Using a hybrid neuron in physiologically inspired models of the basal ganglia

Abstract

Our current understanding of the basal ganglia (BG) has facilitated the creation of computational models that have contributed novel theories, explored new functional anatomy and demonstrated results complementing physiological experiments. However, the utility of these models extends beyond these applications. Particularly in neuromorphic engineering, where the basal ganglia's role in computation is important for applications such as power efficient autonomous agents and model-based control strategies. The neurons used in existing computational models of the BG, however, are not amenable for many low-power hardware implementations. Motivated by a need for more hardware accessible networks, we replicate four published models of the BG, spanning single neuron and small networks, replacing the more computationally expensive neuron models with an Izhikevich hybrid neuron. This begins with a network modeling action-selection, where the basal activity levels and the ability to appropriately select the most salient input is reproduced. A Parkinson's disease model is then explored under normal conditions, Parkinsonian conditions and during subthalamic nucleus deep brain stimulation (DBS). The resulting network is capable of replicating the loss of thalamic relay capabilities in the Parkinsonian state and its return under DBS. This is also demonstrated using a network capable of action-selection. Finally, a study of correlation transfer under different patterns of Parkinsonian activity is presented. These networks successfully captured the significant results of the originals studies. This not only creates a foundation for neuromorphic hardware implementations but may also support the development of large-scale biophysical models. The former potentially providing a way of improving the efficacy of DBS and the latter allowing for the efficient simulation of larger more comprehensive networks.

Cost-efficient FPGA implementation of basal ganglia and their Parkinsonian analysis

“…13, which means that we need to ensure that our system behaves in a similar manner to the actual biological system. We found that the NS employed in our study could reproduce most of the dynamic characteristics of the BG, as also demonstrated in the study by Thibeault and Srinivasa (2013). Thus, our proposed hardware platform can reproduce most of the biological dynamics of the BG with a considerably high level of precision and reliability.…”

“…In PD, a rhythmic synchronization of the mean output from the BG nuclei occurs due to the reduction in tonic and phasic dopamine, which causes a decrease in the ability of TC neurons to relay the depolarizing input current (Thibeault & Srinivasa, 2013). This loss of the relay ability is one of the major problems in many clinical PD cases (Putzke et al, 2003).…”

“…The basal ganglia (BG) have attracted considerable attention in recent years due to their important roles in cognitive and behavioral phenomena such as the control of voluntary motor movements, goal-oriented actions, and cognition (Thibeault & Srinivasa, 2013). Moreover, some neurological and psychiatric disorders are related to people suffer from the neurological disorders that cause paralysis or cognitive deficit.…”

The connectome of the basal ganglia

“…As shown before by this step can be performed also in neuroVIISAS using a hybrid neuron model (Thibeault and Srinivasa 2013) in the NEST simulation engine (Gewaltig and Diesmann 2007).…”

Neurological Diseases from a Systems Medicine Point of View

“…One of the very-well-explored disease-related circuits is the model of basal ganglia dysfunction in PD [ 163 , 164 ]. The architecture of corticobasal ganglia-cortical loops [ 165 ] is in fact a reconstructed network, with interactions being projections of different neuronal subtypes to basal ganglia and cortical regions [ 166 ]. Here, the disturbance of these mapped circuits may be assessed using the technique of recording neuronal activity in local brain areas, called electroencephalography ( EEG) .…”