4 research outputs found
Neuronal oscillations: from single-unit activity to emergent dynamics and back
L’objectiu principal d’aquesta tesi és avançar en la comprensió del processament d’informació en xarxes neuronals en presència d’oscil lacions subumbrals. La majoria de neurones propaguen la seva activitat elèctrica a través de sinapsis quÃmiques que són activades, exclusivament, quan el corrent elèctric que les travessa supera un cert llindar. És per aquest motiu que les descà rregues rà pides i intenses produïdes al soma neuronal, els anomenats potencials d’acció, són considerades la unitat bà sica d’informació neuronal, és a dir, el senyal mÃnim i necessari per a iniciar la comunicació entre dues neurones. El codi neuronal és entès, doncs, com un llenguatge binari que expressa qualsevol missatge (estÃmul sensorial, memòries, etc.) en un tren de potencials d’acció. Tanmateix, cap funció cognitiva rau en la dinà mica d’una única neurona. Circuits de milers de neurones connectades entre sà donen lloc a determinats ritmes, palesos en registres d’activitat colectiva com els electroencefalogrames (EEG) o els potencials de camp local (LFP). Si els potencials d’acció de cada cèl lula, desencadenats per fluctuacions estocà stiques de les corrents sinà ptiques, no assolissin un cert grau de sincronia, no apareixeria aquesta periodicitat a nivell de xarxa.
Per tal de poder entendre si aquests ritmes intervenen en el codi neuronal hem estudiat tres situacions. Primer, en el CapÃtol 2, hem mostrat com una
cadena oberta de neurones amb un potencial de membrana intrÃnsecament oscil latori filtra un senyal periòdic arribant per un dels extrems. La resposta
de cada neurona (pulsar o no pulsar) depèn de la seva fase, de forma que cada una d’elles rep un missatge filtrat per la precedent. A més, cada potencial
d’acció presinà ptic provoca un canvi de fase en la neurona postsinà ptica que depèn de la seva posició en l’espai de fases. Els perÃodes d’entrada capaços de sincronitzar les oscil lacions subumbrals són aquells que mantenen la fase d’arribada dels potencials d’acció fixa al llarg de la cadena. Per tal de què el missatge arribi intacte a la darrera neurona cal, a més a més, que aquesta fase permeti la descà rrega del voltatge transmembrana.
En segon cas, hem estudiat una xarxa neuronal amb connexions tant a veïns propers com de llarg abast, on les oscil lacions subumbrals emergeixen de
l’activitat col lectiva reflectida en els corrents sinà ptics (o equivalentment en el LFP). Les neurones inhibidores aporten un ritme a l’excitabilitat de la
xarxa, és a dir, que els episodis en què la inhibició és baixa, la probabilitat d’una descà rrega global de la població neuronal és alta. En el CapÃtol 3
mostrem com aquest ritme implica l’aparició d’una bretxa en la freqüència de descà rrega de les neurones: o bé polsen espaiadament en el temps o bé
en rà fegues d’elevada intensitat. La fase del LFP determina l’estat de la xarxa neuronal codificant l’activitat de la població: els mÃnims indiquen la
descà rrega simultà nia de moltes neurones que, ocasionalment, han superat el llindar d’excitabilitat degut a un decreixement global de la inhibició, mentre
que els mà xims indiquen la coexistència de rà fegues en diferents punts de la xarxa degut a decreixements locals de la inhibició en estats globals d’excitació. Aquesta dinà mica és possible grà cies al domini de la inhibició sobre l’excitació. En el CapÃtol 4 considerem acoblament entre dues xarxes neuronals per tal d’estudiar la interacció entre ritmes diferents. Les oscil lacions indiquen recurrència en la sincronització de l’activitat col lectiva, de manera que durant aquestes finestres temporals una població optimitza el seu impacte en una xarxa diana. Quan el ritme de la població receptora i el de l’emissora difereixen significativament, l’eficiència en la comunicació decreix, ja que les fases de mà xima resposta de cada senyal LFP no mantenen una diferència constant entre elles.
Finalment, en el CapÃtol 5 hem estudiat com les oscil lacions col lectives pròpies de l’estat de son donen lloc al fenomen de coherència estocà stica.
Per a una intensitat òptima del soroll, modulat per l’excitabilitat de la xarxa, el LFP assoleix una regularitat mà xima donant lloc a un perÃode refractari de
la població neuronal.
En resum, aquesta Tesi mostra escenaris d’interacció entre els potencials d’acció, caracterÃstics de la dinà mica de neurones individuals, i les oscil lacions
subumbrals, fruit de l’acoblament entre les cèl lules i ubiqües en la dinà mica de poblacions neuronals. Els resultats obtinguts aporten funcionalitat a aquests
ritmes emergents, agents sincronitzadors i moduladors de les descà rregues neuronals i reguladors de la comunicació entre xarxes neuronals.The main objective of this thesis is to better understand information processing in neuronal networks in the presence of subthreshold oscillations. Most neurons propagate their electrical activity via chemical synapses, which are only activated when the electric current that passes through them surpasses a certain threshold. Therefore, fast and intense discharges produced at the neuronal soma (the action potentials or spikes) are considered the basic unit of neuronal information. The neuronal code is understood, then, as a binary language that expresses any message (sensory stimulus, memories, etc.) in a train of action potentials. Circuits of thousands of interconnected neurons give rise to certain rhythms, revealed in collective activity measures such as electroencephalograms (EEG) and local field potentials (LFP). Synchronization of action potentials of each cell, triggered by stochastic fluctuations of the synaptic currents, cause this periodicity at the network level.To understand whether these rhythms are involved in the neuronal code we studied three situations. First, in Chapter 2, we showed how an open chain of neurons with an intrinsically oscillatory membrane potential filters a periodic signal coming from one of its ends. The response of each neuron (to spike or not) depends on its phase, so that each cell receives a message filtered by the preceding one. Each presynaptic action potential causes a phase change in the postsynaptic neuron, which depends on its position in the phase space. Those incoming periods that are able to synchronize the subthreshold oscillations, keep the phase of arrival of action potentials fixed along the chain. The original message reaches intact the last neuron provided that this phase allows the discharge of the transmembrane voltage.I the second case, we studied a neuronal network with connections to both long range and close neighbors, in which the subthreshold oscillations emerge from the collective activity apparent in the synaptic currents. The inhibitory neurons provide a rhythm to the excitability of the network. When inhibition is low, the likelihood of a global discharge of the neuronal population is high. In Chapter 3 we show how this rhythm causes a gap in the discharge frequency of neurons: either they pulse single spikes or they fire bursts of high intensity. The LFP phase determines the state of the neuronal network, coding the activity of the population: its minima indicate the simultaneous discharge of many neurons, while its maxima indicate the coexistence of bursts due to local decreases of inhibition at global states of excitation. In Chapter 4 we consider coupling between two neural networks in order to study the interaction between different rhythms. The oscillations indicate recurrence in the synchronization of collective activity, so that during these time windows a population optimizes its impact on a target network. When the rhythm of the emitter and receiver population differ significantly, the communication efficiency decreases as the phases of maximum response of each LFP signal do not maintain a constant difference between them.Finally, in Chapter 5 we studied how oscillations typical of the collective sleep state give rise to stochastic coherence. For an optimal noise intensity, modulated by the excitability of the network, the LFP reaches a maximal regularity leading to a refractory period of the neuronal population.In summary, this Thesis shows scenarios of interaction between action potentials, characteristics of the dynamics of individual neurons, and the subthreshold oscillations, outcome of the coupling between the cells and ubiquitous in the dynamics of neuronal populations . The results obtained provide functionality to these emerging rhythms, triggers of synchronization and modulator agents of the neuronal discharges and regulators of the communication between neuronal networks
Role of frequency mismatch in neuronal communication through coherence
Neuronal gamma oscillations have been described in local field potentials of different brain regions of multiple species. Gamma oscillations are thought to reflect rhythmic synaptic activity organized by inhibitory interneurons. While several aspects of gamma rhythmogenesis are relatively well understood, we have much less solid evidence about how gamma oscillations contribute to information processing in neuronal circuits. One popular hypothesis states that a flexible routing of information between distant populations occurs via the control of the phase or coherence between their respective oscillations. Here, we investigate how a mismatch between the frequencies of gamma oscillations from two populations affects their interaction. In particular, we explore a biophysical model of the reciprocal interaction between two cortical areas displaying gamma oscillations at different frequencies, and quantify their phase coherence and communication efficiency. We observed that a moderate excitatory coupling between the two areas leads to a decrease in their frequency detuning, up to similar to 6 Hz, with no frequency locking arising between the gamma peaks. Importantly, for similar gamma peak frequencies a zero phase difference emerges for both LFP and MUA despite small axonal delays. For increasing frequency detunings we found a significant decrease in the phase coherence (at non-zero phase lag) between the MUAs but not the LFPs of the two areas. Such difference between LFPs and MUAs behavior is due to the misalignment between the arrival of afferent synaptic currents and the local excitability windows. To test the efficiency of communication we evaluated the success of transferring rate-modulations between the two areas. Our results indicate that once two populations lock their peak frequencies, an optimal phase relation for communication appears. However, the sensitivity of locking to frequency mismatch suggests that only a precise and active control of gamma frequency could enable the selection of communication channels and their directionality
Role of frequency mismatch in neuronal communication through coherence
Neuronal gamma oscillations have been described in local field potentials of different brain regions of multiple species. Gamma oscillations are thought to reflect rhythmic synaptic activity organized by inhibitory interneurons. While several aspects of gamma rhythmogenesis are relatively well understood, we have much less solid evidence about how gamma oscillations contribute to information processing in neuronal circuits. One popular hypothesis states that a flexible routing of information between distant populations occurs via the control of the phase or coherence between their respective oscillations. Here, we investigate how a mismatch between the frequencies of gamma oscillations from two populations affects their interaction. In particular, we explore a biophysical model of the reciprocal interaction between two cortical areas displaying gamma oscillations at different frequencies, and quantify their phase coherence and communication efficiency. We observed that a moderate excitatory coupling between the two areas leads to a decrease in their frequency detuning, up to similar to 6 Hz, with no frequency locking arising between the gamma peaks. Importantly, for similar gamma peak frequencies a zero phase difference emerges for both LFP and MUA despite small axonal delays. For increasing frequency detunings we found a significant decrease in the phase coherence (at non-zero phase lag) between the MUAs but not the LFPs of the two areas. Such difference between LFPs and MUAs behavior is due to the misalignment between the arrival of afferent synaptic currents and the local excitability windows. To test the efficiency of communication we evaluated the success of transferring rate-modulations between the two areas. Our results indicate that once two populations lock their peak frequencies, an optimal phase relation for communication appears. However, the sensitivity of locking to frequency mismatch suggests that only a precise and active control of gamma frequency could enable the selection of communication channels and their directionality
Phase-Coherence Transitions and Communication in the Gamma Range between Delay-Coupled Neuronal Populations
Synchronization between neuronal populations plays an important role in information transmission between brain areas. In
particular, collective oscillations emerging from the synchronized activity of thousands of neurons can increase the
functional connectivity between neural assemblies by coherently coordinating their phases. This synchrony of neuronal
activity can take place within a cortical patch or between different cortical regions. While short-range interactions between
neurons involve just a few milliseconds, communication through long-range projections between different regions could
take up to tens of milliseconds. How these heterogeneous transmission delays affect communication between neuronal
populations is not well known. To address this question, we have studied the dynamics of two bidirectionally delayedcoupled
neuronal populations using conductance-based spiking models, examining how different synaptic delays give rise
to in-phase/anti-phase transitions at particular frequencies within the gamma range, and how this behavior is related to the
phase coherence between the two populations at different frequencies. We have used spectral analysis and information
theory to quantify the information exchanged between the two networks. For different transmission delays between the
two coupled populations, we analyze how the local field potential and multi-unit activity calculated from one population
convey information in response to a set of external inputs applied to the other population. The results confirm that zero-lag
synchronization maximizes information transmission, although out-of-phase synchronization allows for efficient
communication provided the coupling delay, the phase lag between the populations, and the frequency of the oscillations
are properly matched.Peer Reviewe