In this company I helped to develop high through-put screening assays that used fluid handling robots to produce uniform cultures of neurons from different regions of the central nervous system. These primary cultures were assayed for targets that pharmaceutical companies were using to elucidate the mode of action of drugs in preclinical experiments. The ultimate goal of my projects in Yoland's lab is to establish if the synaptic plasticity GABA receptors localization in the thalamus may play a role in Parkinson's disease pathophysiology.
The hypothesis is that in Parkinson's disease the lack of striatal dopamine leads to increased GABAergic basal ganglia outflow to the thalamus. If so, postsynaptic GABA receptors could respond to this increased release of neurotransmitter by either downregulation or changes in pharmacological properties. To do so, I have been using pre-and post-embedding immunocytochemichal techniques for electron microscopy.
Another important objective of this project is to know the sources of GABAergic presynaptic boutons in the different thalamic nuclei studied. Scale Bar in A1 applies to A5 and A9: Recent Research Projects 1. Smith The goal of this project is to elucidate various aspects of the synaptology and plasticity of the thalamostriatal system in normal and MPTP-treated parkinsonian monkeys. Specific Research in the Grant: Wichmann The goal of this project is to elucidate the role of the thalamostriatal system in regulating activity of cholinergic interneurons and projection neurons in normal and parkinsonian monkeys.
Unbiased stereological quantification of the total neuronal number in the intralaminar thalamic nuclei CM and PF of control and MPTP-treated animals using Nissl and histochemical staining of consecutive serial sections. Differential structural of corticostriatal and thalamostriatal axo-spinous synapses in MPTP-treated parkinsonian monkeys. Parkinsonism and related Disorders 15 3: An overview of it's anatomical organization in normal and parkinsonian brains, Mov Disorders 23, Suppl 3: Wichmann GABAergic modulation of the activity of globus pallidus neurons in primates: The major input connections are inhibitory innervations from the GPe and the striatum as well as excitatory innervations from the subthalamic nucleus.
The GPi innervates the thalamus and among other things is involved in limb and trunk movements. The firing patterns of the GPi neurons employed here match those of the GPe neurons presented above not shown. They do however, have a higher level of basal activity Rubin and Terman, The Subthalamis Nucleus appears to contain only one type of neuron that is excitatory and releases glutamate Gerfen and Bolam, The neurons of the STN model used here have spontaneous activity of around 5—10 Hz.
In addition, when a depolarizing current is applied the STN model responds with a high-frequency tonic firing and a quiescent period after sustained depolarization. The model will fire rebound bursts in response to sufficient hyperpolarizations see Figure 1. Missing from this model are the spontaneous bursts in the absence of inputs as well as plateau deploarizations as observed experimentally.
The simple hybrid model is incapable of including all of the STN cell dynamics. Included here only for completeness, the Substania Nigra pars compacta is at the core of the dopaminergic system of the midbrain. These neurons are spontaneously active and provide tonic and phasic releases of dopamine at about 5 Hz Cohen and Frank, The ventral region of the SNc connects to small islands or patches spatially segregated in the striatum. While the neurons of the dorsal SNc project to the regions surrounding the patches, referred to as the matrix Gerfen and Bolam, The functional implications of this organization are still unknown.
The Substania Nigra pars reticulata is the other output nuclei of the BG and is responsible for head, neck and eye movements. The SNr receives inputs from STN and striatum and outputs to the superior colliculus, the thalamus and the pedunculopontine nucleus. Figure 1 presents the single neuron dynamics of the SNr used here. It is theorized that the primary role of the thalamus is to modulate and process the information entering the cortex Sherman and Guillery, A thalamocortical relay neuron is used here to model that influence.
Parameters were selected to achieve the dual firing modes described in Sherman They do not fire spontaneously but when in the tonic mode show an increase in firing rate in response to larger depolarizing currents.
The Basal Ganglia IX
In the burst mode, when subjected to sustained hyperpolarizing input, the model neurons respond with periods of bursting that is dependent on the strength and duration of the applied current see Figure 1. Conceptually, action-selection is the arbitration of competing signals and the role of the BG is to select the most appropriate one. The complex circuitry of the BG is active in gating information flow in the frontal cortex and the selection mechanism can affect simple action all the way up to behaviors and cognitive processing Cohen and Frank, To explore that mechanism in a physiologically meaningful way, Humphries et al.
The biological fidelity of the model was validated at the population level as well as single-unit recordings from networks replicating anesthetized or lesioned conditions. This was the first network model recreated here. The network exploits the concept of competing anatomical channels within the BG. Three separate channels were constructed using the layout of Figure 2. Each population consisted of 64 neurons per channel with the parameters of Table 1A. This is consistent with the more diffuse outputs from the STN Haber, Cortical inputs to the striatum were simulated as Poisson random spikes.
The synaptic parameters are listed in Table 1C. Action-selection network model Humphries et al. The modeling study of Rubin and Terman presented a network level explanation for the mechanism of action of DBS. In Parkinson's disease there is a marked loss of dopaminergic cells in the SNc. The reduction in tonic and phasic dopamine onto the BG nuclei results in, among many other phenomena, a rhythmic synchronization of the major output nuclei of the BG.
Within the computational model this resulted in a decrease in the ability of thalamocortical neurons to respond to depolarizing cortical inputs. It was hypothesized that this loss of relay fidelity is one of the underlying causes for many clinical Parkinsonian symptoms. It consists of four populations: With the exception of the thalamus, that contains 2 neurons, each population has 16 neurons.
Unlike the action-selection model presented above, the RT model maintained consistent network connectivity that was exactly the same used by Rubin and Terman Figure 3B illustrates the connectivity patterns for individual neurons of the network. A Network layout of Rubin and Terman B Individual neuron connections. The parameters used for the neurons of the RT model are listed in Table 2A. Note that similar to the original model a constant input current, I app , is added to each of the neurons to increase the basal activity.
The synaptic conductances were randomly selected from a normal distribution with the ranges specified in Table 2B. There are two sources of depolarizing input current used in this model. Both follow an equation of the form. The values used for each of these currents is presented in Table 2C. The synaptic delay, imposed by the simulator, was 2 ms.
The Basal Ganglia
The synaptic parameters matched those in Table 1C. As discussed above, the GPi is the major output nuclei and is responsible here for appropriately controlling the activity of the thalamic neurons. The role of the thalamus in this case is simplified into a relay station; responsible for appropriately relaying depolarizing signals from somatomotor inputs. Under the normal mode of operation the nuclei of the BG produce irregular firing patterns and the thalamus is capable of relaying somatomotor information reliably.
In the Parkinsonian state the GPe and STN nuclei have more regular synchronized firing rates and the thalamic relay fidelity is greatly diminished. This follows the procedure of Rubin and Terman and is based on the activity patterns of Terman et al. To quantitatively evaluate the performance of the model in each of the three states, an error index measure was introduced in Rubin and Terman This is defined as.
The error index evaluation was completed by running 20 simulations of the model in each of the modes described above. Each run resulted in different results due to the randomly selected connection weights described in Table 2B. The error index was calculated for each of the TC cells and a box and whisker plot were created to compare with Rubin and Terman In addition to exploring different sites of DBS application, the work of Pirini et al. The model was capable of demonstrating simple two channel action-selection by way of disinhibition. The successful switching between channels was lost under Parkinsonian conditions but could be restored by the application of DBS into the STN.
The exact two channel network from Pirini et al. The network layout and individual neuron connections matched those of Figure 3 and the parameters of Table 2 were used with some modifications to handle the change in network configuration as well as the stochastic input pattern.
Reward Yourself
The somatomotor input into the TC cells was reduced to In addition, the pulse times were randomly selected from an exponential distribution with a mean of 15 Hz. The action-selection mechanism signaled by the striatum is modeled as a current input into the GPi nucleus.
Each state lasts 2 s before switching. Correlated firing in neuronal ensembles is important in both understanding information encoding and in interpreting functional anatomy Cohen and Kohn, Correlated activity in many brain regions has been linked to stimulus decoding and discrimination, attention, and motor behavior de la Rocha et al. In addition, highly correlated firing has been associated with pathological conditions.
In the BG in particular, correlated activity of globus pallidus internal GPi neurons is associated with Parkinson's disease or pharmacological agents causing Parkinsonian like conditions Reitsma et al. In the Parkinsonian BG the firing patterns become increasingly oscillatory with pronounced bursting.
This synchronous fire rate can have deleterious effects on the functionality of the BG. The consequence of those patterns of activity on thalamocortical relay fidelity was explored through correlation analysis of a computational model. One conclusion of that work was that the integrate-and-fire-or-burst IFB neuron model demonstrated similar firing patterns and correlation transfer to that of a conductance-based model.
This not only strengthened the overall conclusions of the study but also motivated the authors to suggest the IFB model as a suitable replacement for the conductance-based model in correlation studies. Here, we explore if a similar result can be accomplished with the hybrid neuron. When the membrane voltage is hyperpolarized the inactivation gate of the channel begins to deinactivate.
When the membrane voltage is depolarized the channel remains activated until the gate is reinactivated. Unlike the IFB model, the hybrid neuron used here does not have an explicit bursting mechanism. Instead the recovery variable is used to put the neuron with the bursting regime of the phase-portrait Izhikevich, However, our goal here was to replicate both the physiological spike patterns of the thalamocortical neurons as well as the correlation transfer. The model consists of two spiking thalamocortical TC neurons subjected to inhibitory input from an engineered GPi signal as well as an excitatory input representing cortical innervations.
This is illustrated in Figure 4. Each TC neuron receives independently generated 20 Hz Poisson random excitatory inputs. Each cell then samples from this spike train with probability f. Correlation network configuration Reitsma et al. The model and corresponding analysis was computed using the numerical programming language Octave Eaton et al. Table 3 presents the parameters used in the model. The simple hybrid neuron of section 2.
The hybrid numerical method treats the synaptic influence implicitly resulting in a linear dependence on the future value of the membrane voltage. Consistent with the original work, four different GPi input patterns are constructed to emulate normal and Parkinsonian conditions observed experimentally. Samples of the rate functions are illustrated in Figure 5A. The normal input is a constant 70 Hz Poisson random spike train. Example GPi spike patterns and TC cell responses for each of the four modes. A Example input rate functions. The first Parkinsonian pattern, labeled oscillatory, is constructed as a sum of 21 sine waves.
The individual sine waves have frequencies ranging from 5 to 15 Hz with step changes of 0. These are weighted by a Gaussian distribution with a mean of 10 Hz and a variance of 1. The phase of the sine waves are then randomly shifted and summed together. The resulting function is then amplified by 50 Hz and shifted up by Hz. Any negative values are set to zero.
Although constructed differently than those described in Reitsma ; Reitsma et al. In addition, the resulting functions exhibited a distinct peak at 10 Hz, see Figure 5A below, similar to the original work. The second Parkinsonian pattern, labeled Bursty, consists of a basal level of firing at 70 Hz interrupted by random bursts stepping to Hz. The duration of each burst is selected from a Gaussian distribution with a mean of 30 ms and a variance of 10 ms. The time between bursts is selected from a Poisson distribution with a mean of 70 ms.
The final input pattern, labeled Oscillatory Bursty, is constructed similar to the bursty case, however, the inter-burst-interval is selected from a Gaussian distribution with a mean of 30 ms and a variance of 10 ms. This results in more periodic bursts. These rate functions are then used to generate Poisson random spike trains. These patterns were selected by Reitsma et al. Both interspike interval ISI distributions and power spectra were computed on the model TC cells for comparisons with the original work. The power spectra was computed for the TC model spike response as well as the corresponding GPi and cortical inputs using the point process multi-taper spectrum analysis from the Chronux software package Bokil et al.
The measure of correlation is calculated using the Pearson's correlation coefficient. This is a spike count measurement that compares the number of spikes that occur over a window of length T defined as. The correlation coefficient is used to calculate the correlation susceptibility that quantifies the degree to which correlations are transferred through the model.
This is computed using the equation. For each value of f , 30 simulations of were run for s each. This resulted in pairs of correlation coefficients.
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This was completed over a range of window sizes T. The pairs were then sampled with replacement to generate a new set correlation coefficients and S values. With the exception of the correlation study, all of the models were simulated using the HRLSim neural simulator package Thibeault, It currently supports two different point neuron implementations, the Leaky Integrate-and-Fire LIF model and the simple hybrid Izhikevich model. It has also proven extremely useful as a general neural simulation environment for other studies Srinivasa and Cho, ; O'Brien and Srinivasa, ; Srinivasa and Jiang, The action-selection model of Figure 2 was first tuned to match the original model of Humphries et al.
Using the model-as-animal strategy, 15 simulations were completed with different randomly connected networks. From each of those simulations 3 cell indexes were randomly selected and the overall activity rate of the last 9 s of simulation were computed for those neurons.
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This is presented in Figure 6. In addition, the spike rasters and binned spike count rate functions are included. The overall mean firing rate results are in good agreement with the original work as well as with the experimental results referenced there. Basal activity of the model of action-selection. The spike rasters for each of the nuclei are overlaid with the corresponding spike-count firing rates. Using the protocol of Humphries et al. Figure 7A illustrates the two channel action-selection results. Initially the network is at its basal level of activity with a 3 Hz Poisson input.
At 1 s the input for channel 1 is increased to 20 Hz, causing, through disinhibition, the selection of that channel. The activity of channel 1 is pushed up to its basal level of activity and the channel 2 output is inhibited causing it to be selected. This selection mechanism is more decisive than the one presented in Humphries et al. In the original work the previously selected channel had an increase in activity that was only slightly above the selection limit.
To build on this result we tested the selection capabilities of all three channels, something that was not part of the original work. The results of this are presented in Figure 7B as well as in Figure 8 where the spike rasters of the model nuclei are plotted with the overlaid spike count rate functions. This is an encouraging result and suggests that the functional anatomy of the original work can be extended to more than three channels.
The model is capable of appropriate selecting the most salient input between two competing channels A as well as three competing channels B. Network response to competing inputs; spike rasters of the major nuclei of the BG action-selection with the spike count rates overlaid. With this irregular pattern of activity the thalamus is capable of reliably transmitting the somatomotor signals see Figure 9A.
Simulated recovery of TC relay fidelity. A Under normal BG activity the thalamus is capable of relaying somatomotor inputs. B Under Parkinsonian conditions the BG nuclei fall into oscillatory firing patterns TC relay capabilities are greatly diminished. In Parkinson's disease, the firing pattern of the BG neurons have been reported to have regular synchronous firing patterns Walters and Bergstrom, In Figure 9B it can be seen that the BG nuclei begin to fire synchronously.
The neurons of the STN separate into two distinct populations with different phases of bursting. The periods of bursting oscillate around 4 Hz which is consistent with synchronous oscillations observed in the Parkinsonian BG Walters and Bergstrom, This synchronous activity results in a marked loss of thalamic relay.
This disruption in the oscillatory activity is sufficient to restore the relay fidelity of the thalamus see Figure 9C. The results of Figure 9 are quantified in Figure Although the spreads are somewhat dissimilar, neither overlaps with the much higher values measured in the Parkinsonian state. Allowing the network connection weights to randomly change over 20 simulations results in the Normal and DBS modes operating with less errors than the PD mode. The modified RT network of Pirini et al. The results of this experiment are shown in Figure Parkinsonian fire patterns result in a loss of accurate selection capabilities.
Validating the generated GPi input spike trains was completed by the spectral power analysis presented in Figure 12A. As in Reitsma et al. As expected the cortical inputs lack a peak in the frequency range of interest see Figure 12B. The parameters for the model were selected based on the TC cells firing patterns and spectral analysis. Although the Normal and Bursty spectral powers do peak around 10 Hz, there are oscillations present in both see Figure 12C. Consistent with the original work, the Oscillatory and Oscillatory Bursty cases both have more distinct peaks around 10 Hz.
The discrepancies are likely due to analysis parameters and the way GPi inputs were generated, as discussed below. There is a clear bimodality to the interspike interval histogram of Figure 12D , which is consistent with the original work. However, the first peak, at 10 ms, is lower than the 30 ms peak described by Reitsma et al. This may be a product of that model using a refractory period of 5 ms, possibly resulting in slower bursts.
It may also be a product of the way the dynamical correlate of the T-current is produced in the hybrid model. This would cause the inputs to recruit the bursting regime of the model in a different or perhaps less efficient way than the IFB or conductance based models used in Reitsma et al. Despite the slight differences, the firing patterns of the hybrid model in this network are still in general agreement with Reitsma et al.
The general susceptibility analysis, Figure 12F , qualitatively matches the results of Reitsma et al. Similar results were found for our implementation of the IFB model not presented , suggesting that the discrepancy in the magnitude of the susceptibility may arise due to differences in the way the input signals are generated. This is a product of generating the spike trains using a common time-dependent rate function. In the work of Reitsma et al. The implications of this are unclear but they do not appear to affect the conclusion that the bursty inputs cause an increase in correlation susceptibility.
In addition, this further supports the conclusion that the correlation results of Reitsma et al. That combined with the fire pattern results above, helps validate the use of the simple hybrid model in correlation studies. An interesting result of this work that was absent from Humphries et al. Even with the added current source the LIF neuron employed by Humphries et al.
Structure and Function
It was argued that the most relevant dynamics are included and given that the model of Humphries et al. However, as illustrated by the results in Figure 7 , the model presented here was able to not only selected the most salient input but also drive the activity of the previously activated channel clearly away from the selection limit.
The selection results presented by Humphries et al. The increased activity of our model is large enough to push the previous channel back to its basal level of firing; reducing the possibility of selecting undesired or multiple channels. The mechanism for the improved selection capabilities is unclear and remains a focus of future studies.
In addition, in the future this model will be extended to include a larger number of channels to determine how feasible it is to scale beyond the three presented here. The original rate based model of Gurney et al. It was then expanded to include both action-selection and reward learning Stewart et al. The combination of action-selection and reinforcement-learning is another aspect of this model we plan to explore. Rubin and Terman offered one of the first models providing an explanation for the paradoxical therapeutic effects of DBS in a Parkinsonian BG. The data driven extension of this work presented by Guo et al.
A similar extension was performed by Meijer et al. Similarly, Dorval et al. The majority of these studies support the results of the work presented here and the theory that oscillatory inputs into the thalamus from the GPi negatively affect relay fidelity of the thalamus.
In addition, constant inputs from the GPi, arising from DBS application, result in more effective relay in the thalamus Rubin et al. There have been a number of studies that have extended the RT model to explore the therapeutic effects of different DBS locations, protocols and strategies Hahn and McIntyre, ; Guo and Rubin, ; Agarwal and Sarma, , as well as closed loop configurations Feng et al. Similarly, the inverse relationship between frequency and stimulus amplitude in clinically effective DBS has been explored with the RT Model Cagnan et al.
Similar extensions are planned for the network model presented here. The correlation study of Reitsma et al. That firing patterns observed in the Parkinsonian BG result in increased correlation susceptibility of the thalamus was also found in the work presented here. This could provide an explanation for some of the pathological hallmarks of Parkinson's disease.
Although it was shown that the T-current, required for TC neuron bursting, is responsible for the spike pattern of the model, it does not appear to have an effect on the correlation transfer Reitsma et al. Here however, we were able to demonstrate both similar spiking patterns as well as similar correlation susceptibility as the models with higher biological fidelity. These results open up a number of future studies employing the hybrid model.
This includes a frequency space analysis of the correlation transfer as well as a more thorough mathematical analysis of the relationship between GPi inhibition and spike correlation. The complexity of the neuron models explored in the original studies require a level of population specificity that is undesirable in generic hardware implementations. Although the LIF neurons of Humphries et al. The motivations for embedding BG models in hardware systems go beyond the obvious applications to intelligent agents and neurorobotics.
It has been shown that the model based control concepts introduced in section 1 have a number of clinical and practical applications Schiff, In addition to the control system computations, are the numerical calculations required for simulating the model aspect of the observer. Combining the control system with neuromorphic hardware, perhaps in a system on chip, would significantly reduce the power consumption and provide a solution appropriate for portable realization.