| 1. |
- Belic, Jovana, 1987-, et al.
(författare)
-
Behavior Discrimination Using a Discrete Wavelet Based Approach for Feature Extraction on Local Field Potentials in the Cortex and Striatum
- 2015
-
Ingår i: 7th International IEEE/EMBS Conference on Neural Engineering (NER). - : IEEE conference proceedings. - 9781467363891 ; , s. 964-967
-
Konferensbidrag (refereegranskat)abstract
- Linkage between behavioral states and neural activity is one of the most important challenges in neuroscience. The network activity patterns in the awake resting state and in the actively behaving state in rodents are not well understood, and a better tool for differentiating these states can provide insights on healthy brain functions and its alteration with disease. Therefore, we simultaneously recorded local field potentials (LFPs) bilaterally in motor cortex and striatum, and measured locomotion from healthy, freely behaving rats. Here we analyze spectral characteristics of the obtained signals and present an algorithm for automatic discrimination of the awake resting and the behavioral states. We used the Support Vector Machine (SVM) classifier and utilized features obtained by applying discrete wavelet transform (DWT) on LFPs, which arose as a solution with high accuracy.
|
|
| 2. |
- Belic, Jovana, 1987-, et al.
(författare)
-
Mapping of Cortical Avalanches to the Striatum
- 2015. - 4
-
Ingår i: Advances in Cognitive Neurodynamics. - Dordrecht : Springer Netherlands. - 9789401795470 ; , s. 291-297
-
Bokkapitel (refereegranskat)abstract
- Neuronal avalanches are found in the resting state activity of the mammaliancortex. Here we studied whether and how cortical avalanches are mappedonto the striatal circuitry, the first stage of the basal ganglia. We first demonstrate using organotypic cortex-striatum-substantia nigra cultures from rat that indeed striatal neurons respond to cortical avalanches originating in superficial layers. We simultaneously recorded spontaneous local field potentials (LFPs) in the cortical and striatal tissue using high-density microelectrode arrays. In the cortex, spontaneous neuronal avalanches were characterized by intermittent spatiotemporal activity clusters with a cluster size distribution that followed a power law with exponent 1.5. In the striatum, intermittent spatiotemporal activity was found to correlate with cortical avalanches. However, striatal negative LFP peaks (nLFPs) did not showavalanche signatures, but formed a cluster size distribution that had a much steeper drop-off, i.e., lacked large spatial clusters that are commonly expected for avalanche dynamics. The underlying de-correlation of striatal activity could have its origin in the striatum through local inhibition and/or could result from a particular mapping in the corticostriatal pathway. Here we show, using modeling, that highly convergent corticostriatal projections can map spatially extended cortical activity into spatially restricted striatal regimes.
|
|
| 3. |
- Berthet, Pierre, et al.
(författare)
-
Action selection performance of a reconfigurable Basal Ganglia inspired model with Hebbian-Bayesian Go-NoGo connectivity
- 2012
-
Ingår i: Frontiers in Behavioral Neuroscience. - : Frontiers Media SA. - 1662-5153. ; 6, s. 65-
-
Tidskriftsartikel (refereegranskat)abstract
- Several studies have shown a strong involvement of the basal ganglia (BG) in action selection and dopamine dependent learning. The dopaminergic signal to striatum, the input stage of the BG, has been commonly described as coding a reward prediction error (RPE), i.e. the difference between the predicted and actual reward. The RPE has been hypothesized to be critical in the modulation of the synaptic plasticity in cortico-striatal synapses in the direct and indirect pathway. We developed an abstract computational model of the BG, with a dual pathway structure functionally corresponding to the direct and indirect pathways, and compared its behaviour to biological data as well as other reinforcement learning models. The computations in our model are inspired by Bayesian inference, and the synaptic plasticity changes depend on a three factor Hebbian-Bayesian learning rule based on co-activation of pre- and post-synaptic units and on the value of the RPE. The model builds on a modified Actor-Critic architecture and implements the direct (Go) and the indirect (NoGo) pathway, as well as the reward prediction (RP) system, acting in a complementary fashion. We investigated the performance of the model system when different configurations of the Go, NoGo and RP system were utilized, e.g. using only the Go, NoGo, or RP system, or combinations of those. Learning performance was investigated in several types of learning paradigms, such as learning-relearning, successive learning, stochastic learning, reversal learning and a two-choice task. The RPE and the activity of the model during learning were similar to monkey electrophysiological and behavioural data. Our results, however, show that there is not a unique best way to configure this BG model to handle well all the learning paradigms tested. We thus suggest that an agent might dynamically configure its action selection mode, possibly depending on task characteristics and also on how much time is available.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Brocke, Ekaterina, et al.
(författare)
-
Efficient Integration of Coupled Electrical-Chemical Systems in Multiscale Neuronal Simulations
- 2016
-
Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 10
-
Tidskriftsartikel (refereegranskat)abstract
- Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. First, the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. Then, the components can be defined by different mathematical formalisms. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, in neuroscience, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components comprising a multiscale test problem in neuroscience. We introduce an efficient coupling method based on the second-order backward differentiation formula (BDF2) numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used in neuroscience for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale neuroscientific test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.
|
|
| 7. |
- Bruce, Neil J., et al.
(författare)
-
Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals
- 2019
-
Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 15:10
-
Tidskriftsartikel (refereegranskat)abstract
- Author summary Adenylyl cyclases (ACs) are enzymes that can translate extracellular signals into the intracellular molecule cAMP, which is thus a 2nd messenger of extracellular events. The brain expresses nine membrane-bound AC variants, and AC5 is the dominant form in the striatum. The striatum is the input stage of the basal ganglia, a brain structure involved in reward learning, i.e. the learning of behaviors that lead to rewarding stimuli (such as food, water, sugar, etc). During reward learning, cAMP production is crucial for strengthening the synapses from cortical neurons onto the striatal principal neurons, and its formation is dependent on several neuromodulatory systems such as dopamine and acetylcholine. It is, however, not understood how AC5 is activated by transient (subsecond) changes in the neuromodulatory signals. Here we combine several computational tools, from molecular dynamics and Brownian dynamics simulations to bioinformatics approaches, to inform and constrain a kinetic model of the AC5-dependent signaling system. We use this model to show how the specific molecular properties of AC5 can detect particular combinations of co-occuring transient changes in the neuromodulatory signals which thus result in a supralinear/synergistic cAMP production. Our results also provide insights into the computational capabilities of the different AC isoforms. Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory G alpha(olf) and inhibitory G alpha(i) proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if G alpha(olf) and G alpha(i) can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5's ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.
|
|
| 8. |
- Hallén, Kristofer, et al.
(författare)
-
mGluR-Mediated calcium oscillations in the lamprey: a computational model
- 2004
-
Ingår i: Neurocomputing. - AMSTERDAM : Elsevier BV. - 0925-2312 .- 1872-8286. ; 58-60, s. 431-435, s. 431-435
-
Tidskriftsartikel (refereegranskat)abstract
- Slow Ca2+ oscillations caused by release from intracellular stores have been observed in neurons in the lamprey spinal cord. These oscillations are triggered by activation of metabotropic glutamate receptors on the cell surface. The pathway leading from receptor activation to the inositol triphosphate-mediated release of Ca2+ from the endoplasmatic reticulum has been modelled in order to facilitate further understanding of the nature of these oscillations. The model generates Ca2+ oscillations with a frequency range of 0.01–0.09 Hz. A prediction of the model is that the frequency will increase with a stronger extracellular glutamate signal.
|
|
| 9. |
|
|
| 10. |
|
|
