Journal of Computational Neuroscience最新文献

We report a parametric simulation study of traveling calcium waves in two classes of cellular structures: dendrite-like processes and an idealized cell body. It is motivated by the hypothesis that calcium waves may participate in spatiotemporal sensory processing; accordingly, its objective is to elucidate the dependence of traveling wave characteristics (e.g., propagation speed and amplitude) on various anatomical and physiological parameters. The models include representations of inositol trisphosphate and ryanodine receptors (which mediate transient calcium entry into the cytoplasm from the endoplasmic reticulum), as well as other entities involved in calcium transport or reactions. These support traveling cytoplasmic calcium waves, which are fully regenerative for significant ranges of model parameters. We also observe Hopf bifurcations between stable and unstable regimes, the latter being characterized by periodic calcium spikes. Traveling waves are possible in unstable processes during phases with sufficiently high calcium levels in the endoplasmic reticulum. Damped and abortive waves are observed for some parameter values. When both receptor types are present and functional, we find wave speeds on the order of 100 to several hundred micrometers per second and cytosolic calcium transients with amplitudes of tens of micromolar; when ryanodine receptors are absent, these values are on the order of tens of micrometers per second and 1-6 micromolar. Even with significantly downgraded channel conductance, ryanodine receptors can significantly impact wave speeds and amplitudes. Receptor areal densities and the diffusion coefficient for cytoplasmic calcium are the parameters to which wave characteristics are most sensitive.

We tackle a quantification of synchrony in a large ensemble of interacting neurons from the observation of spiking events. In a simulation study, we efficiently infer the synchrony level in a neuronal population from a point process reflecting spiking of a small number of units and even from a single neuron. We introduce a synchrony measure (order parameter) based on the Bartlett covariance density; this quantity can be easily computed from the recorded point process. This measure is robust concerning missed spikes and, if computed from observing several neurons, does not require spike sorting. We illustrate the approach by modeling populations of spiking or bursting neurons, including the case of sparse synchrony.

We present a model of pattern memory and retrieval with novel, technically useful and biologically realistic properties. Specifically, we enter n variations of k pattern classes (n*k patterns) onto a cortex-like balanced inhibitory-excitatory network with heterogeneous neurons, and let the pattern spread within the recurrent network. We show that we can identify high mutual-information (MI) neurons as major information-bearing elements within each pattern representation. We employ a simple one-shot adaptive (learning) process focusing on high MI neurons and inhibition. Such 'localist plasticity' has high efficiency, because it requires only few adaptations for each pattern. Specifically, we store k=10 patterns of size s=400 in a 1000/1200 neuron network. We stimulate high MI neurons and in this way recall patterns, such that the whole network represents this pattern. We assess the quality of the representation (a) before learning, when entering the pattern into a naive network, (b) after learning, on the adapted network, and (c) after recall by stimulation. The recalled patterns could be easily recognized by a trained classifier. The recalled pattern 'unfolds' over the recurrent network with high similarity to the original input pattern. We discuss the distribution of neuron properties in the network, and find that an initial Gaussian distribution changes into a more heavy-tailed, lognormal distribution during the adaptation process. The remarkable result is that we are able to achieve reliable pattern recall by stimulating only high information neurons. This work provides a biologically-inspired model of cortical memory and may have interesting technical applications.

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).

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).

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.