The Effect of Different Forms of Synaptic Plasticity on Pattern Recognition in the Cerebellar Cortex

“The original publication is available at www.springerlink.com”. Copyright Springer.Many cerebellar learning theories assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells (PCs) provides the basis for pattern recognition in the cerebellum. Previous work has suggested that PCs can use a novel neural code based on the duration of silent periods. These simulations have used a simplified learning rule, where the synaptic conductance was halved each time a pattern was learned. However, experimental studies in cerebellar slices show that the synaptic conductance saturates and is rarely reduced to less than 50% of its baseline value. Moreover, the previous simulations did not include plasticity of the synapses between inhibitory interneurons and PCs. Here we study the effect of LTD saturation and inhibitory synaptic plasticity on pattern recognition in a complex PC model. We find that the PC model is very sensitive to the value at which LTD saturates, but is unaffected by inhibitory synaptic plasticity.Peer reviewe

An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network

Polychronous neural groups are effective structures for the recognition of precise spike-timing patterns but the detection method is an inefficient multi-stage brute force process that works off-line on pre-recorded simulation data. This work presents a new model of polychronous patterns that can capture precise sequences of spikes directly in the neural simulation. In this scheme, each neuron is assigned a randomized code that is used to tag the post-synaptic neurons whenever a spike is transmitted. This creates a polychronous code that preserves the order of pre-synaptic activity and can be registered in a hash table when the post-synaptic neuron spikes. A polychronous code is a sub-component of a polychronous group that will occur, along with others, when the group is active. We demonstrate the representational and pattern recognition ability of polychronous codes on a direction selective visual task involving moving bars that is typical of a computation performed by simple cells in the cortex. The computational efficiency of the proposed algorithm far exceeds existing polychronous group detection methods and is well suited for online detection.Comment: 17 pages, 8 figure

Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise

Safaryan, K. et al. Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise. Sci. Rep. 7, 46550; doi: 10.1038/srep46550 (2017). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ © The Author(s) 2017.Many forms of synaptic plasticity require the local production of volatile or rapidly diffusing substances such as nitric oxide. The nonspecific plasticity these neuromodulators may induce at neighboring non-active synapses is thought to be detrimental for the specificity of memory storage. We show here that memory retrieval may benefit from this non-specific plasticity when the applied sparse binary input patterns are degraded by local noise. Simulations of a biophysically realistic model of a cerebellar Purkinje cell in a pattern recognition task show that, in the absence of noise, leakage of plasticity to adjacent synapses degrades the recognition of sparse static patterns. However, above a local noise level of 20 %, the model with nonspecific plasticity outperforms the standard, specific model. The gain in performance is greatest when the spatial distribution of noise in the input matches the range of diffusion-induced plasticity. Hence non-specific plasticity may offer a benefit in noisy environments or when the pressure to generalize is strong.Peer reviewe

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

No abstract availabl

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate realistic models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems

A neural network model of adaptively timed reinforcement learning and hippocampal dynamics

A neural model is described of how adaptively timed reinforcement learning occurs. The adaptive timing circuit is suggested to exist in the hippocampus, and to involve convergence of dentate granule cells on CA3 pyramidal cells, and NMDA receptors. This circuit forms part of a model neural system for the coordinated control of recognition learning, reinforcement learning, and motor learning, whose properties clarify how an animal can learn to acquire a delayed reward. Behavioral and neural data are summarized in support of each processing stage of the system. The relevant anatomical sites are in thalamus, neocortex, hippocampus, hypothalamus, amygdala, and cerebellum. Cerebellar influences on motor learning are distinguished from hippocampal influences on adaptive timing of reinforcement learning. The model simulates how damage to the hippocampal formation disrupts adaptive timing, eliminates attentional blocking, and causes symptoms of medial temporal amnesia. It suggests how normal acquisition of subcortical emotional conditioning can occur after cortical ablation, even though extinction of emotional conditioning is retarded by cortical ablation. The model simulates how increasing the duration of an unconditioned stimulus increases the amplitude of emotional conditioning, but does not change adaptive timing; and how an increase in the intensity of a conditioned stimulus "speeds up the clock", but an increase in the intensity of an unconditioned stimulus does not. Computer simulations of the model fit parametric conditioning data, including a Weber law property and an inverted U property. Both primary and secondary adaptively timed conditioning are simulated, as are data concerning conditioning using multiple interstimulus intervals (ISIs), gradually or abruptly changing ISis, partial reinforcement, and multiple stimuli that lead to time-averaging of responses. Neurobiologically testable predictions are made to facilitate further tests of the model.Air Force Office of Scientific Research (90-0175, 90-0128); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-87-16960); Office of Naval Research (N00014-91-J-4100

Why should we keep the cerebellum in mind when thinking about addiction?

Increasing evidence has involved the cerebellum in functions beyond the sphere of motor control. In the present article, we review evidence that involves the cerebellum in addictive behaviour. We aimed on molecular and cellular targets in the cerebellum where addictive drugs can act and induce mechanisms of neuroplasticity that may contribute to the development of an addictive pattern of behaviour. Also, we analyzed the behavioural consequences of repetitive drug administration that result from activitydependent changes in the efficacy of cerebellar synapses. Revised research involves the cerebellum in drug-induced long-term memory, druginduced sensitization and the perseverative behavioural phenotype. Results agree to relevant participation of the cerebellum in the functional systems underlying drug addiction. The molecular and cellular actions of addictive drugs in the cerebellum involve long-term adaptative changes in receptors, neurotransmitters and intracellular signalling transduction pathways that may lead to the re-organization of cerebellar microzones and in turn to functional networks where the cerebellum is an important nodal structure. We propose that drug induced activity-dependent synaptic changes in the cerebellum are crucial to the transition from a pattern of recreational drug taking to the compulsive behavioural phenotype. Functional and structural modifications produced by drugs in the cerebellum may enhance the susceptibility of fronto-cerebellar circuitry to be changed by repeated drug exposure. As a part of this functional reorganization, drug-induced cerebellar hyper-responsiveness appears to be central to reducing the influence of executive control of the prefrontal cortex on behaviour and aiding the transition to an automatic mode of contro

Computational models of intracellular signalling in cerebellar Purkinje cells

In spite of the regular and well-characterised anatomy of the cerebellum, its function is still not clear. To understand the function of the cerebellum, it is necessary to understand the behaviour of a single cerebellar Purkinje cell. The behaviour of Purkinje cells is determined by their intracellular calcium dynamics, and by the network of intracellular signalling molecules that control the calcium dynamics. The aim of this thesis is to contribute to an understanding of the intracellular signalling network that is linked to the activation of metabotropic glutamate receptors (mGluRs) in a cerebellar Purkinje cell. In the thesis, ten different computational models of the mGluR signalling network are mathematically analysed and numerically integrated. The main result of this thesis is that the mGluR signalling network can implement an adaptive time delay between the activation of the mGluRs by glutamate and the release of calcium from intracellular stores. The adaptation of the time de..

Impairments in motor coordination without major changes in cerebellar plasticity in the Tc1 mouse model of Down syndrome

Down syndrome (DS) is a genetic disorder arising from the presence of a third copy of human chromosome 21 (Hsa21). Recently, O’Doherty et al. [An aneuploid mouse strain carrying human chromosome 21 with Down syndrome phenotypes. Science 309 (2005) 2033–2037] generated a trans-species aneuploid mouse line (Tc1) that carries an almost complete Hsa21. The Tc1 mouse is the most complete animal model for DS currently available. Tc1 mice show many features that relate to human DS, including alterations in memory, synaptic plasticity, cerebellar neuronal number, heart development and mandible size. Because motor deficits are one of the most frequently occurring features of DS, we have undertaken a detailed analysis of motor behaviour in cerebellum-dependent learning tasks that require high motor coordination and balance. In addition, basic electrophysiological properties of cerebellar circuitry and synaptic plasticity have been investigated. Our results reveal that, compared with controls, Tc1 mice exhibit a higher spontaneous locomotor activity, a reduced ability to habituate to their environments, a different gait and major deficits on several measures of motor coordination and balance in the rota rod and static rod tests. Moreover, cerebellar long-term depression is essentially normal in Tc1 mice, with only a slight difference in time course. Our observations provide further evidence that support the validity of the Tc1 mouse as a model for DS, which will help us to provide insights into the causal factors responsible for motor deficits observed in persons with DS