804 research outputs found
Regulation of Irregular Neuronal Firing by Autaptic Transmission
The importance of self-feedback autaptic transmission in modulating
spike-time irregularity is still poorly understood. By using a biophysical
model that incorporates autaptic coupling, we here show that self-innervation
of neurons participates in the modulation of irregular neuronal firing,
primarily by regulating the occurrence frequency of burst firing. In
particular, we find that both excitatory and electrical autapses increase the
occurrence of burst firing, thus reducing neuronal firing regularity. In
contrast, inhibitory autapses suppress burst firing and therefore tend to
improve the regularity of neuronal firing. Importantly, we show that these
findings are independent of the firing properties of individual neurons, and as
such can be observed for neurons operating in different modes. Our results
provide an insightful mechanistic understanding of how different types of
autapses shape irregular firing at the single-neuron level, and they highlight
the functional importance of autaptic self-innervation in taming and modulating
neurodynamics.Comment: 27 pages, 8 figure
Structure of Spontaneous UP and DOWN Transitions Self-Organizing in a Cortical Network Model
Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP–DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits
UP-DOWN cortical dynamics reflect state transitions in a bistable network
In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests
Mesoscale Systems, Finite Size Effects, and Balanced Neural Networks
Cortical populations are typically in an asynchronous state, sporadically interrupted by brief epochs of coordinated population activity. Current cortical models are at a loss to explain this combination of states. At one extreme are network models where recurrent in- hibition dynamically stabilizes an asynchronous low activity state. While these networks are widely used they cannot produce the coherent population-wide activity that is reported in a variety of datasets. At the other extreme are models where short term synaptic depression between excitatory neurons can generate the epochs of population-wide activity. However, in these networks inhibition plays only a perfunctory role in network stability, which is at odds with many reports across cortex. In this study we analyze spontaneously active in vitro preparations of primary auditory cortex that show dynamics that are emblematic of this mix- ture of states. To capture this complex population activity we consider models where large excitation is balanced by recurrent inhibition yet we include short term synaptic depression dynamics of the excitatory connections. This model gives very rich nonlinear behavior that mimics the core features of the in vitro data, including the possibility of low frequency (2- 12 Hz) rhythmic dynamics within population events. Our study extends balanced network models to account for nonlinear, population-wide correlated activity, thereby providing a critical step in a mechanistic theory of realistic cortical activity. We further investigate an extension of this model that l exhibits clearly non-Arrhenius behavior, whereby lower noise systems may exhibit faster escape from a stable state. We show that this behavior is due to the system size dependent vector field, intrinsically linking noise and dynamics
Dynamic Effective Connectivity of Inter-Areal Brain Circuits
Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question “Which areas cause the present activity of which others?”. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this “communication-through-coherence”, making thus possible a fast “on-demand” reconfiguration of global information routing modalities
Deep brain stimulation for movement disorder treatment: Exploring frequency-dependent efficacy in a computational network model
A large scale computational model of the basal ganglia (BG) network is
proposed to describes movement disorder including deep brain stimulation (DBS).
The model of this complex network considers four areas of the basal ganglia
network: the subthalamic nucleus (STN) as target area of DBS, globus pallidus,
both pars externa and pars interna (GPe-GPi), and the thalamus (THA).
Parkinsonian conditions are simulated by assuming reduced dopaminergic input
and corresponding pronounced inhibitory or disinhibited projections to GPe and
GPi. Macroscopic quantities can be derived which correlate closely to thalamic
responses and hence motor programme fidelity. It can be demonstrated that
depending on different levels of striatal projections to the GPe and GPi, the
dynamics of these macroscopic quantities switch from normal conditions to
parkinsonian. Simulating DBS on the STN affects the dynamics of the entire
network, increasing the thalamic activity to levels close to normal, while
differing from both normal and parkinsonian dynamics. Using the mentioned
macroscopic quantities, the model proposes optimal DBS frequency ranges above
130 Hz.Comment: 40 pages, 16 figure
Potential mechanisms for imperfect synchronization in parkinsonian basal ganglia
Neural activity in the brain of parkinsonian patients is characterized by the
intermittently synchronized oscillatory dynamics. This imperfect
synchronization, observed in the beta frequency band, is believed to be related
to the hypokinetic motor symptoms of the disorder. Our study explores potential
mechanisms behind this intermittent synchrony. We study the response of a
bursting pallidal neuron to different patterns of synaptic input from
subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe)
neuron is sensitive to the phase of the input from the STN cell and can exhibit
intermittent phase-locking with the input in the beta band. The temporal
properties of this intermittent phase-locking show similarities to the
intermittent synchronization observed in experiments. We also study the
synchronization of GPe cells to synaptic input from the STN cell with
dependence on the dopamine-modulated parameters. Dopamine also affects the
cellular properties of neurons. We show how the changes in firing patterns of
STN neuron due to the lack of dopamine may lead to transition from a lower to a
higher coherent state, roughly matching the synchrony levels observed in basal
ganglia in normal and parkinsonian states. The intermittent nature of the
neural beta band synchrony in Parkinson's disease is achieved in the model due
to the interplay of the timing of STN input to pallidum and pallidal neuronal
dynamics, resulting in sensitivity of pallidal output to the phase of the
arriving STN input. Thus the mechanism considered here (the change in firing
pattern of subthalamic neurons through the dopamine-induced change of membrane
properties) may be one of the potential mechanisms responsible for the
generation of the intermittent synchronization observed in Parkinson's disease.Comment: 27 pages, 9 figure
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