No description available.

On short timescales, the brain represents, transmits, and processes information through the electrical activity of its neurons. On long timescales, the brain stores information in the strength of the synaptic connections between its neurons. This thesis examines the surprising implications of two separate, well documented microscopic processes — the stochastic gating of ion channels and the plasticity of dendritic spines — for neural information processing and storage. Electrical activity in neurons is mediated by many small membrane proteins called ion channels. Although single ion channels are known to open and close stochastically, the macroscopic behaviour of populations of ion channels are often approximated as deterministic. This is based on the assumption that the intrinsic noise introduced by stochastic ion channel gating is so weak as to be negligible. In this study we take advantage of newly developed efficient computer simulation methods to examine cases where this assumption breaks down. We find that ion channel noise can mediate spontaneous action potential firing in small nerve fibres, and explore its possible implications for neuropathic pain disorders of peripheral nerves. We then characterise the magnitude of ion channel noise for single neurons in the central nervous system, and demonstrate through simulation that channel noise is sufficient to corrupt synaptic integration, spike timing and spike reliability in dendritic neurons. The second topic concerns neural information storage. Learning and memory in the brain has long been believed to be mediated by changes in the strengths of synaptic connections between neurons — a phenomenon termed synaptic plasticity. Most excitatory synapses in the brain are hosted on small membrane structures called dendritic spines, and plasticity of these synapses is dependent on calcium concentration changes within the dendritic spine. In the last decade, it has become clear that spines are highly dynamic structures that appear and disappear, and can shrink and enlarge on rapid timescales. It is also clear that this spine structural plasticity is intimately linked to synaptic plasticity. Small spines host weak synapses, and large spines host strong synapses. Because spine size is one factor which determines synaptic calcium concentration, it is likely that spine structural plasticity influences the rules of synaptic plasticity. We theoretically study the consequences of this observation, and find that different spine-size to synaptic-strength relationships can lead to qualitative differences in long-term synaptic strength dynamics and information storage. This novel theory unifies much existing disparate data, including the unimodal distribution of synaptic strength, the saturation of synaptic plasticity, and the stability of strong synapses.

by Wei-Zai Shen. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (p. 170-193) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

Neurons are constantly integrating information from external and internal sources, causing them to spike at particular times. The exact timing of spikes is determined by a neuron's intrinsic properties, as well as the interplay between local excitatory and inhibitory inputs. Although inhibitory interneurons have been extensively studied, their contribution to neuronal integration and spike timing remains poorly understood. To elucidate the functional role of GABAergic interneurons during cortical activity, we combined molecular identification of interneurons, two photon imaging and electrophysiological recordings in mouse thalamocortical slices. In this preparation, cortical UP states, a network state characterized by prolonged periods of depolarization and synchronized spiking, can be evoked by thalamic stimulation and can also occur spontaneously. To assay the role of inhibition, we first characterized the firing properties of Parvalbumin (PV) and Somatostatin (SOM) interneurons during UP states activity, and found a higher probability and rate of spiking in these two subtypes compared to excitatory cells. These subtypes did not display differential timing of activation during the evoked response. Furthermore, calcium imaging showed low correlations among PV and SOM interneurons, indicating that neurons sharing these neurochemical markers do not coordinate their firing. Intracellular recordings confirmed that nearby interneurons, known to be electrically coupled, do not display more synchronous spiking than excitatory cells, suggesting that this coupling may not function to synchronize the activity of interneurons on fast time scales¬¬¬. After characterizing inhibitory interneuron outputs, we next studied the timing and correlation of inhibitory inputs, which we isolated from excitatory inputs by voltage clamping at the reversal for excitation (0mV) or inhibition (-70mV). In both thalamically triggered and spontaneous activations, IPSCs between cell pairs were remarkably well correlated, with correlation coefficients reaching over .9 in some cases. This high degree of correlation has previously been assumed to be due to interneuron synchrony, but our population imaging and paired recordings did not support this view. In addition, we found that the connection rate between interneurons is very high (~80%), and quantal analysis revealed that each IPSC recorded in neighboring cells during an UP state could be due to a single presynaptic interneuron. Therefore, we explain the high IPSCs correlations in nearby pyramidal cells are emerging from the common input from individual interneurons, rather than from synchronization of interneuron activity across the population. In a final set of experiments, we found that a partial pharmacological block of inhibitory signaling increased EPSC correlations. Our data support a model in which inhibitory neurons do not fire in a correlated fashion but have strong, dense connections to pyramidal neurons that serve to prevent local excitatory synchrony during UP states. This would mean that inhibition may not, as previously thought, serve to synchronize the firing of excitatory cells, but have precisely the opposite effect, decorrelating their activity by breaking down their coordinated firing. This is consistent with the hypothesis that pyramidal cells are carrying out an essentially integrative function in the circuit and that interneurons expand the temporal dynamic range of this integration.

A fundamental challenge of neuroscience is to understand how interconnected populations of neurons give rise to the remarkable computational abilities of our brains. Large neural datasets offer promise, but they are perilous: they are too complex to be studied with traditional single-neuron analyses, and thus require new analyses that can uncover structure at the level of the population. However, since these analyses operate on large datasets, our intuition whether structure is significant breaks down. Hence, we run the risk of over-interpreting structure from the population data that may have a simple explanation. Thus, with population analysis methods, there is also a need for methods that can validate the significance of structure identified. In this dissertation, I discuss topics covering both the identification and the validation of structure in population data. In the first part, I discuss novel methods for uncovering the computational strategy employed by the motor cortex to flexibly switch between different neural computations. I demonstrate that collective activity patterns of motor cortex neurons related to different computations are orthogonal yet can still be linked, indicating a degree of flexibility that was not displayed or predicted by existing cortical models. In the second part, I discuss a novel analytical framework to rigorously test the novelty of population-level findings, given a specified set of primary features such as correlations across time, neurons and experimental conditions. This framework provides a general tool for validating population findings across the brain and across population-level hypotheses.

No description available.

Direct cortico-motoneuronal (CM) connections of corticospinal tract neurons are a distinctive feature of the primate motor system which are known to be important for the capacity to perform independent finger movements. However, it is still unclear how the appropriate combinations of CM cells are recruited to produce the selective (fractionated) control over muscles of the upper limb that is necessary for independent finger movements. I have investigated whether GABAergic intracortical inhibitory (ICI) circuits in human motor cortex contribute to the selection of the appropriate CM cells during a motor task requiring selective activation of one of several intrinsic hand muscles. Behaviour of ICI circuits during voluntary contraction was compared for the dominant and non-dominant hemisphere of right-handed subjects, as hemispheric differences in ICI may contribute to preferential use of the right hand for fine motor tasks. Finally, I investigated the range of forces over which ICI contributes to selective activation of a hand muscle. Neurologically normal adult human subjects were recruited for all experiments. Surface electrodes recorded electromyographic activity of abductor pollicis brevis (APB), first dorsal interosseous and abductor digiti minimi muscles during controlled isometric contractions of APB at different force levels while subjects attempted to keep the other two muscles relaxed using visual feedback of EMG. Paired-pulse transcranial magnetic stimulation (TMS) was used to assess ICI at rest and during selective activation of a hand muscle. TMS intensity and interstimulus interval were varied in different trials. Data were compared for two different directions of induced current in the brain; posteriorly directed current (PA stimulation) and anteriorly directed current (AP stimulation). ICI is suppressed for corticospinal neurons controlling the muscle targeted for selective activation; no change in ICI was seen for corticospinal neurons controlling the muscles required to be relaxed. This indicates that differential modulation of ICI in human motor cortex contributes to selective activation of a hand muscle. The direction of current flow induced in the brain proved to be critical for demonstrating this effect. It was observed with AP stimulation but not PA stimulation. I argue that this is due to preferential activation by PA stimulation of interneurons producing I1 waves in corticospinal neurons. These interneurons are not acted upon by ICI circuits. This problem makes the conventional PA paired-pulse TMS technique unreliable for the assessment of ICI during voluntary contraction. With AP stimulation it was demonstrated that ICI is not modulated during weak selective activation of a hand muscle (&lt5percent of maximal voluntary contraction), but ICI effects on CM cells controlling the target muscle are progressively suppressed at higher levels of activation. The present study is the first to examine hemispheric differences in ICI during selective isometric contraction of an intrinsic hand muscle. No hemispheric differences were observed. These studies have demonstrated a functional role for ICI in fractionation of hand muscle activity in normal subjects. It also provides an improved basis for investigating the changes in ICI with TMS in various neurological conditions in which it has been reported that GABAergic inhibition is abnormal. / Thesis (Ph.D.)--School of Molecular and Biochemical Science, 2004.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 215-232). Also available in electronic version.

Thesis (Ph. D.)--University of Chicago, Department of Neurobiology, Pharmacology, and Physiology, 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Biology." Includes bibliographical references (p. 97-119).