Journal of Computational Neuroscience最新文献

According to current anatomical models, motor cortical areas, the basal ganglia, and the ventral motor thalamus form partially closed (re-entrant) loop structures. The normal patterning of neuronal activity within this network regulates aspects of movement planning and execution, while abnormal firing patterns can contribute to movement impairments, such as those seen in Parkinson's disease. Most previous research on such firing pattern abnormalities has focused on parkinsonism-associated changes in the basal ganglia, demonstrating, among other abnormalities, prominent changes in firing rates, as well as the emergence of synchronized beta-band oscillatory burst patterns. In contrast, abnormalities of neuronal activity in the thalamus and cortex are less explored. However, recent studies have shown both changes in thalamocortical connectivity and anatomical changes in corticothalamic terminals in Parkinson's disease. To explore these changes, we created a computational framework to model the effects of changes in thalamocortical connections as they may occur when an individual transitions from the healthy to the parkinsonian state. A 5-dimensional average neuronal firing rate model was fitted to replicate neuronal firing rate information recorded in healthy and parkinsonian primates. The study focused on the effects of (1) changes in synaptic weights of the reciprocal projections between cortical neurons and thalamic principal neurons, and (2) changes in synaptic weights of the cortical projection to thalamic interneurons. We found that it is possible to force the system to change from a healthy to a parkinsonian state, including the emergent oscillatory activity, by only adjusting these two sets of synaptic weights. Thus, this study demonstrates that small changes in the afferent and efferent connections of thalamic neurons could contribute to the emergence of network-wide firing patterns that are characteristic for the parkinsonian state.

A model for NMDA-receptor-mediated synaptic currents in leaky integrate-and-fire neurons, first proposed by Wang (J Neurosci, 1999), has been widely studied in computational neuroscience. The model features a fast rise in the NMDA conductance upon spikes in a pre-synaptic neuron followed by a slow decay. In a general implementation of this model which allows for arbitrary network connectivity and delay distributions, the summed NMDA current from all neurons in a pre-synaptic population cannot be simulated in aggregated form. Simulating each synapse separately is prohibitively slow for all but small networks, which has largely limited the use of the model to fully connected networks with identical delays, for which an efficient simulation scheme exists. We propose an approximation to the original model that can be efficiently simulated for arbitrary network connectivity and delay distributions. Our results demonstrate that the approximation incurs minimal error and preserves network dynamics. We further use the approximate model to explore binary decision making in sparsely coupled networks.

Computational Neuroscience (CN) is an interdisciplinary field that combines neuroscience, mathematics, artificial intelligence, theoretical models and experimental data to understand how the brain works. It unravels the intricacies of the nervous system contributing significantly to cognitive science, neuroengineering and machine learning. CN importance in artificial intelligence and medical research remains underrepresented in Africa's academic landscape. This paper explores the current state of CN in Africa, the challenges hindering its integration, the emerging opportunities, and the evidence-based strategies for curriculum implementation. Capacity building, interdisciplinary collaboration, open science, theoretical neuroscience, development of local capacity, and leveraging international partnerships are emphasized.

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.

Macroscopic studies of cortical tissue reveal a prevalence of oscillatory activity, that reflect a fine tuning of neural interactions. This research extends neural field theories by incorporating generalized oscillatory dynamics into previous work on conservative or semi-conservative neural field dynamics. Prior studies have largely assumed isotropic connections among neural units; however, this study demonstrates that a broad range of anisotropic and fluctuating connections can still sustain oscillations. Using Lagrangian field methods, we examine different types of connectivity, their dynamics, and potential interactions with neural fields. From this theoretical foundation, we derive a framework that incorporates Hebbian and non-Hebbian learning - i.e., plasticity - into the study of neural fields via the concept of a connectivity field.

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).

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.