Signaling in largescale neural networks

Authors: Rune W Berg Jørn Hounsgaard

Publish Date: 2008/11/14

Volume: 10, Issue: 1, Pages: 9-

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Abstract

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neuronsIt seems that behavioral diversity and flexibility scale with the number of interconnected neurons in nervous systems For this reason alone the relation between behavior and the properties and activity of individual neurons becomes increasingly intractable in larger brains Furthermore recent decades have shown that the response properties of neurons are dynamic highly nonlinear and unique for each cell type in the brain A general theory on how to bridge the gap between cell signaling at the microscopic level and function at the macroscopic level in largescale networks is not in sight Here we explore basic questions that may guide diagnostic experimentation aimed at the formation of theoretical insights beyond case by case numerical modelingSensory afferents and motor efferents provide strategic anchors for experimentation on nervous systems because their activity is uniquely decodable by means of external sensory and motor reference frames For this reason our focus is on networks that perform sensorymotor transduction The possible function and relative contribution of synaptic and intrinsic conductances to cell firing during functional network activity will figure in the discussion of each of the questions posedSensorymotor transduction is a fundamental process in living organisms from protozoan eukaryotes and onwards Banchetti 2005 Motor behavior probably constituted the key evolutionary drive for the differentiation of nervous systems Llinás 2000 Early nerve cells may have emerged as isolated multifunctional sensory motor and secretory ‘allinone’ cells derived from the epithelium in Cnidarians more than 700 millions years ago Lentz 1968 MiljkovicLicina et al 2004 Westfall 1996 From these origins interneurons evolved to become mediators between specific sensory cells and specialized effector cells such as nematocysts and muscles Organized in nonpolarized nerve nets interneurons could mediate integrative sensing of environmental information so that appropriate behaviors followed Satterlie and Nolen 2001 However in becoming abstract signaling devices the interneurons also gave rise to the fundamental coding problem still inherent in the study of nervous systems information is carried and processed by molecular machineries in individual nerve cells while function is the collective sum of the activity of interacting neurons organized in networks The gap between the constituents and the functional whole is aggravated in largescale networks because neurons receive signals from a large number of other neurons For this reason the activity of individual neurons is rarely directly relatable to singular events in other neurons or in the outside world Because neurons process synaptic input and reduce information it is impossible to reconstruct their input patterns entirely from their output In addition it is practically never possible to record all the presynaptic input patterns that give rise to particular output in a neuron Even worse the information coded by these input patterns will usually not themselves be decodable For all these reasons the coding that real neurons perform is not well understood In fact the relation between signaling and coding in neurons and function at the network level is one of the hard problems in neuroscience Nevertheless there is much to be said about synaptic transduction in individual neurons Intercalated between the synaptic currents and spike generation are active nonlinear filters provided by current generating voltage gated ion channels Llinas 1988 Since nerve cells differ in the kind density and distribution of the ion channels that they express they also differ in the way incoming information is processed to spike patterns Awareness of this active filter function and its potential significance spurred a flurry of experimentation to characterize the nonlinear properties their underlying mechanisms and functions in neurons in a range of networks and organisms Llinas 1988 London and Hausser 2005 Selverston and Moulins 1985 ToledoRodriguez et al 2005 The intrinsic response properties were seen to provide a bias that favored certain spike patterns over others particularly time varying activity like oscillations Llinas 1988 The mix of voltage sensitive ion channels in each cell would be well suited to reduce the problem of pattern formation by funneling a wide range of noisy synaptic input into a few preselected ‘useful’ output patterns provided by the intrinsic response properties Hounsgaard and Midtgaard 1989 Midtgaard 1989 The role of intrinsic response properties of individual neurons in the formation of ‘useful’ activity patterns in networks of neurons is well supported by experimental evidence from oligocellular circuits with the stomastogastric ganglion as a prime example Marder and Bucher 2007 Even in larger functional circuits intrinsic response properties of neurons are thought to play a key role in formation of time scale dynamics and coherence Grillner 2003 Llinas 1988We have seen that neurons in all nervous systems are equipped with nonlinear intrinsic response properties Gated by voltage the underlying ion channels interact to produce dynamic transmembrane currents The resulting variations in membrane potential are continuous in time smoothened by the large number of contributing channels and by membrane capacitance Therefore perturbations in transmembrane current in isolated neurons will produce smooth changes in membrane potential and firing rate However during behavior neurons often display highly irregular firing rates Knierim and van Essen 1992 Newsome et al 1989 Zoccolan et al 2002 Firing with a high coefficient of variation is not easily accounted for by a smoothly varying membrane potential or by integration of excitatory synaptic potentials Koch 1999 Softky and Koch 1993 On the other hand uncorrelated interspike intervals automatically emerge from ‘noisy’ fluctuations in membrane potential Calvin and Stevens 1967 that readily arise from randomly elicited excitatory and inhibitory synaptic potentials Gerstein and Mandelbrot 1964 Softky and Koch 1993 Behavioral states in which neurons receive mixed inhibitory and excitatory signals have now been reported in the cerebral cortex Marino et al 2005 Steriade et al 2001 and in the spinal cord Berg et al 2007 Concurrent increase in inhibition and excitation leads to a dramatic increase in average conductance even for a moderate depolarization from the resting membrane potential Berg et al 2007 Destexhe et al 2003 Haider et al 2006 Marino et al 2005 High conductance states by balanced increase in inhibition and excitation are characterized by a dramatic increase in the amplitude and power spectrum of membrane potential fluctuations due to high density of uncorrelated inhibitory and excitatory synaptic conductances Berg et al 2007 It is these fluctuations that lead to irregular firing rates Further support for a widespread occurrence of high conductance states during functional network activity is their association with the UPstates proposed to be the active state of neurons during behavior Destexhe et al 2007 2003 Haider et al 2006The essentials of network dynamics are synaptic interactions between the constituent neurons Synaptic input in neurons can be modeled as synaptic current or synaptic conductance for a review see Burkitt 2006 The resulting network models are generally referred to as current based and conductance based respectively In the latter case the change in driving force and input resistance is taken into account while these are assumed constant in the currentbased network Explicitly written the dynamical equations for the membrane potential in a onecompartment model areNeurons in two types of networks a Lowintensity network left few active neurons result in low synaptic conductance and therefore the neurons express a range of complex intrinsic response properties as indicated by colored cell bodies Right typical activity pattern of a single neuron embedded in such a network Synaptic input is of low intensity and spiking regular and largely determined by the intrinsic properties of the cell itself b High intensity network the inhibitory and excitatory neurons black and gray project to many other neurons and receive intense stochastic input Left Right the activity of a typical neuron is irregular due to the fluctuating intense synaptic input This intense input causes a substantial decrease in input resistance and therefore shunts the slow intrinsic properties so the different cells diminish their individuality illustrated as lack of color of cell bodyNetwork reliability By having many intensely interacting neurons each individual neuron makes an insignificant contribution This implies a reliable probabilistic type of coding that is distributed over many neurons Shadlen and Newsome 1998 One of the consequences of this large population coding is that the total synaptic conductance becomes high and stochastic on short time scales

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