Jozsef Csicsvari

Bio: Jozsef Csicsvari is an academic researcher from Institute of Science and Technology Austria. The author has contributed to research in topics: Population & Hippocampal formation. The author has an hindex of 42, co-authored 65 publications receiving 13452 citations. Previous affiliations of Jozsef Csicsvari include University of Oxford & Rutgers University.

Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements

Abstract: Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward ...

Intracellular Features Predicted by Extracellular Recordings in the Hippocampus In Vivo

Abstract: Multichannel tetrode array recording in awake behaving animals provides a powerful method to record the activity of large numbers of neurons. The power of this method could be extended if further i...

Oscillatory Coupling of Hippocampal Pyramidal Cells and Interneurons in the Behaving Rat

Abstract: We examined whether excitation and inhibition are balanced in hippocampal cortical networks. Extracellular field and single-unit activity were recorded by multiple tetrodes and multisite silicon probes to reveal the timing of the activity of hippocampal CA1 pyramidal cells and classes of interneurons during theta waves and sharp wave burst (SPW)-associated field ripples. The somatic and dendritic inhibition of pyramidal cells was deduced from the activity of interneurons in the pyramidal layer [int(p)] and in the alveus and st. oriens [int(a/o)], respectively. Int(p) and int(a/o) discharged an average of 60 and 20° before the population discharge of pyramidal cells during the theta cycle, respectively. SPW ripples were associated with a 2.5-fold net increase of excitation. The discharge frequency of int(a/o) increased, decreased (“anti-SPW” cells), or did not change (“SPW-independent” cells) during SPW, suggesting that not all interneurons are innervated by pyramidal cells. Int(p) either fired together with (unimodal cells) or both before and after (bimodal cells) the pyramidal cell burst. During fast-ripple oscillation, the activity of interneurons in both the int(p) and int(a/o) groups lagged the maximum discharge probability of pyramidal neurons by 1–2 msec. Network state changes, as reflected by field activity, covaried with changes in the spike train dynamics of single cells and their interactions. Summed activity of parallel-recorded interneurons, but not of pyramidal cells, reliably predicted theta cycles, whereas the reverse was true for the ripple cycles of SPWs. We suggest that network-driven excitability changes provide temporal windows of opportunity for single pyramidal cells to suppress, enable, or facilitate selective synaptic inputs.

Replay and Time Compression of Recurring Spike Sequences in the Hippocampus

Abstract: Information in neuronal networks may be represented by the spatiotemporal patterns of spikes. Here we examined the temporal coordination of pyramidal cell spikes in the rat hippocampus during slow-wave sleep. In addition, rats were trained to run in a defined position in space (running wheel) to activate a selected group of pyramidal cells. A template-matching method and a joint probability map method were used for sequence search. Repeating spike sequences in excess of chance occurrence were examined by comparing the number of repeating sequences in the original spike trains and in surrogate trains after Monte Carlo shuffling of the spikes. Four different shuffling procedures were used to control for the population dynamics of hippocampal neurons. Repeating spike sequences in the recorded cell assemblies were present in both the awake and sleeping animal in excess of what might be predicted by random variations. Spike sequences observed during wheel running were “replayed” at a faster timescale during single sharp-wave bursts of slow-wave sleep. We hypothesize that the endogenously expressed spike sequences during sleep reflect reactivation of the circuitry modified by previous experience. Reactivation of acquired sequences may serve to consolidate information.

Human-level control through deep reinforcement learning

Abstract: The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Complex brain networks: graph theoretical analysis of structural and functional systems

Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

Neuronal Oscillations in Cortical Networks

Abstract: Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain’s design? Mammalian cortical neurons form behavior-dependent oscillating networks of various sizes, which span five orders of magnitude in frequency. These oscillations are phylogenetically preserved, suggesting that they are functionally relevant. Recent findings indicate that network oscillations bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and long-term consolidation of information.

Rhythms of the brain

Abstract: Prelude. Cycle 1. Introduction. Cycle 2. Structure defines function. Cycle 3. Diversity of cortical functions is provided by inhibition. Cycle 4. Windows on the brain. Cycle 5. A system of rhythms: from simple to complex dynamics. Cycle 6. Synchronization by oscillation. Cycle 7. The brain's default state: self-organized oscillations in rest and sleep. Cycle 8. Perturbation of the default patterns by experience. Cycle 9. The gamma buzz: gluing by oscillations in the waking brain. Cycle 10. Perceptions and actions are brain state-dependent. Cycle 11. Oscillations in the "other cortex:" navigation in real and memory space. Cycle 12. Coupling of systems by oscillations. Cycle 13. The tough problem. References.

Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus.

Abstract: Exposure to an enriched environment increases neurogenesis in the dentate gyrus of adult rodents. Environmental enrichment, however, typically consists of many components, such as expanded learning opportunities, increased social interaction, more physical activity and larger housing. We attempted to separate components by assigning adult mice to various conditions: water-maze learning (learner), swim-time-yoked control (swimmer), voluntary wheel running (runner), and enriched (enriched) and standard housing (control) groups. Neither maze training nor yoked swimming had any effect on bromodeoxyuridine (BrdU)-positive cell number. However, running doubled the number of surviving newborn cells, in amounts similar to enrichment conditions. Our findings demonstrate that voluntary exercise is sufficient for enhanced neurogenesis in the adult mouse dentate gyrus.