Chaitanya Medini

Modeling spike-train processing in the cerebellum granular layer and changes in plasticity reveal single neuron effects in neural ensembles

The cerebellum input stage has been known to perform combinatorial operations on input signals. In this paper, two types of mathematical models were used to reproduce the role of feed-forward inhibition and computation in the granular layer microcircuitry to investigate spike train processing. A simple spiking model and a biophysically-detailed model of the network were used to study signal recoding in the granular layer and to test observations like center-surround organization and time-window hypothesis in addition to effects of induced plasticity. Simulations suggest that simple neuron models may be used to abstract timing phenomenon in large networks, however detailed models were needed to reconstruct population coding via evoked local field potentials (LFP) and for simulating changes in synaptic plasticity. Our results also indicated that spatio-temporal code of the granular network is mainly controlled by the feed-forward inhibition from the Golgi cell synapses. Spike amplitude and total number of spikes were modulated by LTP and LTD. Reconstructing granular layer evoked-LFP suggests that granular layer propagates the nonlinearities of individual neurons. Simulations indicate that granular layer network operates a robust population code for a wide range of intervals, controlled by the Golgi cell inhibition and is regulated by the post-synaptic excitability.

Modeling cerebellar granular layer excitability and combinatorial computation with spikes

The cerebellum input stage has been known to perform combinatorial operations [1] [3] on input signals. In this paper, we developed a model to study information transmission and signal recoding in the cerebellar granular layer and to test observations like center-surround organization and time-window hypothesis [1] [2]. We also developed simple neuron models for abstracting timing phenomena in large networks. Detailed biophysical models were used to study synaptic plasticity and its effect in generation and modulation of spikes in the granular layer network. Our results indicated that spatio-temporal information transfer through the granular network is controlled by synaptic inhibition [1]. Spike amplitude and number of spikes were modulated by L TP and LTD. Both in vitro and in vivo simulations indicated that inhibitory input via Golgi cells acts as a modulator and regulates the post synaptic excitability.

Computationally EfficientBio-realistic Reconstructions of Cerebellar Neuron Spiking Patterns

Simple spiking models have been known to replicate detailed mathematical models firing properties with reliable accuracy in spike timing. We modified the adaptive exponential integrate and fire mathematical model to reconstruct different cerebellar neuronal firing patterns. We were able to reconstruct the firing dynamics of various types of cerebellar neurons and validated with previously published experimental studies. To model the neurons, we exploited particle swarm optimization to fit the parameters. The study showcases the match of electroresponsiveness of the neuronal models to data from biological neurons. Results suggest that models are close reconstructions of the biological data since frequency and spike-timing closely matched known values and were similar to those in previously published detailed computationally intensive biophysical models. Such spiking models have a number of applications including design of large-scale circuit models in order to understand physiological dysfunction and for various computational advantages.

Classification of robotic arm movement using SVM and Naïve Bayes classifiers

Target-oriented approaches have been commonly used in robotics. In 3D space, movement of a robotic arm depends on the target position which can either follow a forward or inverse kinematics approach to reach the target. Predicting the movement of a robotic arm requires prior learning through methods such as transformation matrices or other machine learning techniques. In this paper, we built an online robotic arm to extract movement datasets and have used machine learning algorithms to predict robotic arm articulation. For efficient training, small training datasets were used for learning purpose. Classification is used as a scheme to replace prediction-correction approach and to test whether the method can function as a replacement of usual forward kinematics schemes or predictor-corrector methods in directing a remotely controlled robotic articulator. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests linear SVM and Naïve Bayes algorithms as alternatives for computational intensive learning schemes while predicting articulator movement in laboratory environments.

Computational Neuroscience of Timing, Plasticity and Function in Cerebellum Microcircuits

Cerebellum has been known to show homogeneity in circuit organization and hence the “modules” or various circuits in the cerebellum are attributed to the diversity of functions such as timing, pattern recognition, movement planning and dysfunctions such as ataxia related to the cerebellum. Ataxia-like conditions, induced by intrinsic excitability changes, disable spiking or bursts and thereby limit the quanta of downstream information. Understanding timing, plasticity and functional roles of cerebellum involve large-scale and microcircuit reconstructions validating molecular mechanisms in population activity. Using mathematical modelling, we attempted to reconstruct information transmission at the granular layer of the cerebellum, a circuit whose role in dysfunctions remain yet to be fully explored. We have employed spiking models to reconstruct timing roles and detailed biophysical models for extracellular activity and local field population response. The roles of inhibition, induced plasticity and their implications in information transmission were evaluated. Modulatory roles of Golgi inhibition and pattern abstraction via optimal storage were estimated. An abstraction of the granular and Purkinje layer circuit for neurorobotic roles such as pattern recognition and spike encoding via two new methods was developed. Simulations suggest plasticity at cerebellar relays may be an important element of tremendous storage capacity reliable in the learning of coordination of actions, sensorimotor or cognitive, in which the cerebellum participates.

Computational characterization of cerebellum granule neuron responses to auditory and visual inputs

The multimodal nature of sensory and tactile inputs to cerebellum is of significance for understanding brain function. Granule neuron properties in modifying auditory and visual stimuli was mathematically modeled in this study. Cerebellum granule neuron is a small electrotonically compact neuron and is among the largest number of neurons in the cerebellum. Granule neurons receives four excitatory inputs from four different mossy fibers. We mathematically reconstructed the firing patterns of both auditory and visual responses and decode the mossy fiber input patterns from both modalities. A detailed multicompartment biophysical model of granule neuron was used and in vivo behavior was modeled with short and long bursts. The cable compartmental model could reproduce input-output behavior as seen in real neurons to specific inputs. The response patterns reveal how auditory and visual patterns are encoded by the mossy fiber-granule cell relay and how multiple information modalities are processed by cerebellum granule neuron as responses of auditory and visual stimuli.

Modeling basal ganglia microcircuits using spiking neurons

Basal ganglia and cerebellum have been implicated in critical roles related to control of voluntary motor movements for action selection and cognition. Basal ganglia primarily receive inputs from cortical areas as well as thalamic regions, and their functional architecture is parallel in nature which link several brain regions like cortex and thalamus. Striatum, substantia nigra, pallidum form different neuronal populations in basal ganglia circuit which were functionally distinct supporting sensorimotor, cognitive and emotional-motivational brain functions. In this paper, we have modelled and simulated basal ganglia neurons as well as basal ganglia circuit using integrate and fire neurons. Firing behaviour of subthalamic nucleus and global pallidus externa show how they modulate spike transmission in the circuit and could be used to model circuit dysfunctions in Parkinsons disease.

Reconstructing fMRI BOLD signals arising from cerebellar granule neurons - comparing GLM and balloon models

Understanding the relationship between fMRI BOLD and underlying neuronal activity has been crucial to connect circuit behavior to cognitive functions. In this paper, we modeled fMRI BOLD reconstructions with general linear model and balloon modeling using biophysical models of rat cerebellum granular layer and stimuli spike trains of various response times. Linear convolution of the hemodynamic response function with the known spiking information reconstructed activity similar to experimental BOLD-like signals with the limitation of short stimuli trains. Balloon model through Volterra kernels gave seemingly similar results to that of general linear model. Our main goal in this study was to understand the activity role of densely populated clusters through BOLD-like reconstructions given neuronal responses and by varying response times for the whole stimulus duration.

Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms

Spiking neural encoding models allow classification of real world tasks to suit for brain-machine interfaces in addition to serving as internal models. We developed a new spike encoding model inspired from cerebellum granular layer and tested different classification techniques like SVM, Naïve Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking network-based pattern recognition, in this study, real world datasets were encoded into spike trains. The objective of this study was to encode information from datasets into spiking neuron patterns that were relevant for spiking neural networks and for conventional machine learning algorithms. In this initial study, we present a new approach similar to cerebellum granular layer encoding and compared it with BSA encoding techniques. We have also compared the efficiency of the encoded dataset with different datasets and with standard machine learning algorithms.

Modeling pattern abstraction in cerebellum and estimation of optimal storage capacity

Precise fine-tuning of motor movements has been known to be a vital function of cerebellum, which is critical for maintaining posture and balance. Purkinje cell (PC) plays a prominent role in this fine-tuning through association of inputs and output alongside learning through error correction. Several classical studies showed that PC follows perceptron like behavior, which can be used to develop cerebellum like neural circuits to address the association and learning. With respect to the input, the PC learns the motor movement through update of synaptic weights. In order to understand how cerebellar circuits associate spiking information during learning, we developed a spiking neural network using adaptive exponential integrate and fire neuron model (AdEx) based on cerebellar molecular layer perceptron-like architecture and estimated the maximal storage capacity at parallel fiber-PC synapse. In this study, we explored information storage in cerebellar microcircuits using this abstraction. Our simulations suggest that perceptron mimicking PC behavior was capable of learning the output through modification via finite precision algorithm. The study evaluates the pattern processing in cerebellar Purkinje neurons via a mathematical model estimating the storage capacity based on input patterns and indicates the role of sparse encoding of granular layer neurons in such circuits.