Journal of Computational Neuroscience最新文献

The basolateral amygdala (BLA) is central to emotional processing, fear learning, and memory. Dopamine (DA) significantly influences BLA function, yet its precise effects are not clear. We present a mathematical model exploring how DA modulation of BLA activity depends on the network's current state. Specifically, we model the firing rates of interconnected neural groups in the BLA and their responses to external stimuli and DA modulation. BLA projection neurons are separated into two groups according to their responses-fear and safety. These groups are connected by mutual inhibition though interneurons. We contrast 'differentiated' BLA states, where fear and safety projection neurons exhibit distinct activity levels, with 'non-differentiated' states. We posit that differentiated states support selective responses and short-term emotional memory. On the other hand, non-differentiated states represent either the case in which BLA is disengaged, or the activation of the fear and safety neurons is at a similar moderate or high level. We show that, while DA further disengages BLA in the low activity state, it destabilizes the moderate activity non-differentiated BLA state. We show that in the latter non-differentiated state the BLA is hypersensitive, and the polarity of its responses (fear or safety) to salient stimuli is highly random. We hypothesize that this non-differentiated state is related to anxiety and Post-Traumatic Stress Disorder (PTSD).

Synaptic and neural properties can change during periods of auditory deprivation. These changes may disrupt the computations that neurons perform. In the brainstem of chickens, auditory deprivation can lead to changes in the size and biophysics of the axon initial segment (AIS) of neurons in the sound source localization circuit. This is the phenomenon of axon initial segment (AIS) plasticity. Individuals who use cochlear implants (CIs) experience periods of hearing loss, and so we ask whether AIS plasticity in neurons of the medial superior olive (MSO), a key stage of sound location processing, would impact time difference sensitivity in the scenario of hearing with cochlear implants. The biophysical changes that we implement in our model of AIS plasticity include enlargement of the AIS and replacement of low-threshold potassium conductance with the more slowly-activated M-type potassium conductance. AIS plasticity has been observed to have a homeostatic effect with respect to excitability. In our model, AIS plasticity has the additional effect of converting MSO neurons from phasic firing type to tonic firing type. Phasic firing is known to have greater temporal sensitivity to coincident inputs. Consistent with this, we find AIS plasticity degrades time difference sensitivity in the auditory deprived MSO neuron model across a range of stimulus parameters. Our study illustrates a possible mechanism of cellular plasticity in a non-peripheral stage of neural processing that could impose barriers to sound source localization by bilateral cochlear implant users.

The brain modifies synaptic strengths to store new information via long-term potentiation (LTP) and long-term depression (LTD). Evidence has mounted that long-term synaptic plasticity is controlled via concentrations of calcium ([Ca²⁺]) in postsynaptic dendritic spines. Several mathematical models describe this phenomenon, including those of Shouval, Bear, and Cooper (SBC) (Shouval et al., 2002, 2010) and Graupner and Brunel (GB) (Graupner & Brunel, 2012). Here we suggest a generalized version of the SBC and GB models, the fixed point - learning rate (FPLR) framework, where the synaptic [Ca²⁺] specifies a fixed point toward which the synaptic weight approaches asymptotically at a [Ca²⁺]-dependent rate. The FPLR framework offers a straightforward phenomenological interpretation of calcium-based plasticity: the calcium concentration tells the synaptic weight where it is going and how quickly it goes there. The FPLR framework can flexibly incorporate various experimental findings, including the existence of multiple regions of [Ca²⁺] where no plasticity occurs, or plasticity observed experimentally in cerebellar Purkinje cells, where the directionality of calcium-based synaptic changes is reversed relative to cortical and hippocampal neurons. We also suggest a modeling approach that captures the dependency of late-phase plasticity stabilization on protein synthesis. We demonstrate that due to the asymptotic nature of synaptic changes in the FPLR rule, the plastic changes induced by frequency- and spike-timing-dependent plasticity protocols are weight-dependent. Finally, we show how the FPLR framework can explain the weight-dependence observed in behavioral time scale plasticity (BTSP).

We present a mean field model for a spiking neural network of excitatory and inhibitory neurons with fast GABA $_A^{}$ and nonlinear slow GABA $_B^{}$ inhibitory conductance-based synapses. This mean field model can predict the spontaneous and evoked response of the network to external stimulation in asynchronous irregular regimes. The model displays theta oscillations for sufficiently strong GABA $_B^{}$ conductance. Optogenetic activation of interneurons and an increase of GABA $_B^{}$ conductance caused opposite effects on the emergence of gamma oscillations in the model. In agreement with direct numerical simulations of neural networks and experimental data, the mean field model predicts that an increase of GABA $_B^{}$ conductance reduces gamma oscillations. Furthermore, the slow dynamics of GABA $_B^{}$ synapses regulates the appearance and duration of transient gamma oscillations, namely gamma bursts, in the mean field model. Finally, we show that nonlinear GABA $_B^{}$ synapses play a major role to stabilize the network from the emergence of epileptic seizures.

Traumatic brain injury (TBI) can have a multitude of effects on neural functioning. In extreme cases, TBI can lead to seizures both immediately following the injury as well as persistent epilepsy over years to a lifetime. However, mechanisms of neural dysfunctioning after TBI remain poorly understood. To address these questions, we analyzed human and animal data and we developed a biophysical network model implementing effects of ion concentration dynamics and homeostatic synaptic plasticity to test effects of TBI on the brain network dynamics. We focus on three primary phenomena that have been reported in vivo after TBI: an increase in infra slow oscillations (<0.1 Hz), increase in Delta power (1 - 4 Hz), and the emergence of broadband Gamma bursts (30 - 100 Hz). Using computational network model, we show that the infra slow oscillations can be directly attributed to extracellular potassium dynamics, while the increase in Delta power and occurrence of Gamma bursts are related to the increase in strength of synaptic weights from homeostatic synaptic scaling triggered by trauma. We also show that the buildup of Gamma bursts in the injured region can lead to seizure-like events that propagate across the entire network; seizures can then be initiated in previously healthy regions. This study brings greater understanding of the network effects of TBI and how they can lead to epileptic activity. This lays the foundation to begin investigating how injured networks can be healed and seizures prevented.

Evidence in favor of an earlier conjecture, namely that the low frequency autonomic regime of neural waves acts as a governing or operating system, processing incoming stimuli in various ways for the purposes of conducting computations, is presented in the context of our network model. The rôle of this low frequency regime in the implementation of preplay compares favorably with recent experimental findings in mice. This is followed by a discussion and analysis of three problems arising from considerations of Working Memory processes. Namely, distinguishability, garbage collection and distractor avoidance. The rôle of inhibitory bursts arises spontaneously in the last two scenarios.

To model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.

Transcranial direct current stimulation (tDCS) generates a weak electric field (EF) within the brain, which induces opposite polarization in the soma and distal dendrite of cortical pyramidal neurons. The somatic polarization directly affects the spike timing, and dendritic polarization modulates the synaptically evoked dendritic activities. Ca²⁺ spike, the most dramatic dendritic activity, is crucial for synaptic integration and top-down signal transmission, thereby indirectly influencing the output spikes of pyramidal cells. Nevertheless, the role of dendritic Ca²⁺ spike in the modulation of neural spike timing with tDCS remains largely unclear. In this study, we use morphologically and biophysically realistic models of layer 5 pyramidal cells (L5 PCs) to simulate the dendritic Ca²⁺ spike and somatic Na⁺ spike in response to distal dendritic synaptic inputs under weak EF stimulation. Our results show that weak EFs modulate the spike timing through the modulation of dendritic Ca²⁺ spike and somatic polarization, and such field effects are dependent on synaptic inputs. At weak synaptic inputs, the spike timing is advanced due to the facilitation of dendritic Ca²⁺ spike by field-induced dendritic depolarization. Conversely, it is delayed by field-induced dendritic hyperpolarization. In this context, the Ca²⁺ spike exhibits heightened sensitivity to weak EFs, thereby governing the changes in spike timing. At strong synaptic inputs, somatic polarization dominates the changes in spike timing due to the decreased sensitivity of Ca²⁺ spike to EFs. Consequently, the spike timing is advanced/delayed by field-induced somatic depolarization/hyperpolarization. Moreover, EFs have significant effects on the changes in the timing of somatic spike and Ca²⁺ spike when synaptic current injection coincides with the onset of EFs. Field effects on spike timing follow a cosine dependency on the field polar angle, with maximum effects in the field direction parallel to the somato-dendritic axis. Furthermore, our results are robust to morphological and biological diversity. These findings clarify the modulation of spike timing with weak EFs and highlight the crucial role of dendritic Ca²⁺ spike. These predictions shed light on the neural basis of tDCS and should be considered when understanding the effect of tDCS on population dynamics and cognitive behavior.

Deficient gamma oscillations in the prefrontal cortex (PFC) of individuals with schizophrenia (SZ) are proposed to arise from alterations in the excitatory drive to fast-spiking interneurons (E $\to$ I) and in the inhibitory drive from these interneurons to excitatory neurons (I $\to$ E). Consistent with this idea, prior postmortem studies showed lower levels of molecular and structural markers for the strength of E $\to$ I and I $\to$ E synapses and also greater variability in E $\to$ I synaptic strength in PFC of SZ. Moreover, simulating these alterations in a network of quadratic integrate-and-fire (QIF) neurons revealed a synergistic effect of their interactions on reducing gamma power. In this study, we aimed to investigate the dynamical nature of this synergistic interaction at macroscopic level by deriving a mean-field description of the QIF model network that consists of all-to-all connected excitatory neurons and fast-spiking interneurons. Through a series of numerical simulations and bifurcation analyses, findings from our mean-field model showed that the macroscopic dynamics of gamma oscillations are synergistically disrupted by the interactions among lower strength of E $\to$ I and I $\to$ E synapses and greater variability in E $\to$ I synaptic strength. Furthermore, the two-dimensional bifurcation analyses showed that this synergistic interaction is primarily driven by the shift in Hopf bifurcation due to lower E $\to$ I synaptic strength. Together, these simulations predict the nature of dynamical mechanisms by which multiple synaptic alterations interact to robustly reduce PFC gamma power in SZ, and highlight the utility of mean-field model to study macroscopic neural dynamics and their alterations in the illness.

Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning. We found that a network based on autoencoders can, given a particular inputs and connectivity constraints, produce scale-invariant time cells. When the animal's velocity modulates the decay rate of the leaky integrators, the same network gives rise to scale-invariant place cells. Importantly, this is not the case when velocity is fed as a direct input to the leaky integrators, implying that weight modulation by velocity might be critical for developing scale-invariant spatial receptive fields. Finally, we demonstrated that after training, scale-invariant place cells emerge in environments larger than those used during training. Taken together, these findings bring us closer to understanding the emergence of neurons with bell-shaped tuning curves in the hippocampus and highlight the critical role of velocity modulation in the formation of scale-invariant place cells.