Metabotropic Glutamate Receptor Activation in Cerebelar Purkinje Cells as Substrate for Adaptive Timing of the Classicaly Conditioned Eye Blink Response

To understand how the cerebellum adaptively times the classically conditioned nictitating membrane response (NMR), a model of the metabotropic glutamate receptor (mGluR) second messenger system in cerebellar Purkinje cells is constructed. In the model slow responses, generated postsynaptically by mGluR-mediated phosphoinositide hydrolysis, and calcium release from intracellular stores, bridge the interstimulus interval (ISI) between the onset of parallel fiber activity associated with the conditioned stimulus (CS) and climbing fiber activity associated with unconditioned stimulus (US) onset. Temporal correlation of metabotropic responses and climbing fiber signals produces persistent phosphorylation of both AMPA receptors and Ca2+-dependent K+ channels. This is responsible for long-term depression (LTD) of AMPA receptors. The phosphorylation of Ca2+-dependent K+ channels leads to a reduction in baseline membrane potential and a reduction of Purkinje cell population firing during the CS-US interval. The Purkinje cell firing decrease disinhibits cerebellar nuclear cells which then produce an excitatory response corresponding to the learned movement. Purkinje cell learning times the response, while nuclear cell learning can calibrate it. The model reproduces key features of the conditioned rabbit NMR: Purkinje cell population response is properly timed, delay conditioning occurs for ISIs of up to four seconds while trace conditioning occurs only at shorter ISIs, mixed training at two different ISis produces a double-peaked response, and ISIs of 200-400ms produce maximal responding. Biochemical similarities between timed cerebellar learning and photoreceptor transduction, and circuit similarities between the timed cerebellar circuit and a timed dentate-CA3 hippocampal circuit, are noted.Office of Naval Research (N00014- 92-J-4015, N00014-92-J-1309, N00014-95-1-0409); Air Force Office of Scientific Research (F49620-92-J-0225);National Science Foundation (IRI-90-24877

Gradients in the mammalian cerebellar cortex enable Fourier-like transformation and improve storing capacity

Cerebellar granule cells (GCs) make up the majority of all neurons in the vertebrate brain, but heterogeneities among GCs and potential functional consequences are poorly understood. Here, we identified unexpected gradients in the biophysical properties of GCs in mice. GCs closer to the white matter (inner-zone GCs) had higher firing thresholds and could sustain firing with larger current inputs than GCs closer to the Purkinje cell layer (outer-zone GCs). Dynamic Clamp experiments showed that inner- and outer-zone GCs preferentially respond to high- and low-frequency mossy fiber inputs, respectively, enabling dispersion of the mossy fiber input into its frequency components as performed by a Fourier transformation. Furthermore, inner-zone GCs have faster axonal conduction velocity and elicit faster synaptic potentials in Purkinje cells. Neuronal network modeling revealed that these gradients improve spike-timing precision of Purkinje cells and decrease the number of GCs required to learn spike-sequences. Thus, our study uncovers biophysical gradients in the cerebellar cortex enabling a Fourier-like transformation of mossy fiber inputs

Computational Models of Timing Mechanisms in the Cerebellar Granular Layer

A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject’s eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum

Evolving spiking neural networks for temporal pattern recognition in the presence of noise

Creative Commons - Attribution-NonCommercial-NoDerivs 3.0 United StatesNervous systems of biological organisms use temporal patterns of spikes to encode sensory input, but the mechanisms that underlie the recognition of such patterns are unclear. In the present work, we explore how networks of spiking neurons can be evolved to recognize temporal input patterns without being able to adjust signal conduction delays. We evolve the networks with GReaNs, an artificial life platform that encodes the topology of the network (and the weights of connections) in a fashion inspired by the encoding of gene regulatory networks in biological genomes. The number of computational nodes or connections is not limited in GReaNs, but here we limit the size of the networks to analyze the functioning of the networks and the effect of network size on the evolvability of robustness to noise. Our results show that even very small networks of spiking neurons can perform temporal pattern recognition in the presence of input noiseFinal Published versio

The hippocampus and cerebellum in adaptively timed learning, recognition, and movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-86-C-0037, 90-0128); Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-92-J-1904); National Institute of Mental Health (MH-42900

A Computational Mechanism for Unified Gain and Timing Control in the Cerebellum

Precise gain and timing control is the goal of cerebellar motor learning. Because the basic neural circuitry of the cerebellum is homogeneous throughout the cerebellar cortex, a single computational mechanism may be used for simultaneous gain and timing control. Although many computational models of the cerebellum have been proposed for either gain or timing control, few models have aimed to unify them. In this paper, we hypothesize that gain and timing control can be unified by learning of the complete waveform of the desired movement profile instructed by climbing fiber signals. To justify our hypothesis, we adopted a large-scale spiking network model of the cerebellum, which was originally developed for cerebellar timing mechanisms to explain the experimental data of Pavlovian delay eyeblink conditioning, to the gain adaptation of optokinetic response (OKR) eye movements. By conducting large-scale computer simulations, we could reproduce some features of OKR adaptation, such as the learning-related change of simple spike firing of model Purkinje cells and vestibular nuclear neurons, simulated gain increase, and frequency-dependent gain increase. These results suggest that the cerebellum may use a single computational mechanism to control gain and timing simultaneously

An Associative Memory Trace in the Cerebellar Cortex

Classical conditioning of motor responses, e.g., the eyeblink response, depends on the cerebellum. In the theoretical works of David Marr (1969) and James Albus (1971), it was proposed that Purkinje cells in the cerebellar cortex learn to associate the neutral conditioned stimulus with the response. Since their work, several studies have provided data that are consistent with this suggestion, but definitive evidence has been lacking. Information on how Purkinje cells change their activity during learning has been ambiguous and contradictory and there has been no information at all about how they behave during extinction and reacquisition. The electrical activity of single Purkinje cells was recorded with microelectrodes in decerebrate ferrets during learning, extinction, and relearning. We demonstrate that paired peripheral forelimb and periocular stimulation, as well as paired direct stimulation of cerebellar afferent pathways (mossy and climbing fibres) consistently causes a gradual acquisition of an inhibitory response in Purkinje cell simple spike firing. The response also displays gradual extinction to unpaired presentations of the stimuli, and reacquisition with substantial savings when paired stimulus presentation is reinstated. This conditioned Purkinje cell response thus has several properties that match known features of the conditioned eyeblink response across training trials. The temporal properties of the conditioned Purkinje cell response were also investigated. The response maximum was adaptively timed to precede the unconditioned stimulus. The latencies to response onset, maximum, and offset varied with the interstimulus interval used during training. Further training with changes in the interstimulus interval caused new learning of response latencies. Finally, short-term manipulations of the conditioned stimulus after training had effects on the Purkinje cell response that match effects on the conditioned eyeblink response. These data suggest that many of the behavioural phenomena in eyeblink conditioning can be explained at the level of the single Purkinje cell

A Purkinje cell Timing Mechanism. On the Physical Basis of a Temporal Duration Memory.

The standard view of neural signaling is that a neuron can influence its target cell by exciting or inhibiting it. Learning is thought to involve strengthening or weakening synaptic connections. For most behaviors, the brain must learn to produce precisely timed activity patterns. Learned response timing is indispensable for a wide range of tasks and requires learning of interstimulus intervals (ISIs). The learning mechanism thought to accomplish this combines time-varying patterns of activity in the pre-synaptic neural network with changes in synaptic strength between the pre-synaptic neurons active at the end of the ISI and the post-synaptic neuron. Timing-dependent learning can be studied in eyeblink conditioning. If a neutral conditional stimulus is paired with an unconditional blink-eliciting stimulus, at an ISI of fixed duration, it acquires the ability to elicit a blink that peaks near the end of the ISI. Cerebellar Purkinje cells that control the blink acquire adaptively timed pauses in spontaneous firing, conditioned Purkinje cell responses, that interrupt their tonic inhibition of cerebellar nuclear cells and cause excitatory output that generates the overt blink. Most models assume the generation of a time code instantiated in varying patterns of activity in the presynaptic granule cells that represent the passage of time. However, we show here (paper I) that a cerebellar Purkinje cell can learn to respond to a specific input with adaptively timed pauses without such a temporally patterned input. Training Purkinje cells with direct stimulation of their presynaptic fibers, and pharmacological blocking of interneurons shows that the timing mechanism is intrinsic to the cell itself and not an emergent property of the network. That an individual neuron can learn temporal relationships suggests the existence of intracellular temporal duration memory. We demonstrate that this Purkinje cell memory is triggered by the metabotropic glutamate receptor 7 (paper II) and that the timed voltage response in large part is produced by the G-protein activated K+ channel family Kir3/GIRK (paper III). The implication is that a learned and adjustable timing of a metabotropic signaling cascade constitutes a physical memory of temporal duration. A theoretical model (paper IV) describes how this could be accomplished by a learning mechanism that selects among a finite number of regulatory proteins, those which bestow the intracellular signaling cascade with latencies to activation and deactivation that matches the ISI. The results presented in this thesis show that the traditional view of learning as a change in synaptic strength is insufficient. Finally, because Purkinje cells directly control the conditioned eyeblink we believe that, to our knowledge, this is the first time that a causal link can be shown between a learned and timing-dependent behavior and not only a single neuron’s memory, but also the specific activating receptor of said memory and the specific ion channel that puts it into effect

Model-Founded Explorations of the Roles of Molecular Layer Inhibition in Regulating Purkinje Cell Responses in Cerebellar Cortex: More Trouble for the Beam Hypothesis

For most of the last 50 years, the functional interpretation for inhibition in cerebellar cortical circuitry has been dominated by the relatively simple notion that excitatory and inhibitory dendritic inputs sum, and if that sum crosses threshold at the soma the Purkinje cell generates an action potential. Thus, inhibition has traditionally been relegated to a role of sculpting, restricting, or blocking excitation. At the level of networks, this relatively simply notion is manifest in mechanisms like “surround inhibition” which is purported to “shape” or “tune” excitatory neuronal responses. In the cerebellum, where all cell types except one (the granule cell) are inhibitory, these assumptions regarding the role of inhibition continue to dominate. Based on our recent series of modeling and experimental studies, we now suspect that inhibition may play a much more complex, subtle, and central role in the physiological and functional organization of cerebellar cortex. This paper outlines how model-based studies are changing our thinking about the role of feed-forward molecular layer inhibition in the cerebellar cortex. The results not only have important implications for continuing efforts to understand what the cerebellum computes, but might also reveal important features of the evolution of this large and quintessentially vertebrate brain structure