bims-climfi 2019-10-27 papers

Issue of 2019–10–27
three papers selected by
Jun Maruta, Mount Sinai Health System

Front Comput Neurosci. 2019 ;13 68

Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties.

Alice Geminiani, Alessandra Pedrocchi, Egidio D'Angelo, Claudia Casellato.

Sensorimotor signals are integrated and processed by the cerebellar circuit to predict accurate control of actions. In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). EGLIF models specifically tuned for each neuron type were embedded into an olivocerebellar scaffold reproducing realistic spatial organization and physiological convergence and divergence ratios of connections. In order to emulate the circuit involved in an eye blink response to two associated stimuli, we modeled two adjacent olivocerebellar microcomplexes with a common mossy fiber input but different climbing fiber inputs (either on or off). EGLIF-SNN model simulations revealed the emergence of fundamental response properties in Purkinje cells (burst-pause) and deep nuclei cells (pause-burst) similar to those reported in vivo. The expression of these properties depended on the specific activation of climbing fibers in the microcomplexes and did not emerge with scaffold models using simplified point neurons. This result supports the importance of embedding SNNs with realistic neuronal dynamics and appropriate connectivity and anticipates the scale-up of EGLIF-SNN and the embedding of plasticity rules required to investigate cerebellar functioning at multiple scales.

Keywords: eyeblink response; non-linear neuronal dynamics; olivocerebellar circuit; point neuron; spiking neural network (SNN)

DOI: https://doi.org/10.3389/fncom.2019.00068

Front Syst Neurosci. 2019 ;13 50

Delayed Complex Spike Response Evoked by Conditioned Stimulus Encodes Movement Onset Time and Is Determined by Intrinsic Inferior Olive Properties.

Yasmin Yarden-Rabinowitz, Yosef Yarom.

Recent studies demonstrate that after classical conditioning the conditioned stimulus (CS) triggers a delayed complex spike. This new finding revolutionizes our view on the role of complex spike activity. The classical view of the complex spike as an error signal has been replaced by a signal that encodes for expectation, prediction and reward. In this brief perspective, we review some of these works, focusing on the characteristic delay of the response (~80 ms), its independence on the time interval between CS and the unconditioned stimulus (US) and its relationship to movement onset. In view of these points, we suggest that the generation of complex spike activity following learning, encodes for timing of movements onset. We then provide original data recorded from Purkinje and cerebellar nuclei neurons, demonstrating that delayed complex spike activity is an intrinsic property of the cerebellar circuit. We, therefore, suggest that learning of classical conditioning involves modulation of cerebellar circuitry where timing is provided by the inferior olive and the movement kinematic is delivered by the cerebellar nuclei projection neurons.

Keywords: Purkinje neurons; cerebellum; classical conditioning; complex spike; inferior olive

DOI: https://doi.org/10.3389/fnsys.2019.00050

Neurobiol Learn Mem. 2019 Oct 21. pii: S1074-7427(19)30170-4. [Epub ahead of print] 107103

Intrinsic memory of temporal intervals in cerebellar Purkinje cells.

Fredrik Johansson.

The general consensus for learning and memory, including in the cerebellum, is that modification of synaptic strength via long-term potentiation (LTP) or long-term depression (LTD) are the primary mechanisms for the formation of memories. Recent findings suggest additional cellular mechanisms - referred to as 'intrinsic plasticity' - where a neuron's membrane excitability intrinsically changes. These mechanisms act like a dimmer and alter neuronal responsiveness by adjusting response amplitudes and spike thresholds. Here, I argue that classical conditioning of cerebellar Purkinje cell responses reveals yet another cell-intrinsic learning mechanism which significantly differs from both changes in synaptic strength and changes in membrane excitability. When the conditional (CS) and unconditional stimuli (US) are delivered directly to the Purkinje cell's immediate pre-synaptic afferents, the parallel fibres and the climbing fibre, the cell learns to respond to the CS with a pause in its spontaneous firing that reflects the interval between the two stimuli. The pause response has a delayed onset and adaptively timed maximum, offset and duration, determined by the previously experienced CS-US interval. The timing is not dependent on any network-generated time-varying input. This implies the existence of a timing mechanism and a memory substrate that encodes the duration of the CS-US interval inside the Purkinje cell. Such temporal interval learning is not simply a change that causes more or less firing in response to an input. Here, I review these findings in relation to the standard theory of synaptic strength changes and the network interactions believed to be necessary for generating time codes.

Keywords: Cerebellum; Eyeblink conditioning; Intrinsic plasticity; Purkinje cell; Timing

DOI: https://doi.org/10.1016/j.nlm.2019.107103