Papers by Nickolas Kintos
Journal of Computational Neuroscience, 2016
Different neuromodulators often target the same ion channel. When such modulators act on differen... more Different neuromodulators often target the same ion channel. When such modulators act on different neuron types, this convergent action can enable a rhythmic network to produce distinct outputs. Less clear are the functional consequences when two neuromodulators influence the same ion channel in the same neuron. We examine the consequences of this seeming redundancy using a mathematical model of the crab gastric mill (chewing) network. This network is activated in vitro by the projection neuron MCN1, which elicits a half-center bursting oscillation between the reciprocally-inhibitory neurons LG and Int1. We focus on two neuropeptides which modulate this network, including a MCN1 neurotransmitter and the hormone crustacean cardioactive peptide (CCAP). Both activate the same voltage-gated current (I MI) in the LG neuron. However, I MI-MCN1 , resulting from MCN1 released neuropeptide, has phasic dynamics in its maximal conductance due to LG presynaptic inhibition of MCN1, while I MI-CCAP retains the same maximal conductance in both phases of the gastric mill rhythm. Separation of time scales allows us to produce a 2D model from which phase plane analysis shows that, as in the biological system, I MI-MCN1 and I MI-CCAP primarily influence the durations of opposing phases of this rhythm. Furthermore, I MI-MCN1 influences the rhythmic output in a manner similar to the Int1-to-LG synapse, whereas I MI-CCAP has an influence similar to the LG-to-Int1 synapse. These results show that distinct neuromodulators which target the same voltage-gated ion channel in the same network neuron can nevertheless produce distinct effects at the network level, providing divergent neuromodulator actions on network activity.
SIAM journal on applied dynamical systems, Jan 12, 2014
Rhythmic activity which underlies motor output is often initiated and controlled by descending mo... more Rhythmic activity which underlies motor output is often initiated and controlled by descending modulatory projection pathways onto central pattern generator (CPG) networks. In turn, these descending pathways receive synaptic feedback from their target CPG network, which can influence the CPG output. However, the mechanisms underlying such bi-directional synaptic interactions are mostly unexplored. We develop a reduced mathematical model, including both feed-forward and feedback circuitry, to examine how the synaptic interactions involving two projection neurons, MCN1 and CPN2, can produce and shape the activity of the gastric mill CPG in the crab stomatogastric nervous system. We use simplifying assumptions that are based on the behavior of the biological system to reduce this model down to 2 dimensions, which allows for phase plane analysis of the model output. The model shows a distinct activity for the gastric mill rhythm that is elicited when MCN1 and CPN2 are co-active compared...
Journal of Computational Neuroscience, 2008
Many central pattern generating networks are influenced by synaptic input from modulatory project... more Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is
sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network inter-actions with the projection neuron. One interesting example
occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.
Many central pattern generating networks are influenced by synaptic input from modulatory project... more Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network interactions with the projection neuron. One interesting example occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron MCN1, despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.
The abstract for this paper can be found by clicking the URL link above.
If you'd like a pre-prin... more The abstract for this paper can be found by clicking the URL link above.
If you'd like a pre-print, just send me an Email
Rhythmic activity which underlies motor output is often initiated and controlled by descending mo... more Rhythmic activity which underlies motor output is often initiated and controlled by descending modulatory projection pathways onto central pattern generator (CPG) networks. In turn, these descending pathways receive synaptic feedback from their target CPG network, which can influence the CPG output. However, the mechanisms underlying such bi-directional synaptic interactions are mostly unexplored. We develop a reduced mathematical model, including both feed-forward and feedback circuitry, to examine how the synaptic interactions involving two projection neurons, MCN1 and CPN2, can produce and shape the activity of the gastric mill CPG in the crab stomatogastric nervous system. We use simplifying assumptions that are based on the behavior of the biological system to reduce this model down to 2 dimensions, which allows for phase plane analysis of the model output. The model shows a distinct activity for the gastric mill rhythm that is elicited when MCN1 and CPN2 are co-active compared to the rhythm elicited by MCN1 activity alone. Furthermore, the presence of feedback to the projection neuron CPN2 provides a distinct locus of pattern generation in the model which does not require reciprocally inhibitory interactions between the gastric mill CPG neurons, but is instead based on a half-center oscillator that occurs through a tri-synaptic pathway that includes CPN2. Our modeling results show that feedback to projection pathways may provide additional mechanisms for the generation of motor activity. These mechanisms can have distinct dependence on network parameters and may therefore provide additional flexibility for the rhythmic motor output.
Abstract:
Many central pattern generating networks are influenced by synaptic input from modulat... more Abstract:
Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network interactions with the projection neuron. One interesting example occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.
Conference Presentations by Nickolas Kintos
Although distinct neuromodulators often target distinct components within a neural network, their... more Although distinct neuromodulators often target distinct components within a neural network, their actions can produce a convergent influence upon the network by targeting the same cellular or synaptic mechanisms, albeit in different neuron types. Such convergence of modulatory actions can provide a neural network with greater flexibility for producing a given mode of output. However, it may appear redundant or inefficient when distinct neuromodulators activate the same mechanism in the same neuron type. The purpose of such redundant actions is not well understood. We investigate what advantage such redundancy could provide using a reduced mathematical model of the gastric mill (chewing) central pattern generator network of the crab, Cancer borealis.
See attached for abstract
Rhythmic motor networks are generally studied assuming a feed-forward architecture in which desce... more Rhythmic motor networks are generally studied assuming a feed-forward architecture in which descending inputs from projection neurons initiate, terminate or modify the network output. Feedback from the target network to descending inputs is nonetheless pervasive in motor pattern generation, although its role is not well understood. We developed a mathematical model to understand how rhythmic feedback to a descending projection neuron shapes the output of the gastric mill (chewing) motor circuit in the stomatogastric ganglion (STG) of the crab C. borealis. Stimulating the projection neuron MCN1 elicits a gastric mill rhythm (GMR) in vitro, where the protractor LG neuron bursts in antiphase with the retractor neuron INT1 (Coleman et al, 1995 Nature). The half-center oscillation of the reciprocally inhibitory LG-INT1 pair underlies the MCN1-elicited GMR. Co-activation of MCN1 and the projection neuron CPN2 elicits a distinct GMR. The STG terminals of CPN2 are electrically coupled to the LG neuron, while the CPN2 soma is inhibited by a feedback synapse from INT1. Previous experiments indicated that the MCN1/CPN2-GMR persists without the inhibitory synapse from INT1 to LG, but the MCN1-GMR requires the presence of that synapse (Akay et al, 2004 SfN Abstr).
See attached for abstract
Talks by Nickolas Kintos
See attached for abstract
Projection neurons shape the activity of many neural networks. In particular, neuromodulatory sub... more Projection neurons shape the activity of many neural networks. In particular, neuromodulatory substances, which are often released by projection neurons, alter the cellular and/or synaptic properties within a target network. However, neural networks in turn influence projection neuron input via synaptic feedback. This dissertation uses mathematical and biophysically-realistic modeling to investigate these issues in the gastric mill (chewing) motor network of the crab, Cancer borealis. The projection neuron MCN1 elicits a gastric mill rhythm in which the LG neuron and INT1 burst in anti-phase due to their reciprocal inhibition. However, bath application of the neuromodulator PK elicits a similar gastric mill rhythm in the absence of MCN1 participation; yet, the mechanism that underlies the PK-elicited rhythm is unknown. This dissertation develops a 2-dimensional model that is used to propose three potential mechanisms by which PK can elicit a similar gastric mill rhythm. The network dynamics of the MCN1-elicited and PK-elicited rhythms are also compared using geometrical properties in the phase plane. Next, the two gastric mill rhythms are compared using a more biophysically-realistic model. Presynaptic inhibition of MCN1 is necessary for coordinating network activity during the MCN1-elicited rhythm. In contrast, the PK-elicited rhythm is shown to be coordinated by a synapse that is not functional during the MCN1-elicited rhythm.
Teaching Documents by Nickolas Kintos
MA-106 Introduction to Probability and Statistics (2 sections)
MA-144 Integral Calculus
MA-37... more MA-106 Introduction to Probability and Statistics (2 sections)
MA-144 Integral Calculus
MA-375 Advanced Calculus
Saint Peter's University:
MA-105 Elementary Applied Mathematics
MA-106 Introduction to Pr... more Saint Peter's University:
MA-105 Elementary Applied Mathematics
MA-106 Introduction to Probability and Statistics
MA-132 Statistics for the Life Sciences
MA-143 Differential Calculus
MA-144 Integral Calculus
MA-375 Advanced Calculus (Spring 2016)
MA-377 Ordinary Differential Equations
MA-382 Mathematical Modeling
MA-387 Topics in Mathematics
MA-504 Statistics, Probability, and Discrete Mathematics
for Middle School (Graduate education course)
Fordham University:
MATH 1100 Finite Mathematics
MATH 1203 Applied Calculus I
MATH 1206 Calculus I
MATH 1207 Calculus II
MATH 2004 Multivariable Calculus I
MATH 2006 Linear Algebra I
MATH 3002 Differential Equations
MATH 3004 Complex Analysis
CISC 6550 Systems Neuroscience (Co-instructor)
(Graduate course – Department of Computer &
Information Science)
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Papers by Nickolas Kintos
sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network inter-actions with the projection neuron. One interesting example
occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.
If you'd like a pre-print, just send me an Email
Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network interactions with the projection neuron. One interesting example occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.
Conference Presentations by Nickolas Kintos
Talks by Nickolas Kintos
Teaching Documents by Nickolas Kintos
MA-144 Integral Calculus
MA-375 Advanced Calculus
MA-105 Elementary Applied Mathematics
MA-106 Introduction to Probability and Statistics
MA-132 Statistics for the Life Sciences
MA-143 Differential Calculus
MA-144 Integral Calculus
MA-375 Advanced Calculus (Spring 2016)
MA-377 Ordinary Differential Equations
MA-382 Mathematical Modeling
MA-387 Topics in Mathematics
MA-504 Statistics, Probability, and Discrete Mathematics
for Middle School (Graduate education course)
Fordham University:
MATH 1100 Finite Mathematics
MATH 1203 Applied Calculus I
MATH 1206 Calculus I
MATH 1207 Calculus II
MATH 2004 Multivariable Calculus I
MATH 2006 Linear Algebra I
MATH 3002 Differential Equations
MATH 3004 Complex Analysis
CISC 6550 Systems Neuroscience (Co-instructor)
(Graduate course – Department of Computer &
Information Science)
sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network inter-actions with the projection neuron. One interesting example
occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.
If you'd like a pre-print, just send me an Email
Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network interactions with the projection neuron. One interesting example occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.
MA-144 Integral Calculus
MA-375 Advanced Calculus
MA-105 Elementary Applied Mathematics
MA-106 Introduction to Probability and Statistics
MA-132 Statistics for the Life Sciences
MA-143 Differential Calculus
MA-144 Integral Calculus
MA-375 Advanced Calculus (Spring 2016)
MA-377 Ordinary Differential Equations
MA-382 Mathematical Modeling
MA-387 Topics in Mathematics
MA-504 Statistics, Probability, and Discrete Mathematics
for Middle School (Graduate education course)
Fordham University:
MATH 1100 Finite Mathematics
MATH 1203 Applied Calculus I
MATH 1206 Calculus I
MATH 1207 Calculus II
MATH 2004 Multivariable Calculus I
MATH 2006 Linear Algebra I
MATH 3002 Differential Equations
MATH 3004 Complex Analysis
CISC 6550 Systems Neuroscience (Co-instructor)
(Graduate course – Department of Computer &
Information Science)