Biology subjects

Lankarany, M.

Publications and source records attributed to Lankarany, M..

25 records · Page 2Linked to original sources

bioRxiv2022

Simulating Short-Term Synaptic Plasticity on SpiNNaker Neuromorphic Hardware

Neuromorphic chips are well-suited for the exploration of neuronal dynamics in (near) real-time. In order to port existing research onto these chips, relevant models of neuronal and synaptic dynamics first need to be supported by their respective development environments and validated against existing simulator backends. At the time of writing, support for short-term synaptic plasticity on neuromorphic hardware is scarce. This technical paper proposes an implementation of dynamic synapses for the SpiNNaker development environment based on the popular synaptic plasticity model by Tsodyks and Markram (TM). This extension is undertaken in the context of existing research on neuromodulation and the study of deep brain stimulation (DBS) effects on singular-neuron responses. The implementation of the TM synapse is first detailed and then, simulated for various response types. Its role in studies of DBS effect on postsynaptic responses is also reviewed. Finally, given the real-time capabilities offered by the hardware, we provide some insight to lay the groundwork for future explorations of closed-loop DBS on neuromorphic chips.

neuroscience↗

bioRxiv2022

Adaptive Unscented Kalman Filter for Neuronal State and Parameter Estimation

Data assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter implementations, the robust adaptive unscented Kalman filter (RAUKF) jointly estimates the states and parameters of the neuronal model while adjusting noise covariance matrices online based on innovation and residual information. We benchmark the adaptive filters performance against existing nonlinear Kalman filters and explore the sensitivity of the filter parameters to the system being modelled. To evaluate the robustness of the proposed solution, we simulate practical settings that challenge tracking performance, such as a model mismatch and measurement faults. Compared to standard variants of the Kalman filter the adaptive variant implemented here is more accurate and robust to faults.

neuroscience↗

bioRxiv2022

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

ObjectiveDeep Brain Stimulation (DBS) is an effective treatment for movement disorders, including Parkinsons disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei--including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr) and ventral intermediate nucleus (Vim)--across different DBS frequencies. Materials and MethodsBoth synthetic and experimental data were utilized in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work 1, and the experimental data were provided using single-unit microelectrode recordings (MER) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr and Vim--across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. ResultsOur model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. ConclusionThe result of our model fitting were in agreement with experimental single-unit microelectrode recording data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS, and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

neuroscience↗

bioRxiv2021

Inferring Deep Brain Stimulation Induced Short-term Synaptic Plasticity Using Novel Dual Optimization Algorithm

Experimental evidence in both human and animal studies demonstrated that deep brain stimulation (DBS) can induce short-term synaptic plasticity (STP) in the stimulated nucleus. Given that DBS-induced STP may be connected to the therapeutic effects of DBS, we sought to develop an appropriate computational predictive model that infers the dynamics of STP in response to DBS at different frequencies. Existing methods for estimating STP - either model-based or model-free approaches - require access to pre-synaptic spiking activity. However, in the context of DBS, extracellular stimulation (e.g. DBS) can be used to elicit presynaptic activations directly. We present a model-based approach that integrates multiple individual frequencies of DBS-like electrical stimulation as pre-synaptic spikes and infers parameters of the Tsodyks-Markram (TM) model from post-synaptic currents of the stimulated nucleus. By distinguishing between the steady-state and transient responses of the TM model, we develop a novel dual optimization algorithm that infers the model parameters in two steps. First, the TM model parameters are calculated by integrating multiple frequencies of stimulation to estimate the steady state response of post-synaptic current through a closed-form analytical solution. The results of this step are utilized as the initial values for the second step in which a non-derivative optimization algorithm is used to track the transient response of the post-synaptic potential across different individual frequencies of stimulation. Moreover, we apply our algorithm to empirical data recorded from acute rodent brain slices of the subthalamic nucleus (STN) during DBS-like stimulation to infer dynamics of STP for inhibitory synaptic inputs.

neuroscience↗

bioRxiv2020

A theoretical framework for the site-specific and frequency-dependent neuronal effects of deep brain stimulation

With the growing interest in the expansion of deep brain stimulation indications, we aimed to provide experimental and computational insights into the brain-region-specific and frequency-dependent effects of extracellular stimulation on human neuronal activity. Experimentally, we demonstrated microstimulation-evoked excitatory neuronal responses in the ventral intermediate nucleus and reticular thalamus, and inhibitory responses in the subthalamic nucleus and substantia nigra pars reticulata; hypothesized to be the result of simultaneous activations of convergent afferent inputs. Higher stimulation frequencies led to a loss of site-specificity and convergence towards neuronal suppression; hypothesized to be mediated by synaptic depression. These experimental findings were reproduced by a computational framework in which relative distributions of convergent excitatory/inhibitory afferents were embedded within a model of short-term synaptic plasticity for the prediction of site-specific and frequency-dependent responses to extracellular stimulation. This theoretical framework may aid in the design of physiologically-informed stimulation paradigms in existing or prospective deep brain stimulation indications.

neuroscience↗

bioRxiv2019

Necessary Conditions for Reliable Representation of Asynchronous Spikes Through a Single-layered Feedforward Network

Reliable propagation of firing rate - specifically slow modulation of asynchronous spikes in fairly short time windows [20-500]ms across multiple layers of a feedforward network (FFN) receiving background synaptic noise has proven difficult to capture in spiking models. We, in this paper, explore how information of asynchronous spikes disrupted in the first layer of a typical FFN, and which factors can enable reliable information representation. Our rationale is that the reliable propagation of information across layers of a FFN is likely if that information can be preserved in the first layer of the FFN. In a typical FFN, each layer comprises a certain number (network size) of excitatory neurons - leaky integrate and fire (LIF) model neuron in this paper - receiving correlated input (common stimulus from the upstream layer) plus independent background synaptic noise. We develop a reduced network model of FFN which captures main features of a conventional all-to-all connected FFN. Exploiting the reduced network model, synaptic weights are calculated using a closed-form optimization framework that minimizes the mean squared error between reconstructed stimulus (by spikes of the first layer of FFN) and the original common stimulus. We further explore how representation of asynchronous spikes in a FFN changes with respect to other factors like the network size and the level of background synaptic noise while synaptic weights are optimized for each scenario. We show that not only synaptic weights but also the network size and the level of background synaptic noise are crucial to preserve a reliable representation of asynchronous spikes in the first layer of a FFN. This work sheds light in better understanding of how information of slowly time-varying fluctuations of the firing rate can be transmitted in multi-layered FFNs.

neuroscience↗

bioRxiv2017

Estimating Excitatory and Inhibitory Synaptic Conductances from Spike Trains using a Recursive Bayesian Approach

Inference of excitatory and inhibitory synaptic conductances (SCs) from the spike trains is poorly addressed in the literature due to the complexity of the problem. As recent technological advancements make recording spikes from multiple (neighbor) neurons of a behaving animal (in some rare cases from humans) possible, this paper tackles the problem of estimating SCs solely from the recorded spike trains. Given an ensemble of spikes corresponding to population of neighbor neurons, we aim to infer the average excitatory and inhibitory SCs underlying the shared neural activity. In this paper, we extended our previously established Kalman filtering (KF) based algorithm to incorporate the voltage-to-spike nonlinearity (mapping from membrane potential to spike rate). Having estimated the instantaneous spike rate using optimal linear filtering (Gaussian kernel), our proposed algorithm uses KF followed by expectation maximization (EM) algorithm in a recursive fashion to infer the average SCs. As the dynamics of SCs and membrane potential is included in our model, the proposed algorithm, unlike other related works, considers different sources of stochasticity, i.e., the variabilities of SCs, membrane potential, and spikes. Moreover, it is worth mentioning that our algorithm is blind to the external stimulus, and it performs only based on observed spikes. We validate the accuracy and practicality of our technique through simulation studies where leaky integrate and fire (LIF) model is used to generate spikes. We show that the estimated SCs can precisely track the original ones. Moreover, we show that the performance of our algorithm can be further improved given enough number of trials (spikes). As a rule of thumb, 50 trials of neurons with the average firing rate of 5 Hz can guarantee the accuracy of our proposed algorithm.

neuroscience↗