665 research outputs found
Communication through coherence in a realistic neuronal model
The Communication Through Coherence (CTC) theory establishes that neural communication is much effective if the underlying oscillatory activity of both populations are phase locked, that is, the input from the emitting population arrives at the peak of excitability of the receiving neural network. To study this setting, we consider a novel population rate model, which provides an exact description of the macroscopic activity of a network, and perturb it with a periodic function, modelling the input. We study analytical and numerically the emerging phase-locked states using tools from dynamical systems
Optimal control of oscillatory neuronal models with applications to communication through coherence
Macroscopic oscillations in the brain are involved in various cognitive and
physiological processes, yet their precise function is not not completely
understood. Communication Through Coherence (CTC) theory proposes that these
rhythmic electrical patterns might serve to regulate the information flow
between neural populations. Thus, to communicate effectively, neural
populations must synchronize their oscillatory activity, ensuring that input
volleys from the presynaptic population reach the postsynaptic one at its
maximum phase of excitability. We consider an Excitatory-Inhibitory (E-I)
network whose macroscopic activity is described by an exact mean-field model.
The E-I network receives periodic inputs from either one or two external
sources, for which effective communication will not be achieved in the absence
of control. We explore strategies based on optimal control theory for
phase-amplitude dynamics to design a control that sets the target population in
the optimal phase to synchronize its activity with a specific presynaptic input
signal and establish communication. The control mechanism resembles the role of
a higher cortical area in the context of selective attention. To design the
control, we use the phase-amplitude reduction of a limit cycle and leverage
recent developments in this field in order to find the most effective control
strategy regarding a defined cost function. Furthermore, we present results
that guarantee the local controllability of the system close to the limit
cycle
Mechanisms for Phase Shifting in Cortical Networks and their Role in Communication through Coherence
In the primate visual cortex, the phase of spikes relative to oscillations in the local field potential (LFP) in the gamma frequency range (30–80 Hz) can be shifted by stimulus features such as orientation and thus the phase may carry information about stimulus identity. According to the principle of communication through coherence (CTC), the relative LFP phase between the LFPs in the sending and receiving circuits affects the effectiveness of the transmission. CTC predicts that phase shifting can be used for stimulus selection. We review and investigate phase shifting in models of periodically driven single neurons and compare it with phase shifting in models of cortical networks. In a single neuron, as the driving current is increased, the spike phase varies systematically while the firing rate remains constant. In a network model of reciprocally connected excitatory (E) and inhibitory (I) cells phase shifting occurs in response to both injection of constant depolarizing currents and to brief pulses to I cells. These simple models provide an account for phase-shifting observed experimentally and suggest a mechanism for implementing CTC. We discuss how this hypothesis can be tested experimentally using optogenetic techniques
Assessing Neuronal Synchrony and Brain Function Through Local Field Potential and Spike Analysis
Studies of neuronal network oscillations and rhythmic neuronal synchronization have led to a number of important insights in recent years, giving us a better understanding of the temporal organization of neuronal activity related to essential brain functions like sensory processing and cognition. Important principles and theories have emerged from these findings, including the communication through coherence hypothesis, which proposes that synchronous oscillations render neuronal communication effective, selective, and precise. The implications of such a theory may be universal for brain function, as the determinants of neuronal communication inextricably shape the neuronal representation of information in the brain. However, the study of communication through coherence is still relatively young. Since its articulation in 2005, the theory has predominantly been applied to assess cortical function and its communication with downstream targets in different sensory and behavioral conditions. The results herein are intended to bolster this hypothesis and explore new ways in which oscillations coordinate neuronal communication in distributed regions. This includes the development of new analytic tools for interpreting electrophysiological patterns, inspired by phase synchronization and spike train analysis. These tools aim to offer fast results with clear statistical and physiological interpretation
Biologically plausible attractor networks
Attractor networks have shownmuch promise as a neural network architecture
that can describe many aspects of brain function. Much of the field of study
around these networks has coalesced around pioneering work done by John
Hoprield, and therefore many approaches have been strongly linked to the field
of statistical physics. In this thesis I use existing theoretical and statistical notions
of attractor networks, and introduce several biologically inspired extensions
to an attractor network for which a mean-field solution has been previously
derived. This attractor network is a computational neuroscience model
that accounts for decision-making in the situation of two competing stimuli.
By basing our simulation studies on such a network, we are able to study situations where mean-
field solutions have been derived, and use these as the starting
case, which we then extend with large scale integrate-and-fire attractor network
simulations. The simulations are large enough to provide evidence that the results
apply to networks of the size found in the brain. One factor that has been
highlighted by previous research to be very important to brain function is that
of noise. Spiking-related noise is seen to be a factor that influences processes
such as decision-making, signal detection, short-term memory, and memory
recall even with the quite large networks found in the cerebral cortex, and this
thesis aims to measure the effects of noise on biologically plausible attractor
networks. Our results are obtained using a spiking neural network made up
of integrate-and-fire neurons, and we focus our results on the stochastic transition
that this network undergoes. In this thesis we examine two such processes
that are biologically relevant, but for which no mean-field solutions yet
exist: graded firing rates, and diluted connectivity. Representations in the cortex
are often graded, and we find that noise in these networks may be larger than
with binary representations. In further investigations it was shown that diluted
connectivity reduces the effects of noise in the situation where the number of
synapses onto each neuron is held constant. In this thesis we also use the same
attractor network framework to investigate the Communication through Coherence
hypothesis. The Communication through Coherence hypothesis states
that synchronous oscillations, especially in the gamma range, can facilitate communication
between neural systems. It is shown that information transfer from
one network to a second network occurs for a much lower strength of synaptic
coupling between the networks than is required to produce coherence. Thus,
information transmission can occur before any coherence is produced. This indicates
that coherence is not needed for information transmission between coupled
networks. This raises a major question about the Communication through
Coherence hypothesis. Overall, the results provide substantial contributions
towards understanding operation of attractor neuronal networks in the brain
Distinct phase-amplitude couplings distinguish cognitive processes in human attention
Abstract
Spatial attention is the cognitive function that coordinates the selection of visual stimuli with appropriate behavioral responses. Recent studies have reported that phase-amplitude coupling (PAC) of low and high frequencies covaries with spatial attention, but differ on the direction of covariation and the frequency ranges involved. We hypothesized that distinct phase-amplitude frequency pairs have differentiable contributions during tasks that manipulate spatial attention. We investigated this hypothesis with electrocorticography (ECoG) recordings from participants who engaged in a cued spatial attention task. To understand the contribution of PAC to spatial attention we classified cortical sites by their relationship to spatial variables or behavioral performance. Local neural activity in spatial sites was sensitive to spatial variables in the task, while local neural activity in behavioral sites correlated with reaction time. We found two PAC frequency clusters that covaried with different aspects of the task. During a period of cued attention, delta-phase/high-gamma (DH) PAC was sensitive to cue direction in spatial sites. In contrast, theta-alpha-phase/beta-low-gamma-amplitude (TABL) PAC robustly correlated with future reaction times in behavioral sites. Finally, we investigated the origins of TABL PAC and found it corresponded to behaviorally relevant, sharp waveforms, which were also coupled to a low frequency rhythm. We conclude that TABL and DH PAC correspond to distinct mechanisms during spatial attention tasks and that sharp waveforms are elements of a coupled dynamical process
LFP beta amplitude is predictive of mesoscopic spatio-temporal phase patterns
Beta oscillations observed in motor cortical local field potentials (LFPs)
recorded on separate electrodes of a multi-electrode array have been shown to
exhibit non-zero phase shifts that organize into a planar wave propagation.
Here, we generalize this concept by introducing additional classes of patterns
that fully describe the spatial organization of beta oscillations. During a
delayed reach-to-grasp task in monkey primary motor and dorsal premotor
cortices we distinguish planar, synchronized, random, circular, and radial
phase patterns. We observe that specific patterns correlate with the beta
amplitude (envelope). In particular, wave propagation accelerates with growing
amplitude, and culminates at maximum amplitude in a synchronized pattern.
Furthermore, the occurrence probability of a particular pattern is modulated
with behavioral epochs: Planar waves and synchronized patterns are more present
during movement preparation where beta amplitudes are large, whereas random
phase patterns are dominant during movement execution where beta amplitudes are
small
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