Controlling Effects of Astrocyte on Neuron Behavior in Tripartite Synapse Using VHDL–AMS

Astrocyte cells form the largest cell population in the brain and can influence neuron behavior. These cells provide appropriate feedback control in regulating neuronal activities in the Central Nervous System (CNS). This paper presents a set of equations as a model to describe the interactions between neurons and astrocyte. A VHDL–AMS-based tripartite synapse model that includes a pre-synaptic neuron, the synaptic terminal, a post-synaptic neuron, and an astrocyte cell is presented. In this model, the astrocyte acts as a controller module for neurons and can regulates the spiking activity of them. Simulation results show that by regulating the coupling coefficients of astrocytes, spiking frequency of neurons can be reduced and the activity of neuronal cells is modulated.

Effects of memristive synapse radiation interactions on learning in spiking neural networks

This study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

Sleep-like behavior is a fundamental property of the tripartite synapse

The tripartite synapse, consisting of the presynaptic neuron, post-synaptic neuron, and an astrocyte, is considered to be the main locus of signaling between neurons in the brain.1,2 Neurotransmission is energetically very expensive3,4, and the primary neurotransmitter utilized for signaling is glutamate. It has been found that glutamate is also used as a substrate for energy generation.5,6 However, it is unclear what the relationship is between energy generation and availability of neurotransmitter during glutamatergic neurotransmission. Here we show that availability of energy, represented by adenosine triphosphate (ATP), and glutamate for neurotransmission are intimately related, and in fact determine the ability to signal at the tripartite synapse. Using a novel neurochemical mathematical model of the tripartite synapse, we found that glutamate concentrations for neurotransmission and ATP concentrations were interdependent, and their interplay controlled the firing pattern of the presynaptic terminal, as defined by synaptic vesicle release. Furthermore, we found that depending on the parameters chosen in the model, the tripartite synapse demonstrated behavior with limit cycles, alternating between high- and low-frequency firing rates. Our results show that complex behavior with high- and low-activity states, qualitatively meeting the characteristics of sleep7 emerges directly from the nature of the tripartite synapse, with glutamate and ATP concentrations serving as the signals for state changes. We anticipate that our model will serve as a starting point to further elucidate the energetics of neuronal and brain functioning, and eventually shed light on the fundamental question of the nature and necessity of sleep.

Multiplexing rhythmic information by spike timing dependent plasticity

Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto a single post-synaptic neuron. Each sub-population was assumed to oscillate in an independent manner and in a different frequency band. To investigate the STDP dynamics, a mean field Fokker-Planck theory was developed in the limit of the slow learning rate. Surprisingly, our theory predicted limited interactions between the different sub-groups. Our analysis further revealed that the interaction between these channels was mainly mediated by the shared component of the mean activity. Next, we generalized these results beyond the simplistic model using numerical simulations. We found that for a wide range of parameters, the system converged to a solution in which the post-synaptic neuron responded to both rhythms. Nevertheless, all the synaptic weights remained dynamic and did not converge to a fixed point. These findings imply that STDP can support the multiplexing of rhythmic information, and demonstrate how functionality (multiplexing of information) can be retained in the face of continuous remodeling of all the synaptic weights.

On the distribution and function of synaptic clusters in dendrites

Local non-linearities in dendrites render neuronal output dependent on the spatial distribution of synapses. A neuron will activate differently depending on whether active synapses are spatially clustered or dispersed. While this sensitivity can in principle expand neuronal computational capacity, it has thus far been employed in very few learning paradigms. To make use of this sensitivity, groups of correlated neurons need to make contact with distinct dendrites, and this requires a mechanism to ensure the correct distribution of synapses contacting from distinct ensembles. To address this problem, we introduce the requirement that on a short time scale, a pre-synaptic neuron makes a constant number of synapses with the same strength on a post-synaptic neuron. We find that this property enables clusters to distribute correctly and guarantees their functionality. Furthermore, we demonstrate that a change in the input statistics can reshape the spatial distribution of synapses. Finally, we show under which conditions clusters do not distribute correctly, e.g. when cross-talk between dendrites is too strong. As well as providing insight into potential biological mechanisms of learning, this work paves the way for new learning algorithms for artificial neural networks that exploit the spatial distribution of synapses.

Expression of the unc-4 homeoprotein in Caenorhabditis elegans motor neurons specifies presynaptic input

In the nematode, Caenorhabditis elegans, VA and VB motor neurons arise from a common precursor cell but adopt different morphologies and synapse with separate sets of interneurons in the ventral nerve cord. A mutation that inactivates the unc-4 homeodomain gene causes VA motor neurons to assume the VB pattern of synaptic input while retaining normal axonal polarity and output; the disconnection of VA motor neurons from their usual presynaptic partners blocks backward locomotion. We show that expression of a functional unc-4-beta-galactosidase chimeric protein in VA motor neurons restores wild-type movement to an unc-4 mutant. We propose that unc-4 controls a differentiated characteristic of the VA motor neurons that distinguishes them from their VB sisters, thus dictating recognition by the appropriate interneurons. Our results show that synaptic choice can be controlled at the level of transcription in the post-synaptic neuron and identify a homeoprotein that defines a subset of cell-specific traits required for this choice.

Bromocriptine and the Clinical Spectrum of Parkinson's Disease

ABSTRACT:As the direct agonist with the widest clinical use, bromocriptine provides a unique window into the clinical spectrum of Parkinson's disease. The efficacy of bromocriptine for therapy of de novo Parkinson's disease has recently been confirmed using a double-blind design with L-Dopa (Sinemet). Over a period of 5.5 months, bromocriptine was found to be as effective as L-Dopa in reducing the functional and neurological disability of Parkinson's disease. This study complements others and demonstrates a role for bromocriptine as de novo therapy. A longitudinal study comparing bromocriptine with L-Dopa is underway, but previous observations with bromocriptine suggest modest, transient beneficial effects with significantly less fluctuation of disability and less dyskinesia when used alone or in combination with L-Dopa. The transient benefits of bromocriptine on progressive disability suggest that both pre-and post-synaptic defects are eventually involved in Parkinson's disease. While agonists with improved efficacy and minimal side effects are required for symptomatic treatment of Parkinson's disease, strategies to protect pre- and post-synaptic neuron populations against progressive dysfunction must be developed.