The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

Cracking the cytoarchitectural organization, activity patterns, and neurotransmitter nature of genetically-distinct cell types in the lateral hypothalamus (LH) is fundamental to develop a mechanistic understanding of how activity dynamics within this brain region are generated and operate together through synaptic connections to regulate circuit function. However, the precise mechanisms through which LH circuits orchestrate such dynamics have remained elusive due to the heterogeneity of the intermingled and functionally distinct cell types in this brain region. Here we reveal that a cell type in the mouse LH identified by the expression of the calcium-binding protein parvalbumin (PVALB; LHPV) is fast-spiking, releases the excitatory neurotransmitter glutamate, and sends long range projections throughout the brain. Thus, our findings challenge long-standing concepts that define neurons with a fast-spiking phenotype as exclusively GABAergic. Furthermore, we provide for the first time a detailed characterization of the electrophysiological properties of these neurons. Our work identifies LHPV neurons as a novel functional component within the LH glutamatergic circuitry.

The balance between excitation (E) and inhibition (I) in neuronal networks controls the firing rate of principal cells through simple network organization, such as feedforward inhibitory circuits. Here, we demonstrate in male mice, that at the granule cell (GrC)molecular layer interneuron (MLI)-Purkinje cell (PC) pathway of the cerebellar cortex, E/I balance is dynamically controlled by short-term dynamics during bursts of stimuli, shaping cerebellar output. Using a combination of electrophysiological recordings, optogenetic stimulation, and modeling, we describe the wide range of bidirectional changes in PC discharge triggered by GrC bursts, from robust excitation to complete inhibition. At high frequency (200 Hz), increasing the number of pulses in a burst (from 3 to 7) can switch a net inhibition of PC to a net excitation. Measurements of EPSCs and IPSCs during bursts and modeling showed that this feature can be explained by the interplay between short-term dynamics of the GrC-MLI-PC pathway and E/I balance impinging on PC. Our findings demonstrate that PC firing rate is highly sensitive to the duration of GrC bursts, which may define a temporal-to-rate code transformation in the cerebellar cortex.

The classical model of basal ganglia has been refined in recent years with discoveries of subpopulations within a nucleus and previously unknown projections. One such discovery is the presence of subpopulations of arkypallidal and prototypical neurons in external globus pallidus, which was previously considered to be a primarily homogeneous nucleus. Developing a computational model of these multiple interconnected nuclei is challenging, because the strengths of the connections are largely unknown. We therefore use a genetic algorithm to search for the unknown connectivity parameters in a firing rate model. We apply a binary cost function derived from empirical firing rate and phase relationship data for the physiological and Parkinsonian conditions. Our approach generates ensembles of over 1,000 configurations, or homologies, for each condition, with broad distributions for many of the parameter values and overlap between the two conditions. However, the resulting effective weights of connections from or to prototypical and arkypallidal neurons are consistent with the experimental data. We investigate the significance of the weight variability by manipulating the parameters individually and cumulatively, and conclude that the correlation observed between the parameters is necessary for generating the dynamics of the two conditions. We then investigate the response of the networks to a transient cortical stimulus, and demonstrate that networks classified as physiological effectively suppress activity in the internal globus pallidus, and are not susceptible to oscillations, whereas parkinsonian networks show the opposite tendency. Thus, we conclude that the rates and phase relationships observed in the globus pallidus are predictive of experimentally observed higher level dynamical features of the physiological and parkinsonian basal ganglia, and that the multiplicity of solutions generated by our method may well be indicative of a natural diversity in basal ganglia networks. We propose that our approach of generating and analyzing an ensemble of multiple solutions to an underdetermined network model provides greater confidence in its predictions than those derived from a unique solution, and that projecting such homologous networks on a lower dimensional space of sensibly chosen dynamical features gives a better chance than a purely structural analysis at understanding complex pathologies such as Parkinson's disease.

Simultaneous oscillations in different frequency bands are implicated in the striatum, and understanding their interactions will bring us one step closer to restoring the spectral characteristics of striatal activity that correspond to the healthy state. We constructed a computational model of the striatum in order to investigate how different, simultaneously present, and externally induced oscillations propagate through striatal circuitry and which stimulation parameters have a significant contribution. Our results show that features of these oscillations and their interactions can be influenced via amplitude, input frequencies, and the phase offset between different external inputs. Our findings provide further untangling of the oscillatory activity that can be seen within the striatal network.

Brief epochs of beta oscillations have been implicated in sensorimotor control in the basal ganglia of task-performing healthy animals. However, which neural processes underlie their generation and how they are affected by sensorimotor processing remains unclear. To determine the mechanisms underlying transient beta oscillations in the LFP, we combined computational modeling of the subthalamo-pallidal network for the generation of beta oscillations with realistic stimulation patterns derived from single-unit data recorded from different basal ganglia subregions in rats performing a cued choice task. In the recordings, we found distinct firing patterns in the striatum, globus pallidus, and subthalamic nucleus related to sensory and motor events during the behavioral task. Using these firing patterns to generate realistic inputs to our network model led to transient beta oscillations with the same time course as the rat LFP data. In addition, our model can account for further nonintuitive aspects of beta modulation, including beta phase resets after sensory cues and correlations with reaction time. Overall, our model can explain how the combination of temporally regulated sensory responses of the subthalamic nucleus, ramping activity of the subthalamic nucleus, and movement-related activity of the globus pallidus leads to transient beta oscillations during behavior.

Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.

Mission and Vision - Computational Brain Science Lab at CST, CSC, KTH

The 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.

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.