Biorobotics/Biomechanics

Spinal cord

Modelling

Neuron

Brain

Analytics

Intracellular

Cerebellum

For three decades our founders challenged the common consensus of one of the biggest scientific questions of our time: How does the brain work?

IntuiCell | Research

Born in the intersection
of AI and Neuroscience.

IntuiCell is a Lund University spin-off with presence in Lund and Stockholm. 

For three decades our founders challenged the common consensus 
of one of the biggest scientific questions of our time:

Today, we apply those groundbreaking neurophysiological insights 
to redesign artificial intelligence. Creating AI that is 

Explore our contrarian research on the mechanisms by which the 
brain predicts the world.

Research

CO Norrsken House
Birger Jarlsgatan 57C
SE-113 56 Stockholm

About

AI isn't actually intelligent. IntuiCell already is.

IntuiCell

The function of the brain function is defined by the interactions between its neurons. But these neurons exist in tremendous numbers, are continuously active and densely interconnected. Thereby they form one of the most complex dynamical systems known and there is a lack of approaches to characterize the functional properties of such biological neuronal networks. Here we introduce an approach to describe these functional properties by using its activity-defining constituents, the weights of the synaptic connections and the current activity of its neurons. We show how a high-dimensional vector field, which describes how the activity distribution across the neuron population is impacted at each instant of time, naturally emerges from these constituents. We show why a mixture of excitatory and inhibitory neurons and a diversity of synaptic weights are critical to obtain a network vector field with a structural richness. We argue that this structural richness is the foundation of activity diversity in the brain and thereby an underpinning of the behavioral flexibility and adaptability that characterizes biological creatures.

The function of the brain function is defined by the interactions between its neurons.

Neuronal networks quantified as vector fields

Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population-level signal complexity, we used highly repeatable resolvable visual, tactile, and visuo-tactile inputs and neuronal unit activity recorded at high temporal resolution. We found the state space of the spontaneous activity to be extremely high-dimensional in S1 populations. Their processing of tactile inputs was profoundly modulated by visual inputs and even fine nuances of visual input patterns were separated. Moreover, the dynamic activity states of the S1 neuron population signaled the preceding specific input long after the stimulation had terminated, i.e., resident information that could be a substrate for a working memory. Hence, the recorded high-dimensional representations carried rich multimodal and internal working memory-like signals supporting high complexity in cortical circuitry operation.

Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain.

High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations

How biological brains can learn so quickly to become operational and achieve complex behavior remains an unresolved issue. Here we introduce a ‘neuromorphic’ learning strategy that resembles how immature biological brains learn by consisting of continual random activations of a complex mechanically coupled system with rich, dynamic intrinsic sensor dependencies, in this regard reminiscent of a biological body. Using a dynamic model of biological skin tissue with embedded sensors, we trained small, recurrent networks that emulated a primordial cortex and featured excitatory and inhibitory neurons with simultaneous independent learning in both types of synapses. Training with non-repetitive, random activations of the skin, where the recurrent network activity state was inherited between activations, autonomously led to rapid acquisition of remarkably generalizable representations of a predictive nature. The network could separate inputs and solve a kinematics task that had never been encountered, also after substantial parts of the sensor population were deleted. This strategy of focussing learning on the dominant regularities in dynamic sensory information can potentially explain complex brain operation and efficient learning.

How biological brains can learn so quickly to become operational and achieve complex behavior remains an unresolved issue.

Sensory dependencies rapidly and autonomously yield generalizable representations in recurrent cortical-like networks

Many studies have suggested that the neocortex operates as a global network of functionally interconnected neurons, indicating that any sensory input could shift activity distributions across the whole brain. A tool assessing the activity distribution across cortical regions with high temporal resolution could then potentially detect subtle changes that may pass unnoticed in regionalized analyses. We used eight-channel, distributed electrocorticogram (ECoG) recordings to analyze changes in global activity distribution caused by single pulse electrical stimulations of the paw. We analyzed the temporally evolving patterns of the activity distributions using principal component analysis (PCA). We found that the localized tactile stimulation caused clearly measurable changes in global ECoG activity distribution. These changes in signal activity distribution patterns were detectable across a small number of ECoG channels, even when excluding the somatosensory cortex, suggesting that the method has high sensitivity, potentially making it applicable to human electroencephalography (EEG) for detection of pathological changes.

Many studies have suggested that the neocortex operates as a global network of functionally interconnected neurons, indicating that any sensory input could shift activity distributions across the whole brain.

ECoG activity distribution patterns detects global cortical responses following weak tactile inputs

Time-frequency decomposition is a well-established method to unmix signals generated by multiple sources with unique characteristics. However, there are cases of high signal complexity where existing time-frequency decomposition tools are insufficient for localizing and representing short-bursting signals. One example is the currently highly popular extracellular low-impedance recordings from multi-electrode arrays in the brain 

 where each neuron repeatedly generates a specific signal ‘fingerprint’ (characteristic spike waveform) that can be mixed with the signals of 100s of other sources, including the spikes of nearby neurons. Here we derive the singular superlet transform (SST) method, which enables highly localized representations of fast and short bursts compared to other super-resolution spectral estimators, while also requiring orders of magnitude fewer operations. We demonstrate a substantial edge of SST over current methods in isolating specific neuronal spikes with high-fidelity in challenging, complex recording signals from neocortex 

. We also exemplify SST’s generic signal processing capability by achieving outstanding resolution in the decomposition of complex acoustic data.

Time-frequency decomposition is a well-established method to unmix signals generated by multiple sources with unique characteristics.

Singular superlet transform achieves markedly improved time-frequency super-resolution for separating complex neural signals

The spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments. Using low noise intracellular whole cell recordings in the decerebrated, non-anesthetized cat in vivo, we could define mono-, di-, and trisynaptic inputs as well as the weights of each input. Whereas each neuron had a highly specific input, and each indirect input could moreover be explained by inputs in other recorded neurons, we unexpectedly also found the input connectivity of the spinal interneuron population to form a continuum. Our data hence contrasts with the currently widespread notion of distinct classes of interneurons. We argue that this suggested diversified physiological connectivity, which likely requires a major component of circuitry learning, implies a more flexible functionality.

The spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments.

Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons

Hodgkin and Huxley's seminal neuron model describes the propagation of voltage spikes in 

 features crucial for understanding the neural code. We consider channel current fluctuations in a trisection of the Hodgkin-Huxley model, allowing an analytic-mechanistic explanation of these features and yielding consistently excellent matches with 

 recordings of cerebellar Purkinje neurons, which we use as model systems. This shows that the neuronal encoding is described conclusively by a soft-thresholding function having just three parameters.

Hodgkin and Huxley's seminal neuron model describes the propagation of voltage spikes in axons, but it cannot explain certain full-neuron features crucial for understanding the neural code. 

Channel current fluctuations conclusively explain neuronal encoding of internal potential into spike trains

 that was able to learn and discriminate 

 model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the 

 between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across 

, makes this functional spiking tactile system effective and suitable for further 

We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism.

Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions

Neuroanatomy suggests that adjacent neocortical neurons share a similar set of afferent synaptic inputs, as opposed to neurons localized to different areas of the neocortex. In the present study, we made simultaneous single-electrode patch clamp recordings from two or three adjacent neurons in the primary somatosensory cortex (S1) of the ketamine-xylazine anesthetized rat 

 to study the correlation patterns in their spike firing during both spontaneous and sensory-evoked activity. One difference with previous studies of pairwise neuronal spike firing correlations was that here we identified several different quantifiable parameters in the correlation patterns by which different pairs could be compared. The questions asked were if the correlation patterns between adjacent pairs were similar and if there was a relationship between the degree of similarity and the layer location of the pairs. In contrast, our results show that for putative pyramidal neurons within layer III and within layer V, each pair of neurons is to some extent unique in terms of their spiking correlation patterns. Interestingly, our results also indicated that these correlation patterns did not substantially alter between spontaneous and evoked activity. Our findings are compatible with the view that the synaptic input connectivity to each neocortical neuron is at least in some aspects unique. A possible interpretation is that plasticity mechanisms, which could either be initiating or be supported by transcriptomic differences, tend to differentiate rather than harmonize the synaptic weight distributions between adjacent neurons of the same type.

Neuroanatomy suggests that adjacent neocortical neurons share a similar set of afferent synaptic inputs, as opposed to neurons localized to different areas of the neocortex.

Absence of Repetitive Correlation Patterns Between Pairs of Adjacent Neocortical Neurons in vivo

The effort toward replicating human skills into artificial systems is growing constantly. While artificial vision has reached a certain reliability, the sense of touch is still hard to introduce into robotic devices. Human manipulation comprises a sequence of static and dynamic actions, which may include unforeseen events, such as variation of object position, movement of the fingers, and modification of the object dimensions and shape (e.g., with soft objects) due to inappropriate force levels. These circumstances are likely to produce the slippage of the object being manipulated. Artificial manipulators are not yet able to be effective in dynamic environments. This paper intends to provide a method for the identification and prevention of slippage with tactile sensors. The method is based on filtering the tactile signals to extract slippage information. The filtering has been executed by means of the stationary wavelet transform that consists of recursive filtering operations. Then, the transformed signal has been rectified and its root mean square has been computed. Finally, an on/off signal has been generated according to a threshold logic. Eight natural surfaces, featuring diverse tactile properties, have been used with the aim of validating the ability of the method to be applied regardless the surface properties. To evaluate repeatability and generalization ability, a total of 2000 experiments have been performed, 250 per each stimulus, with a mechatronic platform: five velocities combined with five indentation force levels, repeating each combination 10 times. Results are provided in terms of true positive detection and of delay between onset of slippage and algorithm output.

The effort toward replicating human skills into artificial systems is growing constantly. 

Born in the intersection
of AI and Neuroscience.

IntuiCell is a Lund University spin-off with presence in Lund and Stockholm.

For three decades our founders challenged the common consensus
of one of the biggest scientific questions of our time:

How does the brain work?

Today, we apply those groundbreaking neurophysiological insights
to redesign artificial intelligence. Creating AI that is actually intelligent.

Explore our contrarian research on the mechanisms by which the
brain predicts the world.

All Publications.

Born in the intersectionof AI and Neuroscience.

IntuiCell is a Lund University spin-off with presence in Lund and Stockholm.

For three decades our founders challenged the common consensus of one of the biggest scientific questions of our time:

How does the brain work?

Today, we apply those groundbreaking neurophysiological insights to redesign artificial intelligence. Creating AI that is actually intelligent.

Explore our contrarian research on the mechanisms by which the brain predicts the world.

All Publications.

Born in the intersection
of AI and Neuroscience.

For three decades our founders challenged the common consensus
of one of the biggest scientific questions of our time:

Today, we apply those groundbreaking neurophysiological insights
to redesign artificial intelligence. Creating AI that is actually intelligent.

Explore our contrarian research on the mechanisms by which the
brain predicts the world.