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  • 1.
    Berthet, Pierre
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm Univ, Sweden; Karolinska Inst, Sweden.
    Lindahl, Mikael
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Inst, Sweden.
    Tully, Philip J.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Inst, Sweden; Univ Edinburgh, Scotland.
    Hellgren-Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Inst, Sweden.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm Univ, Sweden; Karolinska Inst, Sweden.
    Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity2016In: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 10, article id 53Article in journal (Refereed)
    Abstract [en]

    The brain enables animals to behaviorally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviors are achieved and computed by its underlying neural circuitry is an open question. To address this concern, we have developed a spiking model of the basal ganglia (BG) that learns to dis-inhibit the action leading to a reward despite ongoing changes in the reward schedule. The architecture of the network features the two pathways commonly described in BG, the direct (denoted D1) and the indirect (denoted D2) pathway, as well as a loop involving striatum and the dopaminergic system. The activity of these dopaminergic neurons conveys the reward prediction error (RPE), which determines the magnitude of synaptic plasticity within the different pathways. All plastic connections implement a versatile four-factor learning rule derived from Bayesian inference that depends upon pre- and post-synaptic activity, receptor type, and dopamine level. Synaptic weight updates occur in the D1 or D2 pathways depending on the sign of the RPE, and an efference copy informs upstream nuclei about the action selected. We demonstrate successful performance of the system in a multiple-choice learning task with a transiently changing reward schedule. We simulate lesioning of the various pathways and show that a condition without the D2 pathway fares worse than one without D1. Additionally, we simulate the degeneration observed in Parkinson's disease (PD) by decreasing the number of dopaminergic neurons during learning. The results suggest that the D1 pathway impairment in PD might have been overlooked. Furthermore, an analysis of the alterations in the synaptic weights shows that using the absolute reward value instead of the RPE leads to a larger change in D1.

  • 2.
    Kaplan, Bernhard A.
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system2014In: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 8, no Feb, p. 5-Article in journal (Refereed)
    Abstract [en]

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

  • 3.
    Krishnamurthy, Pradeep
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Silberberg, Gilad
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Sweden.
    Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model2015In: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 9, article id 60Article in journal (Refereed)
    Abstract [en]

    Although the importance of long-range connections for cortical information processing has been acknowledged for a long time, most studies focused on the long-range interactions between excitatory cortical neurons. Inhibitory interneurons play an important role in cortical computation and have thus far been studied mainly with respect to their local synaptic interactions within the cortical microcircuitry. A recent study showed that long-range excitatory connections onto Martinotti cells (MC) mediate surround suppression. Here we have extended our previously reported attractor network of pyramidal cells (PC) and MC by introducing long-range connections targeting MC. We have demonstrated how the network with Martinotti cell-mediated long-range inhibition gives rise to surround suppression and also promotes saliency of locations at which simple non-uniformities in the stimulus field are introduced. Furthermore, our analysis suggests that the presynaptic dynamics of MC is only ancillary to its orientation tuning property in enabling the network with saliency detection. Lastly, we have also implemented a disinhibitory pathway mediated by another interneuron type (VIP interneurons), which inhibits MC and abolishes surround suppression.

  • 4. Lindroos, Robert
    et al.
    Dorst, Matthijs C.
    Du, Kai
    Filipovic, Marko
    Keller, Daniel
    Ketzef, Maya
    Kozlov, Alexander K.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kumar, Arvind
    Lindahl, Mikael
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nair, Anu G.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Perez-Fernandez, Juan
    Grillner, Sten
    Silberberg, Gilad
    Hällgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.22018In: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 12, article id 3Article in journal (Refereed)
    Abstract [en]

    The basal ganglia are involved in the motivational and habitual control of motor and cognitive behaviors. Striatum, the largest basal ganglia input stage, integrates cortical and thalamic inputs in functionally segregated cortico-basal ganglia-thalamic loops, and in addition the basal ganglia output nuclei control targets in the brainstem. Striatal function depends on the balance between the direct pathway medium spiny neurons (D1-MSNs) that express D1 dopamine receptors and the indirect pathway MSNs that express D2 dopamine receptors. The striatal microstructure is also divided into striosomes and matrix compartments, based on the differential expression of several proteins. Dopaminergic afferents from the midbrain and local cholinergic interneurons play crucial roles for basal ganglia function, and striatal signaling via the striosomes in turn regulates the midbrain dopaminergic system directly and via the lateral habenula. Consequently, abnormal functions of the basal ganglia neuromodulatory system underlie many neurological and psychiatric disorders. Neuromodulation acts on multiple structural levels, ranging from the subcellular level to behavior, both in health and disease. For example, neuromodulation affects membrane excitability and controls synaptic plasticity and thus learning in the basal ganglia. However, it is not clear on what time scales these different effects are implemented. Phosphorylation of ion channels and the resulting membrane effects are typically studied over minutes while it has been shown that neuromodulation can affect behavior within a few hundred milliseconds. So how do these seemingly contradictory effects fit together? Here we first briefly review neuromodulation of the basal ganglia, with a focus on dopamine. We furthermore use biophysically detailed multi-compartmental models to integrate experimental data regarding dopaminergic effects on individual membrane conductances with the aim to explain the resulting cellular level dopaminergic effects. In particular we predict dopaminergic effects on Kv4.2 in D1-MSNs. Finally, we also explore dynamical aspects of the onset of neuromodulation effects in multi-scale computational models combining biochemical signaling cascades and multi-compartmental neuron models.

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