137 research outputs found
IST Austria Thesis
Distinguishing between similar experiences is achieved by the brain in a process called pattern separation. In the hippocampus, pattern separation reduces the interference of memories and increases the storage capacity by decorrelating similar inputs patterns of neuronal activity into non-overlapping output firing patterns. Winners-take-all (WTA) mechanism is a theoretical model for pattern separation in which a "winner" cell suppresses the activity of the neighboring neurons through feedback inhibition. However, if the network properties of the dentate gyrus support WTA as a biologically conceivable model remains unknown. Here, we showed that the connectivity rules of PV+interneurons and their synaptic properties are optimizedfor efficient pattern separation. We found using multiple whole-cell in vitrorecordings that PV+interneurons mainly connect to granule cells (GC) through lateral inhibition, a form of feedback inhibition in which a GC inhibits other GCs but not itself through the activation of PV+interneurons. Thus, lateral inhibition between GCâPV+interneurons was ~10 times more abundant than recurrent connections. Furthermore, the GCâPV+interneuron connectivity was more spatially confined but less abundant than PV+interneuronsâGC connectivity, leading to an asymmetrical distribution of excitatory and inhibitory connectivity. Our network model of the dentate gyrus with incorporated real connectivity rules efficiently decorrelates neuronal activity patterns using WTA as the primary mechanism. This process relied on lateral inhibition, fast-signaling properties of PV+interneurons and the asymmetrical distribution of excitatory and inhibitory connectivity. Finally, we found that silencing the activity of PV+interneurons in vivoleads to acute deficits in discrimination between similar environments, suggesting that PV+interneuron networks are necessary for behavioral relevant computations. Our results demonstrate that PV+interneurons possess unique connectivity and fast signaling properties that confer to the dentate gyrus network properties that allow the emergence of pattern separation. Thus, our results contribute to the knowledge of how specific forms of network organization underlie sophisticated types of information processing
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
Dynamic Control of Network Level Information Processing through Cholinergic Modulation
Acetylcholine (ACh) release is a prominent neurochemical marker of arousal state
within the brain. Changes in ACh are associated with changes in neural activity and
information processing, though its exact role and the mechanisms through which it
acts are unknown. Here I show that the dynamic changes in ACh levels that are
associated with arousal state control informational processing functions of networks
through its effects on the degree of Spike-Frequency Adaptation (SFA), an activity
dependent decrease in excitability, synchronizability, and neuronal resonance displayed
by single cells. Using numerical modeling I develop mechanistic explanations
for how control of these properties shift network activity from a stable high frequency
spiking pattern to a traveling wave of activity. This transition mimics the change
in brain dynamics seen between high ACh states, such as waking and Rapid Eye
Movement (REM) sleep, and low ACh states such as Non-REM (NREM) sleep. A
corresponding, and related, transition in network level memory recall is also occurs
as ACh modulates neuronal SFA. When ACh is at its highest levels (waking) all
memories are stably recalled, as ACh is decreased (REM) in the model weakly encoded
memories destabilize while strong memories remain stable. In levels of ACh
that match Slow Wave Sleep (SWS), no encoded memories are stably recalled. This
results from a competition between SFA and excitatory input strength and provides
a mechanism for neural networks to control the representation of underlying synaptic
information. Finally I show that during the low ACh conditions, oscillatory conditions
allow for external inputs to be properly stored in and recalled from synaptic weights. Taken together this work demonstrates that dynamic neuromodulation is
critical for the regulation of information processing tasks in neural networks. These
results suggest that ACh is capable of switching networks between two distinct information
processing modes. Rate coding of information is facilitated during high
ACh conditions and phase coding of information is facilitated during low ACh conditions.
Finally I propose that ACh levels control whether a network is in one of
three functional states: (High ACh; Active waking) optimized for encoding of new
information or the stable representation of relevant memories, (Mid ACh; resting
state or REM) optimized for encoding connections between currently stored memories
or searching the catalog of stored memories, and (Low ACh; NREM) optimized
for renormalization of synaptic strength and memory consolidation. This work provides
a mechanistic insight into the role of dynamic changes in ACh levels for the
encoding, consolidation, and maintenance of memories within the brain.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147503/1/roachjp_1.pd
Functional integration in the cortical neuronal network of conscious and anesthetized animals
General anesthesia consists of amnesia, analgesia, areflexia and unconsciousness. How anesthetics suppress consciousness has been a mystery for more than one and a half centuries.
The overall goal of my research has been to determine the neural correlates of anesthetic-induced loss of consciousness. I hypothesized that anesthetics induce unconsciousness by interfering with the functional connectivity of neuronal networks of the brain and consequently, reducing the brain\u27s capacity for information processing. To test this hypothesis, I performed experiments in which neuronal spiking activity was measured with chronically implanted microelectrode arrays in the visual cortex of freely-moving rats during wakefulness and at graded levels of anesthesia produced by the inhalational anesthetic agent desflurane. I then applied linear and non-parametric information-theoretic analyses to quantify the concentration-dependent effect of general anesthetics on spontaneous and visually evoked spike firing activity in rat primary visual cortex.
Results suggest that desflurane anesthesia disrupts cortical neuronal integration as measured by monosynaptic connectivity, spike burst coherence and information capacity. This research furthers our understanding of the mechanisms involved with the anesthetic-induced LOC which may facilitate in the development of better anesthetic monitoring devices and the creation of effective anesthetic agents that will be free of unwanted side effects
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
âserial-compoundâ 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
Identifying Network Correlates of Memory Consolidation
Neuronal spiking activity carries information about our experiences in the waking world but exactly how the brain can quickly and efficiently encode sensory information into a useful neural code and then subsequently consolidate that information into memory remains a mystery. While neuronal networks are known to play a vital role in these processes, detangling the properties of network activity from the complex spiking dynamics observed is a formidable challenge, requiring collaborations across scientific disciplines. In this work, I outline my contributions in computational modeling and data analysis toward understanding how network dynamics facilitate memory consolidation. For experimental perspective, I investigate hippocampal recordings of mice that are subjected to contextual fear conditioning and subsequently undergo sleep-dependent fear memory consolidation. First, I outline the development of a functional connectivity algorithm which rapidly and robustly assesses network structure based on neuronal spike timing. I show that the relative stability of these functional networks can be used to identify global network dynamics, revealing that an increase in functional network stability correlates with successful fear memory consolidation in vivo. Using an attractor-based model to simulate memory encoding and consolidation, I go on to show that dynamics associated with a second-order phase transition, at a critical point in phase-space, are necessary for recruiting additional neurons into network dynamics associated with memory consolidation. I show that successful consolidation subsequently shifts dynamics away from a critical point and towards sub-critical dynamics. Investigations of in vivo spiking dynamics likewise revealed that hippocampal dynamics during non-rapid-eye-movement (NREM) sleep show features of being near a critical point and that fear memory consolidation leads to a shift in dynamics. Finally, I investigate the role of NREM sleep in facilitating memory consolidation using a conductance-based model of neuronal activity that can easily switch between modes of activity loosely representing waking and NREM sleep. Analysis of model simulations revealed that oscillations associated with NREM sleep promote a phase-based coding of information; neurons with high firing rates during periods of wake lead spiking activity during NREM oscillations. I show that when phase-coding is active in both simulations and in vivo, synaptic plasticity selectively strengthens the input to neurons firing late in the oscillation while simultaneously reducing input to neurons firing early in the oscillation. The effect is a net homogenization of firing rates observed in multiple other studies, and subsequently leads to recruitment of new neurons into a memory engram and information transfer from fast firing neurons to slow firing neurons. Taken together, my work outlines important, newly-discovered features of neuronal network dynamics related to memory encoding and consolidation: networks near criticality promote recruitment of additional neurons into stable firing patterns through NREM-associated oscillations and subsequently consolidates information into memories through phase-based coding.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162991/1/qmskill_1.pd
Metastability: an emergent phenomenon in networks of spiking neurons
It is widely recognised that different brain areas perform different specialised functions. However, it remains an open question how different brain areas coordinate with each other and give rise to global brain states and high-level cognition. Recent theories suggest that transient periods of synchronisation and desynchronisation provide a mechanism for dynamically integrating and forming coalitions of functionally related neural areas, and that at these times conditions are optimal for information transfer. Empirical evidence from human resting state networks has shown a tendency for multiple brain areas to synchronise for short amounts of time, and for different synchronous groups to appear at different times. In dynamical systems terms, this behaviour resembles metastability â an intrinsically driven movement between transient, attractor-like states. However, it remains an open question what the underlying mechanism is that gives rise to these observed phenomena.
The thesis first establishes that oscillating neural populations display a great amount of spectral complexity, with several rhythms temporally coexisting in the same and different structures. The thesis next explores inter-band frequency modulation between neural oscillators. The results show that oscillations in different neural populations, and in different frequency bands, modulate each other so as to change frequency. Further to this, the interaction of these fluctuating frequencies in the network as a whole is able to drive different neural populations towards episodes of synchrony.
Finally, a symbiotic relationship between metastability and underlying network structure is elucidated, in which the presence of plasticity, responding to the interactions between different neural areas, will naturally form modular small-world networks that in turn further promote metastability. This seemingly inevitable drive towards metastabilty in simulation suggests that it should also be present in biological brains. The conclusion drawn is that these key network characteristics, and the metastable dynamics they promote, facilitate versatile exploration, integration, and communication between functionally related neural areas, and thereby support sophisticated cognitive processing in the brain.Open Acces
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
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