Definitions of state variables and state space for brain–computer interface

The hypothesis is proposed that the central dynamics of the action–perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action–perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain. http://sulcus.berkeley.edu     

Definitions of state variables and state space for brain-computer interface

Neocortical state variables are defined and evaluated at three levels: microscopic using multiple spike activity (MSA), mesoscopic using local field potentials (LFP) and electrocorticograms (ECoG), and macroscopic using electroencephalograms (EEG) and brain imaging. Transactions between levels occur in all areas of cortex, upwardly by integration (abstraction, generalization) and downwardly by differentiation (speciation). The levels are joined by circular causality: microscopic activity upwardly creates mesoscopic order parameters, which downwardly constrain the microscopic activity that creates them. Integration dominates in sensory cortices. Microscopic activity evoked by receptor input in sensation induces emergence of mesoscopic activity in perception, followed by integration of perceptual activity into macroscopic activity in concept formation. The reverse process dominates in motor cortices, where the macroscopic activity embodying the concepts supports predictions of future states as goals. These macroscopic states are conceived to order mesoscopic activity in patterns that constitute plans for actions to achieve the goals. These planning patterns are conceived to provide frames in which the microscopic activity evolves in trajectories that adapted to the immediate environmental conditions detected by new stimuli. This circular sequence forms the action-perception cycle. Its upward limb is understood through correlation of sensory cortical activity with behavior. Now brain-machine interfaces (BMI) offer a means to understand the downward sequence through correlation of behavior with motor cortical activity, beginning with macroscopic goal states and concluding with recording of microscopic MSA trajectories that operate neuroprostheses. Part 1 develops a hypothesis that describes qualitatively the neurodynamics that supports the action-perception cycle and derivative reflex arc. Part 2 describes episodic, “cinematographic” spatial pattern formation and predicts some properties of the macroscopic and mesoscopic frames by which the embedded trajectories of the microscopic activity of cortical sensorimotor neurons might be organized and controlled. URL: http://sulcus.berkeley.edu     

Subjective visual perception: from local processing to emergent phenomena of brain activity

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.     

Mesoscopic neuron population modeling of normal/epileptic brain dynamics

Simulations of EEG data provide the understanding of how the limbic system exhibits normal and abnormal states of the electrical activity of the brain. While brain activity exhibits a type of homeostasis of excitatory and inhibitory mesoscopic neuron behavior, abnormal neural firings found in the seizure state exhibits brain instability due to runaway oscillatory entrained neural behavior. We utilize a model of mesoscopic brain activity, the KIV model, where each network represents the areas of the limbic system, i.e., hippocampus, sensory cortex, and the amygdala. Our model initially demonstrates oscillatory entrained neural behavior as the epileptogenesis, and then by increasing the external weights that join the three networks that represent the areas of the limbic system, seizure activity entrains the entire system. By introducing an external signal into the model, simulating external electrical titration therapy, the modeled seizure behavior can be ‘rebalanced’ back to its normal state.     

Order in spontaneous behavior

Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered Drosophila fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. Drosophila can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents.     

Intrinsic activity in the fly brain gates visual information during behavioral choices

The small insect brain is often described as an input/output system that executes reflex-like behaviors. It can also initiate neural activity and behaviors intrinsically, seen as spontaneous behaviors, different arousal states and sleep. However, less is known about how intrinsic activity in neural circuits affects sensory information processing in the insect brain and variability in behavior. Here, by simultaneously monitoring Drosophila's behavioral choices and brain activity in a flight simulator system, we identify intrinsic activity that is associated with the act of selecting between visual stimuli. We recorded neural output (multiunit action potentials and local field potentials) in the left and right optic lobes of a tethered flying Drosophila, while its attempts to follow visual motion (yaw torque) were measured by a torque meter. We show that when facing competing motion stimuli on its left and right, Drosophila typically generate large torque responses that flip from side to side. The delayed onset (0.1-1 s) and spontaneous switch-like dynamics of these responses, and the fact that the flies sometimes oppose the stimuli by flying straight, make this behavior different from the classic steering reflexes. Drosophila, thus, seem to choose one stimulus at a time and attempt to rotate toward its direction. With this behavior, the neural output of the optic lobes alternates; being augmented on the side chosen for body rotation and suppressed on the opposite side, even though the visual input to the fly eyes stays the same. Thus, the flow of information from the fly eyes is gated intrinsically. Such modulation can be noise-induced or intentional; with one possibility being that the fly brain highlights chosen information while ignoring the irrelevant, similar to what we know to occur in higher animals.     

Electrophysiological evidence for spatiotemporal flexibility in the ventrolateral attention network

Successful completion of many everyday tasks depends on interactions between voluntary attention, which acts to maintain current goals, and reflexive attention, which enables responding to unexpected events by interrupting the current focus of attention. Past studies, which have mostly examined each attentional mechanism in isolation, indicate that volitional and reflexive orienting depend on two functionally specialized cortical networks in the human brain. Here we investigated how the interplay between these two cortical networks affects sensory processing and the resulting overt behavior. By combining measurements of human performance and electrocortical recordings with a novel analytical technique for estimating spatiotemporal activity in the human cortex, we found that the subregions that comprise the reflexive ventrolateral attention network dissociate both spatially and temporally as a function of the nature of the sensory information and current task demands. Moreover, we found that together with the magnitude of the early sensory gain, the spatiotemporal neural dynamics accounted for the high amount of the variance in the behavioral data. Collectively these data support the conclusion that the ventrolateral attention network is recruited flexibly to support complex behaviors.     

Pattern orthogonalization via channel decorrelation by adaptive networks

The early processing of sensory information by neuronal circuits often includes a reshaping of activity patterns that may facilitate further processing in the brain. For instance, in the olfactory system the activity patterns that related odors evoke at the input of the olfactory bulb can be highly similar. Nevertheless, the corresponding activity patterns of the mitral cells, which represent the output of the olfactory bulb, can differ significantly from each other due to strong inhibition by granule cells and peri-glomerular cells. Motivated by these results we study simple adaptive inhibitory networks that aim to separate or even orthogonalize activity patterns representing similar stimuli. Since the animal experiences the different stimuli at different times it is difficult for the network to learn the connectivity based on their similarity; biologically it is more plausible that learning is driven by simultaneous correlations between the input channels. We investigate the connection between pattern orthogonalization and channel decorrelation and demonstrate that networks can achieve effective pattern orthogonalization through channel decorrelation if they simultaneously equalize their output levels. In feedforward networks biophysically plausible learning mechanisms fail, however, for even moderately similar input patterns. Recurrent networks do not have that limitation; they can orthogonalize the representations of highly similar input patterns. Even when they are optimized for linear neuronal dynamics they perform very well when the dynamics are nonlinear. These results provide insights into fundamental features of simplified inhibitory networks that may be relevant for pattern orthogonalization by neuronal circuits in general.     

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.     

Biological and bionic hands: natural neural coding and artificial perception

The first decade and a half of the twenty-first century brought about two major innovations in neuroprosthetics: the development of anthropomorphic robotic limbs that replicate much of the function of a native human arm and the refinement of algorithms that decode intended movements from brain activity. However, skilled manipulation of objects requires somatosensory feedback, for which vision is a poor substitute. For upper-limb neuroprostheses to be clinically viable, they must therefore provide for the restoration of touch and proprioception. In this review, I discuss efforts to elicit meaningful tactile sensations through stimulation of neurons in somatosensory cortex. I focus on biomimetic approaches to sensory restoration, which leverage our current understanding about how information about grasped objects is encoded in the brain of intact individuals. I argue that not only can sensory neuroscience inform the development of sensory neuroprostheses, but also that the converse is true: stimulating the brain offers an exceptional opportunity to causally interrogate neural circuits and test hypotheses about natural neural coding.     

Shaping the dynamics of a bidirectional neural interface

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.     

Shaping embodied neural networks for adaptive goal-directed behavior

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.     

A Dynamic Neural Field Model of Mesoscopic Cortical Activity Captured with Voltage-Sensitive Dye Imaging

A neural field model is presented that captures the essential non-linear characteristics of activity dynamics across several millimeters of visual cortex in response to local flashed and moving stimuli. We account for physiological data obtained by voltage-sensitive dye (VSD) imaging which reports mesoscopic population activity at high spatio-temporal resolution. Stimulation included a single flashed square, a single flashed bar, the line-motion paradigm – for which psychophysical studies showed that flashing a square briefly before a bar produces sensation of illusory motion within the bar – and moving squares controls. We consider a two-layer neural field (NF) model describing an excitatory and an inhibitory layer of neurons as a coupled system of non-linear integro-differential equations. Under the assumption that the aggregated activity of both layers is reflected by VSD imaging, our phenomenological model quantitatively accounts for the observed spatio-temporal activity patterns. Moreover, the model generalizes to novel similar stimuli as it matches activity evoked by moving squares of different speeds. Our results indicate that feedback from higher brain areas is not required to produce motion patterns in the case of the illusory line-motion paradigm. Physiological interpretation of the model suggests that a considerable fraction of the VSD signal may be due to inhibitory activity, supporting the notion that balanced intra-layer cortical interactions between inhibitory and excitatory populations play a major role in shaping dynamic stimulus representations in the early visual cortex.     

A cultured human neural network operates a robotic actuator

The development of bio-electronic prostheses, hybrid human-electronics devices and bionic robots has been the aim of many researchers. Although neurophysiologic processes have been widely investigated and bio-electronics has developed rapidly, the dynamics of a biological neuronal network that receive sensory inputs, store and control information is not yet understood. Toward this end, we have taken an interdisciplinary approach to study the learning and response of biological neural networks to complex stimulation patterns. This paper describes the design, execution, and results of several experiments performed in order to investigate the behavior of complex interconnected structures found in biological neural networks. The experimental design consisted of biological human neurons stimulated by parallel signal patterns intended to simulate complex perceptions. The response patterns were analyzed with an innovative artificial neural network (ANN), called ITSOM (Inductive Tracing Self Organizing Map). This system allowed us to decode the complex neural responses from a mixture of different stimulations and learned memory patterns inherent in the cell colonies. In the experiment described in this work, neurons derived from human neural stem cells were connected to a robotic actuator through the ANN analyzer to demonstrate our ability to produce useful control from simulated perceptions stimulating the cells. Preliminary results showed that in vitro human neuron colonies can learn to reply selectively to different stimulation patterns and that response signals can effectively be decoded to operate a minirobot. Lastly the fascinating performance of the hybrid system is evaluated quantitatively and potential future work is discussed.     

Neuronal activity during spontaneous walking--I. Starting and stopping

1. The spontaneous release of walking in the locust is preceded by a preparatory phase consisting of "intentional" movements. This phase is absent when walking is evoked by sensory stimuli. 2. Some brain and suboesophageal (SOG) descending interneurons (DINs) become active before the preparatory phase. 3. During and after the preparatory phase new DINs are recruited predominantly from the SOG. 4. A large variety of activity patterns was recorded during spontaneous starts. These activity patterns are absent when walking is evoked by sensory stimulation. 5. Similarly complex and long lasting patterns were recorded during spontaneous stopping. 6. The data lead to refinement of the consensus model.     

Dynamics of a neural system with a multiscale architecture

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales-neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are 'slaved' to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested.     

A field-theoretic approach to understanding scale-free neocortical dynamics

A mesoscopic field-theoretic approach is compared with neural network and brain imaging approaches to understanding brain dynamics. Analysis of high spatiotemporal resolution rabbit electroencephalogram (EEG) reveals neural fields in the form of spatial patterns in amplitude (AM) and phase (PM) modulation of gamma and beta carrier waves that serve to classify EEGs from trials with differing conditioned stimuli (CS+/−). Paleocortex exemplified by olfactory EEG has one AM–PM pattern at a time that forms by an input-dependent phase transition. Neocortex shows multiple overlapping AM–PM patterns before and during presentation of CSs. Modeling suggests that neocortex is stabilized in a scale-free state of self-organized criticality, enabling cooperative domains to form virtually instantaneously by phase transitions ranging in size from a few hypercolumns to an entire hemisphere. Self-organized local domains precede formation of global domains that supervene and contribute global modulations to local domains. This mechanism is proposed to explain Gestalt formation in perception.     

Mesoscopic Organization Reveals the Constraints Governing Caenorhabditis elegans Nervous System

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. The coordination of many different co-occurring processes at this level underlies the command and control of overall network activity. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations such as, optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Even including information about the genetic relatedness of the cells cannot account for the observed modular structure. Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.     

An emergent mechanism of selective visual attention in Drosophila

Due to the limited computational capacity of visual systems and the limited capacity to perform several mental operations at once, animals only select a small proportion of the stimuli available at any one time. It remains to be clarified how this process is related to the spatio-temporal dynamics of cell assemblies in the brain. By employing the flight simulator, selective visual attention behavior is studied in Drosophila. It has been found that for the visual objects presented, the tethered fruitflies display various attention patterns. Specifically, the learning memory mutants dunce and amnesiac possess attention patterns totally different from that of the wild-type fly. To explain these results from the viewpoint of dynamic cell assemblies, a neural network has been developed in which a possible link between the activity of cell assemblies, encoding of sensory information, and selective attention in Drosophila is proposed. Received: 3 November 1998 / Accepted in revised form: 1 July 1999     

Coordination dynamics: (in)stability and metastability in the behavioural and neural systems

For more than 20 years, coordination dynamics have provided research on human movement science with new views about the nonlinear relationships between behavioral and neural dynamics. A number of studies across various experimental settings including bimanual, postural or interpersonal coordination, and also coordination between movements of a limb and an external event in the environment revealed the self-organized nature of human coordination. Here we review an extensive body of literature - in the human movement science and the neuroscience fields - that has investigated the coordination dynamics of brain and behavior when individuals are involved in two rhythmic coordination patterns: synchronization (on-the-beat movements) and syncopation (in-between beats movements). When the frequency of movement approaches 2 Hz, the syncopation mode is destabilized and synchronization is spontaneously adopted. The abrupt change between the two patterns illustrates a phenomenon known as non-equilibrium phase transition. Phase transitions offer a novel entry point into the investigation of pattern formation (and dissolution) at both the behavioral and the cerebral levels as they illustrate the loss of stability of the system. Brain imaging methods (MEG, EEG and fMRI) were used to reveal the neural signatures of (in)stability underlying the differences between behavioral coordination patterns, and pointed at the role of self-organization and metastability principles in brain functioning. Relationships between behavioral and brain dynamics can therefore be investigated within a unified empirical and theoretical framework.