Cellular mechanisms underlying burst firing in substantia nigra dopamine neurons

Burst firing of substantia nigra dopamine (SN DA) neurons is believed to represent an important teaching signal that instructs synaptic plasticity and associative learning. However, the mechanisms through which synaptic excitation overcomes the limiting effects of somatic Ca2+-dependent K+ current to generate burst firing are controversial. Modeling studies suggest that synaptic excitation sufficiently amplifies oscillatory dendritic Ca2+ and Na+ channel currents to lead to the initiation of high-frequency firing in SN DA neuron dendrites. To test this model, visually guided compartment-specific patch-clamp recording and ion channel manipulation were applied to rodent SN DA neurons in vitro. As suggested previously, the axon of SN DA neurons was typically found to originate from a large-diameter dendrite that was proximal to the soma. However, in contrast to the predictions of the model, (1) somatic current injection generated firing that was similar in frequency and form to burst firing in vivo, (2) the efficacy of glutamatergic excitation was inversely related to the distance of excitation from the axon, (3) pharmacological blockade or genetic deletion of Ca2+ channels did not prevent high-frequency firing, (4) action potential bursts were invariably detected first at sites that were proximal to the axon, and (5) pharmacological blockade of Na+ channels in the vicinity of the axon/soma but not dendritic excitation impaired burst firing. Together, these data suggest that SN DA neurons integrate their synaptic input in a more conventional manner than was hypothesized previously

Somatic and dendritic GABAB receptors regulate neuronal excitability via different mechanisms

GABAB receptors play a key role in regulating neuronal excitability in the brain. Whereas the impact of somatic GABAB receptors on neuronal excitability has been studied in some detail, much less is known about the role of dendritic GABAB receptors. Here, we investigate the impact of GABAB receptor activation on the somato-dendritic excitability of layer 5 pyramidal neurons in the rat barrel cortex. Activation of GABAB receptors led to hyperpolarization and a decrease in membrane resistance that was greatest at somatic and proximal dendritic locations. These effects were occluded by low concentrations of barium (100 μM), suggesting that they are mediated by potassium channels. In contrast, activation of dendritic GABAB receptors decreased the width of backpropagating action potential (APs) and abolished dendritic calcium electrogenesis, indicating that dendritic GABAB receptors regulate excitability, primarily via inhibition of voltage-dependent calcium channels. These distinct actions of somatic and dendritic GABAB receptors regulated neuronal output in different ways. Activation of somatic GABAB receptors led to a reduction in neuronal output, primarily by increasing the AP rheobase, whereas activation of dendritic GABAB receptors blocked burst firing, decreasing AP output in the absence of a significant change in somatic membrane properties. Taken together, our results show that GABAB receptors regulate somatic and dendritic excitability of cortical pyramidal neurons via different cellular mechanisms. Somatic GABAB receptors activate potassium channels, leading primarily to a subtractive or shunting form of inhibition, whereas dendritic GABAB receptors inhibit dendritic calcium electrogenesis, leading to a reduction in bursting firing.NHMR

Improving Associative Memory in a Network of Spiking Neurons

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory

Neuron as a reward-modulated combinatorial switch and a model of learning behavior

This paper proposes a neuronal circuitry layout and synaptic plasticity principles that allow the (pyramidal) neuron to act as a "combinatorial switch". Namely, the neuron learns to be more prone to generate spikes given those combinations of firing input neurons for which a previous spiking of the neuron had been followed by a positive global reward signal. The reward signal may be mediated by certain modulatory hormones or neurotransmitters, e.g., the dopamine. More generally, a trial-and-error learning paradigm is suggested in which a global reward signal triggers long-term enhancement or weakening of a neuron's spiking response to the preceding neuronal input firing pattern. Thus, rewards provide a feedback pathway that informs neurons whether their spiking was beneficial or detrimental for a particular input combination. The neuron's ability to discern specific combinations of firing input neurons is achieved through a random or predetermined spatial distribution of input synapses on dendrites that creates synaptic clusters that represent various permutations of input neurons. The corresponding dendritic segments, or the enclosed individual spines, are capable of being particularly excited, due to local sigmoidal thresholding involving voltage-gated channel conductances, if the segment's excitatory and absence of inhibitory inputs are temporally coincident. Such nonlinear excitation corresponds to a particular firing combination of input neurons, and it is posited that the excitation strength encodes the combinatorial memory and is regulated by long-term plasticity mechanisms. It is also suggested that the spine calcium influx that may result from the spatiotemporal synaptic input coincidence may cause the spine head actin filaments to undergo mechanical (muscle-like) contraction, with the ensuing cytoskeletal deformation transmitted to the axon initial segment where it may...Comment: Version 5: added computer code in the ancillary files sectio

Statistical physics of neural systems with non-additive dendritic coupling

How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such non-additive dendritic processing on single neuron responses and the performance of associative memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality