Selena Singh
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Selena Singh has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Abraham Nunes
Dalhousie University
3 shared publications - Thomas Trappenberg
Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
3 shared publications - Harvey Wang
Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
2 shared publications - Maximilian Petzi
Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
1 shared publication
Latest Publications
- Open Access
An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices
Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modeled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e., coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a three-dimensional manifold with mutually orthogonal coordinates representing the neurons’ marginal and correlational firing rates. We use this highly controlled system to examine the behavior of spike train similarity indices commonly used in pattern separation research. We find that all indices (except scaling factor) are sensitive to relative differences in marginal firing rates, but no index adequately captures differences in spike trains that result from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons but (B) insensitive to patterns that differ only in relative spike timing (e.g., synchrony between neurons in the ensemble).
Open AccessAn Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices
Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modeled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e., coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a three-dimensional manifold with mutually orthogonal coordinates representing the neurons’ marginal and correlational firing rates. We use this highly controlled system to examine the behavior of spike train similarity indices commonly used in pattern separation research. We find that all indices (except scaling factor) are sensitive to relative differences in marginal firing rates, but no index adequately captures differences in spike trains that result from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons but (B) insensitive to patterns that differ only in relative spike timing (e.g., synchrony between neurons in the ensemble).
- Open Access
An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices
Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modelled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e. coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a 3-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behaviour of spike train similarity indices commonly used in pattern separation research. We found that all indices (except scaling factor) were sensitive to relative differences in marginal firing rates, but no index adequately captured differences in spike trains that resulted from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons, but (B) insensitive to patterns that differ only in relative spike timing (e.g. synchrony between neurons in the ensemble).
Open AccessAn Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices
Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modelled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e. coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a 3-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behaviour of spike train similarity indices commonly used in pattern separation research. We found that all indices (except scaling factor) were sensitive to relative differences in marginal firing rates, but no index adequately captured differences in spike trains that resulted from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons, but (B) insensitive to patterns that differ only in relative spike timing (e.g. synchrony between neurons in the ensemble).
- Open Access
- 7 November 2023
- Published by MDPI AG in Brain Sciences
- Vol. 13 (11) , 1562
Mechanisms of Sustained Increases in γ Power Post-Ketamine in a Computational Model of the Hippocampal CA3: Implications for Ketamine’s Antidepressant Mechanism of Action
Subanaesthetic doses of ketamine increase oscillation power in neural activity measured using electroencephalography (EEG), and this effect lasts several hours after ketamine administration. The mechanisms underlying this effect are unknown. Using a computational model of the hippocampal cornu ammonis 3 (CA3) network, which is known to reproduce ketamine’s acute effects on power, we simulated the plasticity of glutamatergic synapses in pyramidal cells to test which of the following hypotheses would best explain this sustained power: the direct inhibition hypothesis, which proposes that increased power post-ketamine administration may be caused by the potentiation of recurrent collateral synapses, and the disinhibition hypothesis, which proposes that potentiation affects synapses from both recurrent and external inputs. Our results suggest that the strengthening of external connections to pyramidal cells is able to account for the sustained power increase observed post-ketamine by increasing the overall activity of and synchrony between pyramidal cells. The strengthening of recurrent pyramidal weights, however, would cause an additional phase shifted voltage increase that ultimately reduces power due to partial cancellation. Our results therefore favor the disinhibition hypothesis for explaining sustained oscillations after ketamine administration.- 7 November 2023
- Published by MDPI AG in Brain Sciences
- Vol. 13 (11) , 1562
Open AccessMechanisms of Sustained Increases in γ Power Post-Ketamine in a Computational Model of the Hippocampal CA3: Implications for Ketamine’s Antidepressant Mechanism of Action
Subanaesthetic doses of ketamine increase oscillation power in neural activity measured using electroencephalography (EEG), and this effect lasts several hours after ketamine administration. The mechanisms underlying this effect are unknown. Using a computational model of the hippocampal cornu ammonis 3 (CA3) network, which is known to reproduce ketamine’s acute effects on power, we simulated the plasticity of glutamatergic synapses in pyramidal cells to test which of the following hypotheses would best explain this sustained power: the direct inhibition hypothesis, which proposes that increased power post-ketamine administration may be caused by the potentiation of recurrent collateral synapses, and the disinhibition hypothesis, which proposes that potentiation affects synapses from both recurrent and external inputs. Our results suggest that the strengthening of external connections to pyramidal cells is able to account for the sustained power increase observed post-ketamine by increasing the overall activity of and synchrony between pyramidal cells. The strengthening of recurrent pyramidal weights, however, would cause an additional phase shifted voltage increase that ultimately reduces power due to partial cancellation. Our results therefore favor the disinhibition hypothesis for explaining sustained oscillations after ketamine administration.