| 1. |
- Hahn, Gerald, et al.
(författare)
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Rate and oscillatory switching dynamics of a multilayer visual microcircuit model
- 2022
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Ingår i: eLIFE. - : eLife Sciences Publications, Ltd. - 2050-084X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- The neocortex is organized around layered microcircuits consisting of a variety of excitatory and inhibitory neuronal types which perform rate- and oscillation-based computations. Using modeling, we show that both superficial and deep layers of the primary mouse visual cortex implement two ultrasensitive and bistable switches built on mutual inhibitory connectivity motives between somatostatin, parvalbumin, and vasoactive intestinal polypeptide cells. The switches toggle pyramidal neurons between high and low firing rate states that are synchronized across layers through translaminar connectivity. Moreover, inhibited and disinhibited states are characterized by low- and high-frequency oscillations, respectively, with layer-specific differences in frequency and power which show asymmetric changes during state transitions. These findings are consistent with a number of experimental observations and embed firing rate together with oscillatory changes within a switch interpretation of the microcircuit.
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| 2. |
- Wärnberg, Emil
(författare)
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On learning in mice and machines : continuous population codes in natural and artificial neural networks
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Neural networks, whether artificial in a computer or natural in the brain, could represent information either using discrete symbols or continuous vector spaces. In this thesis, I explore how neural networks can represent continuous vector spaces, using both simulated neural networks and analysis of real neural population data recorded from mice. A special focus is on the networks of the basal ganglia circuit and on reinforcement learning, i.e., learning from rewards and punishments.The thesis includes four scientific papers: two theoretical/computational (Papers I and IV) and two with analysis of real data (Papers II and III).In Paper I, we explore methods for implementing continuous vector spaces in networks of spiking neurons using multidimensional attractors, and propose an explanation for why it is hard to escape the neural manifolds created by such attractors.In Paper II, we analyze experimental data from dorsomedial striatum collected using 1-photon calcium imaging of transgenic mice with celltype-specific markers for the striatal direct, indirect and patch pathways, as the mice were gathering rewards in a 2-choice task. In line with extensive previous results, our data analysis revealed a number of neural signatures of reinforcement learning, but no apparent difference between the pathways.In Paper III, we present a new software tool for tracking neurons across weeks of 1-photon calcium imaging, and employ it to follow patch-specific striatal projection neurons from the dorsomedial striatum across two weeks of daily recordings.In Paper IV, we propose a model for how the nigrostriatal dopaminergic projection could, in a biologically plausible way, convey a vector-valued error gradient to the dorsal striatum, as required for backpropagation.Based on the results of the papers and a review of existing literature, I argue that while the basal ganglia indeed make up a circuit for reinforcement learning as previously thought, this circuit represents reinforcement learning states, actions and policies using a continuous population code and not using discrete symbols.
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| 3. |
- Sandstrom, Malin, et al.
(författare)
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Recommendations for repositories and scientific gateways from a neuroscience perspective
- 2022
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective.
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| 4. |
- Ekeberg, Örjan, et al.
(författare)
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Computational Brain Science at CST, CSC, KTH
- 2016
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Mission and Vision - Computational Brain Science Lab at CST, CSC, KTHThe scientific mission of the Computational Brain Science Lab at CSC is to be at the forefront of mathematical modelling, quantitative analysis and mechanistic understanding of brain function. We perform research on (i) computational modelling of biological brain function and on (ii) developing theory, algorithms and software for building computer systems that can perform brain-like functions. Our research answers scientific questions and develops methods in these fields. We integrate results from our science-driven brain research into our work on brain-like algorithms and likewise use theoretical results about artificial brain-like functions as hypotheses for biological brain research.Our research on biological brain function includes sensory perception (vision, hearing, olfaction, pain), cognition (action selection, memory, learning) and motor control at different levels of biological detail (molecular, cellular, network) and mathematical/functional description. Methods development for investigating biological brain function and its dynamics as well as dysfunction comprises biomechanical simulation engines for locomotion and voice, machine learning methods for analysing functional brain images, craniofacial morphology and neuronal multi-scale simulations. Projects are conducted in close collaborations with Karolinska Institutet and Karolinska Hospital in Sweden as well as other laboratories in Europe, U.S., Japan and India.Our research on brain-like computing concerns methods development for perceptual systems that extract information from sensory signals (images, video and audio), analysis of functional brain images and EEG data, learning for autonomous agents as well as development of computational architectures (both software and hardware) for neural information processing. Our brain-inspired approach to computing also applies more generically to other computer science problems such as pattern recognition, data analysis and intelligent systems. Recent industrial collaborations include analysis of patient brain data with MentisCura and the startup company 13 Lab bought by Facebook.Our long term vision is to contribute to (i) deeper understanding of the computational mechanisms underlying biological brain function and (ii) better theories, methods and algorithms for perceptual and intelligent systems that perform artificial brain-like functions by (iii) performing interdisciplinary and cross-fertilizing research on both biological and artificial brain-like functions. On one hand, biological brains provide existence proofs for guiding our research on artificial perceptual and intelligent systems. On the other hand, applying Richard Feynman’s famous statement ”What I cannot create I do not understand” to brain science implies that we can only claim to fully understand the computational mechanisms underlying biological brain function if we can build and implement corresponding computational mechanisms on a computerized system that performs similar brain-like functions.
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| 5. |
- Hahn, Gerald, et al.
(författare)
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Communication through resonance in spiking neuronal networks
- 2014
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 10:8
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Tidskriftsartikel (refereegranskat)abstract
- The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance''). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.
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| 6. |
- Yim, Man Yi, et al.
(författare)
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Impact of correlated inputs to neurons : modeling observations from in vivo intracellular recordings
- 2014
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Ingår i: Journal of Computational Neuroscience. - : Springer. - 0929-5313 .- 1573-6873. ; 37:2, s. 293-304
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Tidskriftsartikel (refereegranskat)abstract
- In vivo recordings in rat somatosensory cortex suggest that excitatory and inhibitory inputs are often correlated during spontaneous and sensory-evoked activity. Using a computational approach, we study how the interplay of input correlations and timing observed in experiments controls the spiking probability of single neurons. Several correlation-based mechanisms are identified, which can effectively switch a neuron on and off. In addition, we investigate the transfer of input correlation to output correlation in pairs of neurons, at the spike train and the membrane potential levels, by considering spike-driving and non-spike-driving inputs separately. In particular, we propose a plausible explanation for the in vivo finding that membrane potentials in neighboring neurons are correlated, but the spike-triggered averages of membrane potentials preceding a spike are not: Neighboring neurons possibly receive an ongoing bombardment of correlated subthreshold background inputs, and occasionally uncorrelated spike-driving inputs.
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| 7. |
- Carannante, Ilaria, et al.
(författare)
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The impact of Parkinson’s disease on striatal network connectivity and cortico-striatal drive : an in-silico study
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Striatum, the input stage of the basal ganglia, is important for sensory-motor integration, initiation and selection of behaviour, as well as reward learning. Striatum receives glutamatergic inputs from mainly cortex and thalamus. In rodents, the striatal projection neurons (SPNs), giving rise to the direct and the indirect pathway (dSPNs and iSPNs, respectively), account for 95% of the neurons and the remaining 5% are GABAergic and cholinergic interneurons. Interneuron axon terminals as well as local dSPN and iSPN axon collaterals form an intricate striatal network. Following chronic dopamine depletion as in Parkinson’s disease (PD), both morphological and electrophysiological striatal neuronal features are altered. Our goal with this \textit{in-silico} study is twofold: a) to predict and quantify how the intrastriatal network connectivity structure becomes altered as a consequence of the morphological changes reported at the single neuron level, and b) to investigate how the effective glutamatergic drive to the SPNs would need to be altered to account for the activity level seen in SPNs during PD. In summary we find that the richness of the connectivity motifs is significantly decreased during PD, while at the same time a substantial enhancement of the effective glutamatergic drive to striatum is present.
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| 8. |
- Wärnberg, Emil, et al.
(författare)
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Perturbing low dimensional activity manifolds in spiking neuronal networks
- 2019
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Ingår i: PloS Computational Biology. - : Public Library of Science. - 1553-734X .- 1553-7358. ; 15:5
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Tidskriftsartikel (refereegranskat)abstract
- Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. In particular, this connectivity pattern appears to be constraining learning so that only neural activity patterns falling within the intrinsic manifold can be learned and elicited. Here, we use three different models of spiking neural networks (echo-state networks, the Neural Engineering Framework and Efficient Coding) to demonstrate how the intrinsic manifold can be made a direct consequence of the circuit connectivity. Using this relationship between the circuit connectivity and the intrinsic manifold, we show that learning of patterns outside the intrinsic manifold corresponds to much larger changes in synaptic weights than learning of patterns within the intrinsic manifold. Assuming larger changes to synaptic weights requires extensive learning, this observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.
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| 9. |
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| 10. |
- Bahuguna, Jyotika, et al.
(författare)
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Existence and control of Go/No-Go decision transition threshold in the striatum
- 2015
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Ingår i: PloS Computational Biology. - : PLOS. - 1553-734X .- 1553-7358. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- A typical Go/No-Go decision is suggested to be implemented in the brain via the activation of the direct or indirect pathway in the basal ganglia. Medium spiny neurons (MSNs) in the striatum, receiving input from cortex and projecting to the direct and indirect pathways express D1 and D2 type dopamine receptors, respectively. Recently, it has become clear that the two types of MSNs markedly differ in their mutual and recurrent connectivities as well as feedforward inhibition from FSIs. Therefore, to understand striatal function in action selection, it is of key importance to identify the role of the distinct connectivities within and between the two types of MSNs on the balance of their activity. Here, we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs. We show that the asymmetric connectivity of the two types of MSNs renders the striatum into a threshold device, indicating the state of cortical input rates and correlations by the relative activity rates of D1 and D2 MSNs. Next, we describe how this striatal threshold can be effectively modulated by the activity of fast spiking interneurons, by the dopamine level, and by the activity of the GPe via pallidostriatal backprojections. We show that multiple mechanisms exist in the basal ganglia for biasing striatal output in favour of either the `Go' or the `No-Go' pathway. This new understanding of striatal network dynamics provides novel insights into the putative role of the striatum in various behavioral deficits in patients with Parkinson's disease, including increased reaction times, L-Dopa-induced dyskinesia, and deep brain stimulation-induced impulsivity.
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