Catalyzing next-generation Artificial Intelligence through NeuroAI.

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells.

The neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. In the entorhinal cortex, grid cells represent the location of an animal in its environment, and this location is updated through movement and path integration. In this paper, we propose that sensory neocortex incorporates movement using grid cell-like neurons that represent the location of sensors on an object. We describe a two-layer neural network model that uses cortical grid cells and path integration to robustly learn and recognize objects through movement and predict sensory stimuli after movement. A layer of cells consisting of several grid cell-like modules represents a location in the reference frame of a specific object. Another layer of cells which processes sensory input receives this location input as context and uses it to encode the sensory input in the object's reference frame. Sensory input causes the network to invoke previously learned locations that are consistent with the input, and motor input causes the network to update those locations. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. We discuss the relationship of the model to cortical circuitry and suggest that the reciprocal connections between layers 4 and 6 fit the requirements of the model. We propose that the subgranular layers of cortical columns employ grid cell-like mechanisms to represent object specific locations that are updated through movement.

A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex.

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods-autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex.

Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.

Differential physiological and behavioral cues observed in individuals smoking botanical marijuana versus synthetic cannabinoid drugs.

CONTEXT: Synthetic cannabinoid use has increased in many states, and medicinal and/or recreational marijuana use has been legalized in some states. These changes present challenges to law enforcement drug recognition experts (DREs) who determine whether drivers are impaired by synthetic cannabinoids or marijuana, as well as to clinical toxicologists who care for patients with complications from synthetic cannabinoids and marijuana. Our goal was to compare what effects synthetic cannabinoids and marijuana had on performance and behavior, including driving impairment, by reviewing records generated by law enforcement DREs who evaluated motorists arrested for impaired driving. METHODS: Data were from a retrospective, convenience sample of de-identified arrest reports from impaired drivers suspected of using synthetic cannabinoids (n = 100) or marijuana (n = 33). Inclusion criteria were arrested drivers who admitted to using either synthetic cannabinoids or marijuana, or who possessed either synthetic cannabinoids or marijuana; who also had a DRE evaluation at the scene; and whose blood screens were negative for alcohol and other drugs. Exclusion criteria were impaired drivers arrested with other intoxicants found in their drug or alcohol blood screens. Blood samples were analyzed for 20 popular synthetic cannabinoids by using liquid chromatography-tandem mass spectrometry. Delta-9-tetrahydrocannabinol (THC) and THC-COOH were quantified by gas chromatography-mass spectrometry. Statistical significance was determined by using Fisher's exact test or Student's t-test, where appropriate, to compare the frequency of characteristics of those in the synthetic cannabinoid group versus those in the marijuana group. RESULTS: 16 synthetic cannabinoid and 25 marijuana records met selection criteria; the drivers of these records were arrested for moving violations. Median age for the synthetic cannabinoid group (n = 16, 15 males) was 20 years (IQR 19-23 years). Median age for the marijuana group (n = 25, 21 males) was 20 years (IQR 19-24 years) (p = 0.46). In the synthetic cannabinoid group, 94% (15/16) admitted to using synthetic cannabinoids. In the marijuana group, 96% (24/25) admitted to using marijuana. Blood was available for testing in 96% (24/25) of the marijuana group; 21 of these 24 had quantitative levels of THC (mean + SD = 10.7 + 5 ng/mL) and THC-COOH (mean + SD = 57.8 + 3 ng/mL). Blood was available for testing in 63% (10/16) of the synthetic cannabinoid group, with 80% (8/10) of these positive for synthetic cannabinoids. Those in the synthetic cannabinoid group were more frequently confused (7/16 [44%] vs. 0/25 [0%], p ≤ 0.003) and disoriented (5/16 [31%] vs. 0/25 [0%], p ≤ 0.003), and more frequently had incoherent, slurred speech (10/16 [63%] vs. 3/25 [12%], p = 0.0014) and horizontal gaze nystagmus (8/16 [50%] vs. 3/25 [12%], p = 0.01) than those in the marijuana group. CONCLUSION: Drivers under the influence of synthetic cannabinoids were more frequently impaired with confusion, disorientation, and incoherent, slurred speech than drivers under the influence of marijuana in this population evaluated by DREs.

Towards a mathematical theory of cortical micro-circuits.

The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains. Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation. Anatomical data provide a contrasting set of organizational constraints. The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others. We describe the pattern recognition capabilities of HTM networks and demonstrate the application of the derived circuits for modeling the subjective contour effect. We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model.

Sequence memory for prediction, inference and behaviour.

In this paper, we propose a mechanism which the neocortex may use to store sequences of patterns. Storing and recalling sequences are necessary for making predictions, recognizing time-based patterns and generating behaviour. Since these tasks are major functions of the neocortex, the ability to store and recall time-based sequences is probably a key attribute of many, if not all, cortical areas. Previously, we have proposed that the neocortex can be modelled as a hierarchy of memory regions, each of which learns and recalls sequences. This paper proposes how each region of neocortex might learn the sequences necessary for this theory. The basis of the proposal is that all the cells in a cortical column share bottom-up receptive field properties, but individual cells in a column learn to represent unique incidences of the bottom-up receptive field property within different sequences. We discuss the proposal, the biological constraints that led to it and some results modelling it.