receptor mechanisms), biophysical constraints and behavior all contribute to active electrolocation, i.e., the fishes' unique ability to determine and distinguish the electrical properties of objects based on the modulation of a self-generated carrier signal, the so-called electric organ discharge.