Cerebellar-like Structure Research Articles

A Continuum of Response Properties across the Population of Unipolar Brush Cells in the Dorsal Cochlear Nucleus.

Unipolar brush cells (UBCs) in the cerebellum and dorsal cochlear nucleus (DCN) perform temporal transformations by converting brief mossy fiber bursts into long-lasting responses. In the cerebellar UBC population, mixing inhibition with graded mGluR1-dependent excitation leads to a continuum of temporal responses. In the DCN, it has been thought that mGluR1 contributes little to mossy fiber responses and that there are distinct excitatory and inhibitory UBC subtypes. Here, we investigate UBC response properties using noninvasive cell-attached recordings in the DCN of mice of either sex. We find a continuum of responses to mossy fiber bursts ranging from 100 ms excitation to initial inhibition followed by several seconds of excitation to inhibition lasting for hundreds of milliseconds. Pharmacological interrogation reveals excitatory responses are primarily mediated by mGluR1 Thus, UBCs in both the DCN and cerebellum rely on mGluR1 and have a continuum of response durations. The continuum of responses in the DCN may allow more flexible and efficient temporal processing than can be achieved with distinct excitatory and inhibitory populations.SIGNIFICANCE STATEMENT UBCs are specialized excitatory interneurons in cerebellar-like structures that greatly prolong the temporal responses of mossy fiber inputs. They are thought to help cancel out self-generated signals. In the DCN, the prevailing view was that there are two distinct ON and OFF subtypes of UBCs. Here, we show that instead the UBC population has a continuum of response properties. Many cells show suppression and excitation consecutively, and the response durations vary considerably. mGluR1s are crucial in generating a continuum of responses. To understand how UBCs contribute to temporal processing, it is essential to consider the continuous variations of UBC responses, which have advantages over just having opposing ON/OFF subtypes of UBCs.

A new control model for the temporal coordination of arm transport and hand preshape applying to two-dimensional space

It is an important research subject for the fields of cybernetics, robotics and artificial intelligence science that on the basis of neurophysiology and neuroanatomy, simulates and replicates the cerebellar sensorimotor control system for the robots. Around this theme and based on Hoff and Arbib׳s control theory of the minimum jerk, this paper presents a new control model with cerebellar-like structure which can account for the temporal coordination of arm transport and hand preshape during reach and grasp tasks. And it has also been suggested that how the structure could learn the two key functions required in the Hoff–Arbib theory, namely state look-ahead and TTG (time-to-go) estimation. By the simulation for two-dimensional motion of arm transport and hand preshape, the results demonstrate that some key features of human reach–grasp kinematics obtained by Hoff–Arbib model can be achieved by the cerebellum control model and some performances are even better. In a word, by learning and training, the model can obtain the accurate smooth motor trajectory.

Using a classic paper by Bell as a platform for discussing the role of corollary discharge-like signals in sensory perception and movement control

Decades of behavioral observations have shown that invertebrate and vertebrate species have the ability to distinguish between self-generated afferent inputs versus those that are generated externally. In the present article, I describe activities focused around the discussion of a classic American Physiological Society paper by Curtis C. Bell that lays the foundation for students to investigate the neural substrate underlying this ability. Students will leave this activity being able to 1) describe the technical aspects and limitations of an electric fish preparation commonly used to acquire single unit (extracellular) neurophysiological data, 2) provide physiological evidence showing that the activity of principal cells in the posterior lateral line lobe of the electric fish brain reflects that of a reafference comparator that could be used in dissociating self-generated versus externally generated sensory signals, and 3) knowledgeably discuss hypotheses concerning the role of corollary discharge and cerebellar-like structures in vertebrate and invertebrate species. The skills and background knowledge gained in this activity lay the platform for advanced study of scientific investigations into sensory, motor, and cognitive processes in undergraduate, graduate, or medical school curricula.

Divisive and non-monotonic gain control in open-loop neural circuits

The proper characterization of input-output properties of neurons is a long-standing goal in neuroscience. A common way to study such properties is by means of the so called f-I curve, which measures the output firing rate of a neuron as a response of a given level of constant input current [1]. In many cases, an average slope or gain characterizing the response may be obtained. This method is particularly interesting to understand the response properties of neurons to slowly modulated input, although extensions to other situations are also possible [2]. Mechanisms able to modulate the neural input-output properties have been extensively studied in this framework. For instance, a shift in the leak conductance of the neuron can cause subtractive effects in the f-I curve [3]. Mechanisms responsible for other forms of gain control, such as divisive or nonlinear effects, have been more elusive [3,4], and only a number of them are known [4,5]. We present here a computational model of a neural circuit which is able to display these two types of gain control mechanisms (i.e. divisive and nonlinear), in addition to the standard subtractive gain control. We have considered a circuit which models the indirect feedback pathway to the superficial pyramidal (SP) neurons, a cerebellar-like structure found in the electrosensory lateral-line lobe of the weakly electric fish. This fish is able to generate an oscillatory electric field which is used for prey detection and communication. Electroreceptors located in the fish' skin detect the modulations in the electric field caused by stimuli, e.g. other fish, and project to SP neurons and to another subpopulation of pyramidal cells called deep pyramidal (DP) neurons. The population of DP neurons then activates the feedback pathway, which provides a net inhibitory signal to the SP cells. We have found that the effect of the contribution of the feedback pathway is to divisively reduce the firing rate of the SP neurons, thus acting as a divisive gain control mechanism. This illustrates how divisive gain control may be obtained in open-loop recurrent circuits. In addition, we analyzed the conditions in which the f-I response curve of the SP neurons becomes non-monotonic, thus revealing a novel nonlinear gain control mechanism which agrees with in vitro experimental recordings in the electric fish [6].

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.

Signal cancellation and contrast invariance in electrosensory systems

When processing sensory input, it is of vital importance for the neural systems to be able to discriminate a novel stimulus from the background of redundant, unimportant signals. Neural mechanisms responsible for prediction and cancellation of redundant information could be an efficient way to achieve such discrimination. While the concrete mechanisms that the brain employs for this task are presently unknown, a network able to perform this cancellation is thought to exist in the electrosensory lateral line lobe (ELL) of weakly electric fish [1]. This fish emits a high-frequency (600-1000 Hz) sinusoidal electric organ discharge (EOD) into its environment to sense its surroundings and communicate to conspecifics. Small objects such as prey create spatially localized amplitude modulations (AMs) of the EOD, whereas tail bending or communication signals induce spatially global AMs [2]. These AMs are detected by electroreceptors that densely cover the body of the fish, and provide feedforward input to pyramidal cells in the ELL. It is known that a subpopulation of such pyramidal cells, the superficial pyramidal (SP) cells, remove low-frequency predictable global signals (i.e. tail bending) from their input to maximize detection of novel local stimuli (i.e. prey) [1]. This is presumably achieved using a feedback pathway involving the granule cell layer (a cerebellarlike structure known as EGp). These granule cells connect to SP cells via parallel fibers (PFs) which may be acting as delay lines segregated into frequency channels to destructively interfere with the global stimulus. Recent in vitro studies found a novel burst timingdependent learning rule which would be able to shape this feedback [3]. Following a previous work [4], we study the cancellation of low-frequency simple redundant signals, i.e. sine waves, in the ELL of the weakly electric fish. The study combines in vitro data, in vivo electrophysiology recordings from neurons in the ELL and numerical modeling to address this issue. More precisely, we model the neural network responsible for signal cancellation in the ELL of the fish, and compare our predictions with electrophysiology data recorded in vivo [4]. In the model, we assume the presence of: 1) stimulus-driven feedback to the SP neurons, 2) a large variety of temporal delays in the PFs transmitting such feedback, and 3) burstinduced long-term plasticity. We show that the modeled network is able to efficiently cancel global redundant signals by shaping the feedback as a negative image of the global signal arriving to the SP cells. Such negative image is generated via the burst-induced anti-Hebbian learning rule in the PF-SP cell synapses, while the full period of the signal is covered by the incoming feedback due to the wide range of PF delays present in the network. The cancellation is found to be in agreement with in vivo recordings, and it is strong for signals with frequencies up to 16 Hz, enabling a clearer background above which to detect relevant non-repetitive stimuli such as prey signals (and thus to better capture the prey). Due to the importance of the phase-relationship between the feedback and the stimulus, the mechanism is found to be frequency-specific, suggesting the presence of multiple frequency channels as observed in vivo [4]. Interestingly, our model predicts that the cancellation is maintained for signals with different AM strengths (i.e. contrasts). Such contrast-invariance is highly desirable since natural signals would display different contrasts depending, for instance, on the distance between the fish and the origin of the EOD perturbation. * Correspondence: jmejias@uottawa.ca Department of Physics, University of Ottawa, Ottawa, K1N 6N5 Ontario, Canada Full list of author information is available at the end of the article Mejias et al. BMC Neuroscience 2012, 13(Suppl 1):F2 http://www.biomedcentral.com/1471-2202/13/S1/F2

Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions

The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness.

Cerebellar Input Configuration Toward Object Model Abstraction in Manipulation Tasks

It is widely assumed that the cerebellum is one of the main nervous centers involved in correcting and refining planned movement and accounting for disturbances occurring during movement, for instance, due to the manipulation of objects which affect the kinematics and dynamics of the robot-arm plant model. In this brief, we evaluate a way in which a cerebellar-like structure can store a model in the granular and molecular layers. Furthermore, we study how its microstructure and input representations (context labels and sensorimotor signals) can efficiently support model abstraction toward delivering accurate corrective torque values for increasing precision during different-object manipulation. We also describe how the explicit (object-related input labels) and implicit state input representations (sensorimotor signals) complement each other to better handle different models and allow interpolation between two already stored models. This facilitates accurate corrections during manipulations of new objects taking advantage of already stored models.

Functional origins of the vertebrate cerebellum from a sensory processing antecedent

Abstract The structure of the cerebellar cortex is remarkably similar across vertebrate phylogeny. It is well developed in basal jawed fishes, such as sharks and rays with many of the same cell types and organizational features found in other vertebrate groups, including mammals. In particular, the lattice-like organization of cerebellar cortex (with a molecular layer of parallel fibres, interneurons, spiny Purkinje cell dendrites, and climbing fires) is a common defining characteristic. In addition to the cerebellar cortex, fishes and aquatic amphibians have a variety of cerebellum-like structures in the dorso-lateral wall of the hindbrain. These structures are adjacent to, and in part, contiguous with, the cerebellum. They derive their cerebellum-like name from the presence of a molecular layer of parallel fibers and inhibitory interneurons, which has striking organizational similarities to the molecular layer of the cerebellar cortex. However, these structures also have characteristics which differ from the cerebellum. For example, cerebellum-like structures do not have climbing fibres, and they are clearly sensory. They receive direct afferent input from peripheral sensory receptors and relay their outputs to midbrain sensory areas. As a consequence of this close sensory association, and the ability to characterise their signal processing in a behaviourally relevant context, good progress has been made in determining the fundamental processing algorithm in cerebellar-like structures. In particular, we have come to understand the contribution to signal processing made by the molecular layer, which provides an adaptive filter to cancel self-generated noise in electrosensory and lateral line systems. Given the fundamental similarities of the molecular layer across these structures, coupled with evidence that cerebellum-like structures may have been the evolutionary antecedent of the cerebellum, we address the question: do both share the same functional algorithm?

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.

Brain Organization and Specialization in Deep-Sea Chondrichthyans

Chondrichthyans occupy a basal place in vertebrate evolution and offer a relatively unexplored opportunity to study the evolution of vertebrate brains. This study examines the brain morphology of 22 species of deep-sea sharks and holocephalans, in relation to both phylogeny and ecology. Both relative brain size (expressed as residuals) and the relative development of the five major brain areas (telencephalon, diencephalon, mesencephalon, cerebellum, and medulla) were assessed. The cerebellar-like structures, which receive projections from the electroreceptive and lateral line organs, were also examined as a discrete part of the medulla. Although the species examined spanned three major chondrichthyan groupings (Squalomorphii, Galeomorphii, Holocephali), brain size and the relative development of the major brain areas did not track phylogenetic groupings. Rather, a hierarchical cluster analysis performed on the deep-sea sharks and holocephalans shows that these species all share the common characteristics of a relatively reduced telencephalon and smooth cerebellar corpus, as well as extreme relative enlargement of the medulla, specifically the cerebellar-like lobes. Although this study was not a functional analysis, it provides evidence that brain variation in deep-sea chondichthyans shows adaptive patterns in addition to underlying phylogenetic patterns, and that particular brain patterns might be interpreted as ‘cerebrotypes’.

Expression of zebrafish ROR alpha gene in cerebellar‐like structures

Mouse genetic studies have identified several genes involved in cerebellar development. The mouse mutants staggerer and lurcher are functionally deficient for the retinoid-related orphan receptor alpha (ROR alpha) and glutamate receptor delta2 (Grid2) genes, respectively, and they show similar functional and developmental abnormalities in the cerebellum. Here, we report the cloning and expression pattern of zebrafish ROR alpha orthologues rora1 and rora2, and compare their expression pattern with that of grid2. Expression of rora1 and rora2 is initiated at late gastrula and pharyngula stages, respectively. Both rora1 and rora2 are spatially expressed in the retina and tectum. Expression of rora2 was further observed in the cerebellum, as reported for mammalian ROR alpha. In the adult brain, rora2 and grid2 are coexpressed in brain regions, designated as cerebellar-like structures. These observations suggest an evolutionarily conserved function of ROR alpha orthologues in the vertebrate brain.

Temporal and spatial delineation of mouse Otx2 functions by conditional self‐knockout

To identify the independent spatial and temporal activities of the essential developmental gene the Otx2, the germline mutation of which is lethal at embryonic day 8.5, we floxed one allele and substituted the other with an inducible CreER recombinase gene. This makes 'trans' self-knockout possible at any developmental stage. The transient action of tamoxifen pulses allows time-course mutation. We demonstrate efficient temporal knockout and demarcate spatio-temporal windows in which Otx2 controls the head, brain structures and body development.

The great gate: control of sensory information flow to the cerebellum.

An evident feature of the physiology of the inferior olivary nucleus is modulation of the responsiveness of neurons to peripheral stimulation by the behavioral state of the subject animal. The olivary response to self-generated sensory inputs, as well as to input predictable from association with other stimuli, is suppressed. This suppression occurs in part at the level of the inferior olivary nucleus itself. On the other hand, the cells respond readily to sensory inputs that are not anticipated. On a cellular level inferior olivary neurons exhibit two properties that might account for information gating. The first one is the organization of synaptic inputs on olivary spines in glomerular structures, where extrinsic inhibitory and excitatory inputs, confined to the same olivary dendritic spine, can efficiently cancel each other if they arrive within a certain time window. About half of the inhibitory inputs to olivary glomeruli originate in the deep cerebellar nuclei and are regarded as an inhibitory feedback. The second property is subthreshold membrane potential oscillations, a property of the electrotonically coupled olivary network. Extrinsic synaptic inputs to the nucleus modulate the subthreshold oscillations, and consequently, the response properties of olivary neurons. A considerable amount of indirect evidence indicates that the occurrence of oscillations corresponds to states of increased responsiveness of the neurons to peripheral stimulation. The sensory filtering role of the inferior olivary nucleus invites comparison between the cerebellum and cerebellar-like structures. This comparison sheds important light on the function of the cerebellum.