Long-Term Activity-Dependent Plasticity of Action Potential Propagation Delay and Amplitude in Cortical Networks

Background: The precise temporal control of neuronal action potentials is essential for regulating many brain functions. From the viewpoint of a neuron, the specific timings of afferent input from the action potentials of its synaptic partners determines whether or not and when that neuron will fire its own action potential. Tuning such input would provide a powerful mechanism to adjust neuron function and in turn, that of the brain. However, axonal plasticity of action potential timing is counter to conventional notions of stable propagation and to the dominant theories of activity-dependent plasticity focusing on synaptic efficacies. Methodology/Principal Findings: Here we show the occurrence of activity-dependent plasticity of action potentia

Contributions of synaptic filters to models of synaptically stored memory

The question of how neural systems encode memories in one-shot without immediately disrupting previously stored information has puzzled theoretical neuroscientists for years and it is the central topic of this thesis. Previous attempts on this topic, have proposed that synapses probabilistically update in response to plasticity inducing stimuli to effectively delay the degradation of old memories in the face of ongoing memory storage. Indeed, experiments have shown that synapses do not immediately respond to plasticity inducing stimuli, since these must be presented many times before synaptic plasticity is expressed. Such a delay could be due to the stochastic nature of synaptic plasticity or perhaps because induction signals are integrated before overt strength changes occur.The later approach has been previously applied to control fluctuations in neural development by low-pass filtering induction signals before plasticity is expressed. In this thesis we consider memory dynamics in a mathematical model with synapses that integrate plasticity induction signals to a threshold before expressing plasticity. We report novel recall dynamics and considerable improvements in memory lifetimes against a prominent model of synaptically stored memory. With integrating synapses the memory trace initially rises before reaching a maximum and then falls. The memory signal dissociates into separate oblivescence and reminiscence components, with reminiscence initially dominating recall. Furthermore, we find that integrating synapses possess natural timescales that can be used to consider the transition to late-phase plasticity under spaced repetition patterns known to lead to optimal storage conditions. We find that threshold crossing statistics differentiate between massed and spaced memory repetition patterns. However, isolated integrative synapses obtain an insufficient statistical sample to detect the stimulation pattern within a few memory repetitions. We extend the modelto consider the cooperation of well-known intracellular signalling pathways in detecting storage conditions by utilizing the profile of postsynaptic depolarization. We find that neuron wide signalling and local synaptic signals can be combined to detect optimal storage conditions that lead to stable forms of plasticity in a synapse specific manner.These models can be further extended to consider heterosynaptic and neuromodulatory interactions for late-phase plasticity.<br/

Branch-specific plasticity enables self-organization of nonlinear computation in single neurons

It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed in order to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, since most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent STDP and branch-strength potentiation could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that in a simplified setup these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles, and to acquire nonlinear computational capabilities, such as for example the capability to bind multiple input features. Hence our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms

The influence of dopamine on prediction, action and learning

In this thesis I explore functions of the neuromodulator dopamine in the context of autonomous learning and behaviour. I first investigate dopaminergic influence within a simulated agent-based model, demonstrating how modulation of synaptic plasticity can enable reward-mediated learning that is both adaptive and self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment interaction and consequently suggest roles for both complex spontaneous neuronal activity and specific neuroanatomy in the expression of early, exploratory behaviour. I then show how the observed response of dopamine neurons in the mammalian basal ganglia may also be modelled by similar processes involving dopaminergic neuromodulation and cortical spike-pattern representation within an architecture of counteracting excitatory and inhibitory neural pathways, reflecting gross mammalian neuroanatomy. Significantly, I demonstrate how combined modulation of synaptic plasticity and neuronal excitability enables specific (timely) spike-patterns to be recognised and selectively responded to by efferent neural populations, therefore providing a novel spike-timing based implementation of the hypothetical ‘serial-compound’ representation suggested by temporal difference learning. I subsequently discuss more recent work, focused upon modelling those complex spike-patterns observed in cortex. Here, I describe neural features likely to contribute to the expression of such activity and subsequently present novel simulation software allowing for interactive exploration of these factors, in a more comprehensive neural model that implements both dynamical synapses and dopaminergic neuromodulation. I conclude by describing how the work presented ultimately suggests an integrated theory of autonomous learning, in which direct coupling of agent and environment supports a predictive coding mechanism, bootstrapped in early development by a more fundamental process of trial-and-error learning

The Impact of Mild Traumatic Brain injury on Neuronal Networks and Neurobehavior

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this dissertation, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We probed the microarchitecture of an injured cortical circuit subject to two different injury levels, mild stretch (10% peak) and mild/moderate (35%). We found that mild injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, mild/moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology, suggesting a threshold for repair and degradation. The more significant changes in network behavior at moderate stretch are influenced by NMDA receptor activation and subsequent proteolytic changes in the neuronal populations. With the ability to analyze individual neurons in a circuit before and after injury, we identified several biomarkers that confer increased risk or protection from mechanical injury. We found that pre-injury connectivity and NMDA receptor subtype composition (NR2A and NR2B content) are important predictors of node loss and remodeling. Mechanistically, stretch injury caused a reduction in voltage-dependent Mg2+ block of the NR2B-cotaning NMDA receptors, resulting in increased uncorrelated activity both at the single channel and network level. The reduced coincidence detection of the NMDA receptor and overactivation of these receptors further impaired network function and plasticity. Given the demonstrated link between NR2B-NMDARs and mitochondrial dysfunction, we discovered that neuronal de-integration from the network is mediated through mitochondrial signaling. Finally, we bridged these network level studies with an investigation of changes in neurobehavior following blast-induced traumatic brain injury (bTBI), a form of mild TBI. We first developed and validated an open-source toolbox for automating the scoring of several common behavior tasks to study the deficits that occur following bTBI. We then specifically evaluated the role of neuronal transcription factor Elk-1 in mediating deficits following blast by exposing Elk-1 knockout mouse to equivalent blast pressure loading. Our systems-level behavior analysis showed that bTBI creates a complex change in behavior, with an increase in anxiety and loss of habituation in object recognition. Moreover, we found these behavioral deficits were eliminated in Elk-1 knockout animals exposed to blast loading. Together, we merged information from different perspectives (in silico, in vitro, and in vivo) and length scales (single channels, single-cells, networks, and animals) to study the impact of mild traumatic brain injury on neuronal networks and neurobehavior

Investigation of intercostal neuronal intracellular processes and connectivity by signal analysis and computer simulation

Imperial Users onl

Calcium dynamics in dendrites and spines of spiny neurons in the somatosensory ‘barrel’ cortex of the rat

Two-photon excitation fluorescence microscopy was combined with the patch-clamp technique to study the Ca2+ dynamics in dendrites and spines of spiny neurons of layer 4 of the somatosensory cortex in acute thalamocortical brain slices of young (P13-P15) rats. Back-propagating action potentials (bAPs) resulted in a transient rise in Ca2+ in all dendrites and spines tested, representing a global intracellular chemical signal about the activity of the cell. In contrast, synaptically evoked excitatory postsynaptic potentials (EPSPs) resulted in a synapse specific, local increase in Ca2+. Pairing both stimuli at different inter-stimulus intervals revealed a precisely tuned coincidence detection mechanism for pre- and postsynaptic activity, coded in the peak Ca2+ transient amplitude. Linear, sub- and supralinear summation of the Ca2+ transients, depending on the time interval and the order of bAP and EPSP, was found. Ca2+ influx was maximal when the action potential followed synaptic stimulation within less than 20 ms. The mechanism of maximal Ca2+ influx could be explained by the properites of the NMDA receptor channel, which was activated by binding glutamate during synaptic stimulation and subsequent relief of the Mg2+ block by the bAP. Coincidence detection was restricted to the synaptic contact and it did not depend on the distance of the contact from the soma. This temporally and spatially highly restricted coincidence detection mechanism, which emplyed the Ca2+ transient amplitude as a readout signal might serve as an input specific trigger for spike-timing dependent plasticity. Indeed potentiation of EPSPs to 150% of the baseline amplitude could be induced by pairing extracellular stimulation with bAPs within the coincidence detection interval. Reversing the order of the stimuli resulted in depression of the EPSP amplitude to 70%. Thus it was concluded that spiny neurons in layer 4 of the juvenile rat barrel cortex exhibit spike-timing dependent plasticity, which corresponded well to the Ca2+ code used by their spines for coincidence detection

Modulation of Neuronal Signal Transduction and Memory Formation by Synaptic Zinc

The physiological role of synaptic zinc has remained largely enigmatic since its initial detection in hippocampal mossy fibers over 50 years ago. The past few years have witnessed a number of studies highlighting the ability of zinc ions to regulate ion channels and intracellular signaling pathways implicated in neuroplasticity, and others that shed some light on the elusive role of synaptic zinc in learning and memory. Recent behavioral studies using knock-out mice for the synapse-specific zinc transporter ZnT-3 indicate that vesicular zinc is required for the formation of memories dependent on the hippocampus and the amygdala, two brain centers that are prominently innervated by zinc-rich fibers. A common theme emerging from this research is the activity-dependent regulation of the Erk1/2 mitogen-activated-protein kinase pathway by synaptic zinc through diverse mechanisms in neurons. Here we discuss current knowledge on how synaptic zinc may play a role in cognition through its impact on neuronal signaling