Arathi G Rajendran

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.

Computational neuroscience of substantia nigra circuit and dopamine modulation during parkinson's disease

Several interconnected brain circuits such as cerebellum, cerebral cortex, thalamus and basal ganglia process motor information in many species including mammals. Interconnection between basal ganglia and cerebellum through thalamus and cortex may influence the pathways involved in basal ganglia processing. Malfunctions in the neural circuitry of basal ganglia influenced by modifications in the dopaminergic system, which are liable for an array of motor disorders and slighter cognitive issues in Parkinsons disease. Both basal ganglia and cerebellum receives input from and send output to the cerebral cortex and these structures influence motor and cognitive operations through cerebellar-thalamo-basal ganglia-cortical circuit. This interconnected circuit (basal ganglia-cerebellum) helps to understand the role of cerebellum in motor dysfunction during Parkinsons disease. To develop models of unsupervised learning as in brain circuits, we modelled sub thalamic nucleus, internal and external parts of Globus pallidus, fast spiking striatal neuron and medium spiny neuron in striatum using Adaptive Exponential Integrate and Fire model. Simulations highlight the correlation between firing of GPe and level of dopamine and the changes induced during simulated Parkinsons disease. Such models are crucial to understand the motor processing and for developing spiking based deep learning algorithms.

Mathematical Modelling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions

Recent studies show cerebellum having a crucial role in motor coordination and cognition, and it has been observed that in patients with movement disorders and other neurological conditions cerebellar circuits are known to be affected. Simulations allow insight on how cerebellar granular layer processes spike information and to understand afferent information divergence in the cerebellar cortex. With excitation-inhibition ratios adapted from in vitro experimental data in the cerebellum granular layer, the model allows reconstructing spatial recoding of sensory and tactile patterns in cerebellum. Granular layer population activity reconstruction was performed with biophysical modeling of fMRI BOLD signals and evoked local field potentials from single neuron and network models implemented in NEURON environment. In this chapter, evoked local field potentials have been reconstructed using biophysical and neuronal mass models interpreting averaged activity and constraining population behavior as observed in experiments. Using neuronal activity and correlating blood flow using the balloon and modified Windkessel model, generated cerebellar granular layer BOLD response. With the focus of relating neural activity to clinical correlations such models help constraining network models and predicting activity-dependent emergent behavior and manifestations. To reverse engineering brain function, cerebellar circuit functions were abstracted into a spiking network based trajectory control model for robotic articulation.