Intracellular calcium dynamics permit a Purkinje neuron model to perform toggle and gain computations upon its inputs

Forrest, Michael

doi:10.3389/fncom.2014.00086

Cited by 21 publications

(25 citation statements)

References 68 publications

(135 reference statements)

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“…GABAergic inhibition generated by cerebellar interneurons locally modifies the conductance changes and Ca 2ϩ fluxes evoked by climbing fiber input (Callaway et al, 1995;Kitamura and Häusser, 2011). Decreases in gain occur following a CS when high Ca 2ϩ levels reduce parallel fiber input by activation of BK channels and/or endocannabinoid release (Brenowitz and Regehr, 2003;Rancz and Häusser, 2010) and modeling suggests that gain increases occur with local increases in Ca 2ϩ (Forrest, 2014). These same sources of variability in the response to climbing fiber input determine whether long-term facilitation or depression results at parallel fiber-Purkinje cell synapses (Coesmans et al, 2004;Medina and Lisberger, 2008;Rasmussen et al, 2013).…”

Section: Bidirectional Changes In Ss Encodingmentioning

confidence: 99%

Climbing Fibers Control Purkinje Cell Representations of Behavior

2017

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A crucial issue in understanding cerebellar function is the interaction between simple spike (SS) and complex spike (CS) discharge, the two fundamentally different activity modalities of Purkinje cells. Although several hypotheses have provided insights into the interaction, none fully explains or is completely consistent with the spectrum of experimental observations. Here, we show that during a pseudorandom manual tracking task in the monkey (Macaca mulatta), climbing fiber discharge dynamically controls the information present in the SS firing, triggering robust and rapid changes in the SS encoding of motor signals in 67% of Purkinje cells. The changes in encoding, tightly coupled to CS occurrences, consist of either increases or decreases in the SS sensitivity to kinematics or position errors and are not due to differences in SS firing rates or variability. Nor are the changes in sensitivity due to CS rhythmicity. In addition, the CS-coupled changes in encoding are not evoked by changes in kinematics or position errors. Instead, CS discharge most often leads alterations in behavior. Increases in SS encoding of a kinematic parameter are associated with larger changes in that parameter than are decreases in SS encoding. Increases in SS encoding of position error are followed by and scale with decreases in error. The results suggest a novel function of CSs, in which climbing fiber input dynamically controls the state of Purkinje cell SS encoding in advance of changes in behavior.

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Section: Bidirectional Changes In Ss Encodingmentioning

confidence: 99%

Climbing Fibers Control Purkinje Cell Representations of Behavior

2017

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“…9-11. In the temporal coding scheme, information is not only encoded in the timing and duration of spiking, but also in the timing and duration of non-spiking (quiescent) 29,30 . In our system, short and long spiking times (t ss and t sl ) in the PSC signal (Supplementary Figs.…”

Section: Resultsmentioning

confidence: 99%

Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves

et al. 2020

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The integration and cooperation of mechanoreceptors, neurons and synapses in somatosensory systems enable humans to efficiently sense and process tactile information. Inspired by biological somatosensory systems, we report an optoelectronic spiking afferent nerve with neural coding, perceptual learning and memorizing capabilities to mimic tactile sensing and processing. Our system senses pressure by MXene-based sensors, converts pressure information to light pulses by coupling light-emitting diodes to analog-to-digital circuits, then integrates light pulses using a synaptic photomemristor. With neural coding, our spiking nerve is capable of not only detecting simultaneous pressure inputs, but also recognizing Morse code, braille, and object movement. Furthermore, with dimensionality-reduced feature extraction and learning, our system can recognize and memorize handwritten alphabets and words, providing a promising approach towards e-skin, neurorobotics and human-machine interaction technologies.

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“…The up state is characterised by high simple spike rates whereas in the down state simple spiking is quiescent. Several models of Purkinje cell activity have attempted to describe this bimodality (Forrest, 2014;Forrest et al, 2012;Llinas and Sugimori, 1980b;Loewenstein et al, 2005;McKay et al, 2007;Williams et al, 2002). Combined theoretical and experimental work has shown that Purkinje cells exhibit a phenomenon known as inverse stochastic resonance.…”

Section: Discussionmentioning

confidence: 99%

A Purkinje cell model that simulates complex spikes

Burroughs

Cerminara

Apps

et al. 2020

Preprint

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Purkinje cells are the principal neurons of the cerebellar cortex. One of their distinguishing features is that they fire two distinct types of action potential, called simple and complex spikes, which interact with one another. Simple spikes are stereotypical action potentials that are elicited at high, but variable, rates (0 − 100 Hz) and have a consistent waveform. Complex spikes are composed of an initial action potential followed by a burst of lower amplitude spikelets. Complex spikes occur at comparatively low rates (∼ 1 Hz) and have a variable waveform. Although they are critical to cerebellar operation a simple model that describes the complex spike waveform is lacking. Here, a novel single-compartment model of Purkinje cell electrodynamics is presented. The simpler version of this model, with two active conductances and a leak channel, can simulate the features typical of complex spikes recorded in vitro. If calcium dynamics are also included, the model can capture the pause in simple spike activity that occurs after complex spike events. Together, these models provide an insight into the mechanisms behind complex spike spikelet generation, waveform variability and their interactions with simple spike activity.

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Intracellular calcium dynamics permit a Purkinje neuron model to perform toggle and gain computations upon its inputs

Cited by 21 publications

References 68 publications

Climbing Fibers Control Purkinje Cell Representations of Behavior

Climbing Fibers Control Purkinje Cell Representations of Behavior

Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves

A Purkinje cell model that simulates complex spikes

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