Activity of bifunctional motoneurons during fictive locomotion: a computational modeling study

More than 90 years ago, Graham Brown demonstrated that the cat spinal cord can generate a locomotor rhythm in the absence of input from higher brain centers and afferent feedback, and proposed a general schematic for the spinal central pattern generator (CPG) generating rhythmic alternating activity of flexor and extensor motoneurons during locomotion, the “half-center” model. Since that time, the half-center concept has been used as the basis in many CPG models. Despite many advantages, classical half-center models of the locomotor CPG have been so far unable to reproduce and explain the generation of more complex activity patterns expressed during locomotion by some bifunctional motoneurons actuating muscles controlling more than one joint, such as posterior biceps and semitendinosus (PBSt) and rectus femoris (RF), which were found to be active within a portion of one phase or generated activity during both phases. During normal locomotion, the activity patterns of PBSt and RF are modulated by supra-spinal inputs and afferent feedback and vary with gate and locomotor conditions. However, even during fictive locomotion in the absence of afferent feedback and patterned supra-spinal inputs, PBSt and RF demonstrate a variety of complex activity patterns, similar to those observed in real locomotion under different conditions. This suggests that the complex patterns of bifunctionals are defined by the intrinsic spinal CPG organization. The non-trivial activity profiles expressed by bifunctional motoneurons have been considered as a strong argument against a bipartite half-center organization of the spinal locomotor CPG. The challenging task of this study was to find and propose a neural organization of the spinal locomotor CPG that is able to reproduce the full repertoire of PBSt and RF activities observed during fictive locomotion within the framework of the bipartite organization of the locomotor CPG, implement it in a computational model, and validate the model by reproducing the behavior of bifunctional motoneurons during various types of deletions occurring during fictive locomotion. This study represents a significant step towards understanding the organization of the mammalian spinal locomotor CPG, shaping complex patterns of bifunctional motoneurons, and offers a mechanism for their control by afferent feedback.Ph.D., Biomedical Engineering -- Drexel University, 201

The Functional Role of Striatal Cholinergic Interneurons in Reinforcement Learning From Computational Perspective

In this study, we explore the functional role of striatal cholinergic interneurons, hereinafter referred to as tonically active neurons (TANs), via computational modeling; specifically, we investigate the mechanistic relationship between TAN activity and dopamine variations and how changes in this relationship affect reinforcement learning in the striatum. TANs pause their tonic firing activity after excitatory stimuli from thalamic and cortical neurons in response to a sensory event or reward information. During the pause striatal dopamine concentration excursions are observed. However, functional interactions between the TAN pause and striatal dopamine release are poorly understood. Here we propose a TAN activity-dopamine relationship model and demonstrate that the TAN pause is likely a time window to gate phasic dopamine release and dopamine variations reciprocally modulate the TAN pause duration. Furthermore, this model is integrated into our previously published model of reward-based motor adaptation to demonstrate how phasic dopamine release is gated by the TAN pause to deliver reward information for reinforcement learning in a timely manner. We also show how TAN-dopamine interactions are affected by striatal dopamine deficiency to produce poor performance of motor adaptation

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Directional Summation in Non-direction Selective Retinal Ganglion Cells

<div><p>Retinal ganglion cells receive inputs from multiple bipolar cells which must be integrated before a decision to fire is made. Theoretical studies have provided clues about how this integration is accomplished but have not directly determined the rules regulating summation of closely timed inputs along single or multiple dendrites. Here we have examined dendritic summation of multiple inputs along On ganglion cell dendrites in whole mount rat retina. We activated inputs at targeted locations by uncaging glutamate sequentially to generate apparent motion along On ganglion cell dendrites in whole mount retina. Summation was directional and dependent13 on input sequence. Input moving away from the soma (centrifugal) resulted in supralinear summation, while activation sequences moving toward the soma (centripetal) were linear. Enhanced summation for centrifugal activation was robust as it was also observed in cultured retinal ganglion cells. This directional summation was dependent on hyperpolarization activated cyclic nucleotide-gated (HCN) channels as blockade with ZD7288 eliminated directionality. A computational model confirms that activation of HCN channels can override a preference for centripetal summation expected from cell anatomy. This type of direction selectivity could play a role in coding movement similar to the axial selectivity seen in locust ganglion cells which detect looming stimuli. More generally, these results suggest that non-directional retinal ganglion cells can discriminate between input sequences independent of the retina network.</p> </div

HCN modulates directional summation.

<p><b>A</b>) A model of the cell in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002969#pcbi-1002969-g010" target="_blank">Figure 10</a>. Circles indicate input sites. Traces are for activation of the middle (M) 3 locations moving away from the soma (color) or toward the soma (black). <b>B</b>) Plots of directional summation (DS) ([away-toward]/toward*100) for amplitude and charge. P = 3 most proximal locations, M = middle 3 locations, D = 3 most distal locations, A = alternating locations (distal, middle, proximal). <b>C</b>) A model of another cell and activation of two dendrites. <b>D</b>) Plots of DS for both dendrites.</p

The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study

Motor adaptation to perturbations is provided by learning mechanisms operating in the cerebellum and basal ganglia. The cerebellum normally performs motor adaptation through supervised learning using information about movement error provided by visual feedback. However, if visual feedback is critically distorted, the system may disengage cerebellar error-based learning and switch to reinforcement learning mechanisms mediated by basal ganglia. Yet, the exact conditions and mechanisms of cerebellum and basal ganglia involvement in motor adaptation remain unknown. We use mathematical modeling to simulate control of planar reaching movements that relies on both error-based and non-error-based learning mechanisms. We show that for learning to be efficient only one of these mechanisms should be active at a time. We suggest that switching between the mechanisms is provided by a special circuit that effectively suppresses the learning process in one structure and enables it in the other. To do so, this circuit modulates learning rate in the cerebellum and dopamine release in basal ganglia depending on error-based learning efficiency. We use the model to explain and interpret experimental data on error- and non-error-based motor adaptation under different conditions

Uncaging EPSP properties.

<p><b>A</b>) Scaled light and uncaging EPSPs (traces are averages of 5 trials). <b>B</b>) Confocal image of ganglion cell dendrites. <b>C</b>) A plot of XY resolution for the cell in B. * = photolysis.</p

Directional summation in a simple model system.

<p><b>A</b>) A cell with an unbranched dendrite was activated at multiple locations to elicit summed EPSPs. <b>B</b>) EPSPs for a model without dendritic voltage-dependent channels as measured at the soma. <b>C</b>) EPSPs after dendritic HCN channels have been added. <b>D</b>) EPSPs after Na/K channels were added to the model. <b>E</b>) EPSPS from a model with Na/K/HCN channels. <b>F</b>) A plot of the % direction selectivity (DS; [away-toward]/toward*100) for amplitude and charge in each condition from B–E.</p

Dendritic summation has a directional component.

<p><b>A</b>) A single confocal plane of an On cell showing uncaging locations over 3 dendrites. <b>B</b>) Individual EPSPs moving either away (black) or toward (grey) the soma for the dendrite in A (400 ms delay between locations; averages of 8 trials). <b>C</b>) Summed EPSPs moving away (black) or toward the soma (50 ms delay between locations; averages of 8 trials). <b>D</b>) A plot of the amplitude ratio (summed EPSP/3x single EPSP). 0.88±0.05 away, 0.73±0.04 toward; paired t-test p<0.005; N = 14 On cells; P14–18). <b>E</b>) A plot of the charge ratio for summed depolarizations moving away from or toward the soma. 1.39±0.08 away, 1.10±0.05 toward; paired t-test; p<0.005; N = 15 On cells.</p

NMDA receptors increase summation but do not confer directionality.

<p><b>A</b>) Summed traces (average of 8 trials) for activation of 3 locations along a single dendrite under control conditions. Lower: A plot of the charge ratio for all experiments. Summation was larger in the away direction (** paired t-test; p<0.01; N = 9 cells). <b>B</b>) Traces from the same 3 locations after application of APV (50 µm). Lower: A plot of the charge ratio for all experiments. Summation was still larger in the away direction (* paired t-test; p<0.05; N = 9 cells).</p