4,017 research outputs found
Nanodiamonds-induced effects on neuronal firing of mouse hippocampal microcircuits
Fluorescent nanodiamonds (FND) are carbon-based nanomaterials that can
efficiently incorporate optically active photoluminescent centers such as the
nitrogen-vacancy complex, thus making them promising candidates as optical
biolabels and drug-delivery agents. FNDs exhibit bright fluorescence without
photobleaching combined with high uptake rate and low cytotoxicity. Focusing on
FNDs interference with neuronal function, here we examined their effect on
cultured hippocampal neurons, monitoring the whole network development as well
as the electrophysiological properties of single neurons. We observed that FNDs
drastically decreased the frequency of inhibitory (from 1.81 Hz to 0.86 Hz) and
excitatory (from 1.61 Hz to 0.68 Hz) miniature postsynaptic currents, and
consistently reduced action potential (AP) firing frequency (by 36%), as
measured by microelectrode arrays. On the contrary, bursts synchronization was
preserved, as well as the amplitude of spontaneous inhibitory and excitatory
events. Current-clamp recordings revealed that the ratio of neurons responding
with AP trains of high-frequency (fast-spiking) versus neurons responding with
trains of low-frequency (slow-spiking) was unaltered, suggesting that FNDs
exerted a comparable action on neuronal subpopulations. At the single cell
level, rapid onset of the somatic AP ("kink") was drastically reduced in
FND-treated neurons, suggesting a reduced contribution of axonal and dendritic
components while preserving neuronal excitability.Comment: 34 pages, 9 figure
Spike Code Flow in Cultured Neuronal Networks
We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network
Emergence of Spatio-Temporal Pattern Formation and Information Processing in the Brain.
The spatio-temporal patterns of neuronal activity are thought to underlie cognitive functions, such as our thoughts, perceptions, and emotions. Neurons and glial cells, specifically astrocytes, are interconnected in complex networks, where large-scale dynamical patterns emerge from local chemical and electrical signaling between individual network components. How these emergent patterns form and encode for information is the focus of this dissertation. I investigate how various mechanisms that can coordinate collections of neurons in their patterns of activity can potentially cause the interactions across spatial and temporal scales, which are necessary for emergent macroscopic phenomena to arise.
My work explores the coordination of network dynamics through pattern formation and synchrony in both experiments and simulations. I concentrate on two potential mechanisms: astrocyte signaling and neuronal resonance properties. Due to their ability to modulate neurons, we investigate the role of astrocytic networks as a potential source for coordinating neuronal assemblies. In cultured networks, I image patterns of calcium signaling between astrocytes, and reproduce observed properties of the network calcium patterning and perturbations with a simple model that incorporates the mechanisms of astrocyte communication. Understanding the modes of communication in astrocyte networks and how they form spatial temporal patterns of their calcium dynamics is important to understanding their interaction with neuronal networks. We investigate this interaction between networks and how glial cells modulate neuronal dynamics through microelectrode array measurements of neuronal network dynamics. We quantify the spontaneous electrical activity patterns of neurons and show the effect of glia on the neuronal dynamics and synchrony.
Through a computational approach I investigate an entirely different theoretical mechanism for coordinating ensembles of neurons. I show in a computational model how biophysical resonance shifts in individual neurons can interact with the network topology to influence pattern formation and separation. I show that sub-threshold neuronal depolarization, potentially from astrocytic modulation among other sources, can shift neurons into and out of resonance with specific bands of existing extracellular oscillations. This can act as a dynamic readout mechanism during information storage and retrieval. Exploring these mechanisms that facilitate emergence are necessary for understanding information processing in the brain.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111493/1/lshtrah_1.pd
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation.
Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted
Contrastive Hebbian Learning with Random Feedback Weights
Neural networks are commonly trained to make predictions through learning
algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by
gradient backpropagation, is based on Hebb's rule and the contrastive
divergence algorithm. It operates in two phases, the forward (or free) phase,
where the data are fed to the network, and a backward (or clamped) phase, where
the target signals are clamped to the output layer of the network and the
feedback signals are transformed through the transpose synaptic weight
matrices. This implies symmetries at the synaptic level, for which there is no
evidence in the brain. In this work, we propose a new variant of the algorithm,
called random contrastive Hebbian learning, which does not rely on any synaptic
weights symmetries. Instead, it uses random matrices to transform the feedback
signals during the clamped phase, and the neural dynamics are described by
first order non-linear differential equations. The algorithm is experimentally
verified by solving a Boolean logic task, classification tasks (handwritten
digits and letters), and an autoencoding task. This article also shows how the
parameters affect learning, especially the random matrices. We use the
pseudospectra analysis to investigate further how random matrices impact the
learning process. Finally, we discuss the biological plausibility of the
proposed algorithm, and how it can give rise to better computational models for
learning
Learning intrinsic excitability in medium spiny neurons
We present an unsupervised, local activation-dependent learning rule for
intrinsic plasticity (IP) which affects the composition of ion channel
conductances for single neurons in a use-dependent way. We use a
single-compartment conductance-based model for medium spiny striatal neurons in
order to show the effects of parametrization of individual ion channels on the
neuronal activation function. We show that parameter changes within the
physiological ranges are sufficient to create an ensemble of neurons with
significantly different activation functions. We emphasize that the effects of
intrinsic neuronal variability on spiking behavior require a distributed mode
of synaptic input and can be eliminated by strongly correlated input. We show
how variability and adaptivity in ion channel conductances can be utilized to
store patterns without an additional contribution by synaptic plasticity (SP).
The adaptation of the spike response may result in either "positive" or
"negative" pattern learning. However, read-out of stored information depends on
a distributed pattern of synaptic activity to let intrinsic variability
determine spike response. We briefly discuss the implications of this
conditional memory on learning and addiction.Comment: 20 pages, 8 figure
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