Cortex, countercurrent context, and dimensional integration of lifetime memory

The correlation between relative neocortex size and longevity in mammals encourages a search for a cortical function specifically related to the life-span. A candidate in the domain of permanent and cumulative memory storage is proposed and explored in relation to basic aspects of cortical organization. The pattern of cortico-cortical connectivity between functionally specialized areas and the laminar organization of that connectivity converges on a globally coherent representational space in which contextual embedding of information emerges as an obligatory feature of cortical function. This brings a powerful mode of inductive knowledge within reach of mammalian adaptations, a mode which combines item specificity with classificatory generality. Its neural implementation is proposed to depend on an obligatory interaction between the oppositely directed feedforward and feedback currents of cortical activity, in countercurrent fashion. Direct interaction of the two streams along their cortex-wide local interface supports a scheme of "contextual capture" for information storage responsible for the lifelong cumulative growth of a uniquely cortical form of memory termed "personal history." This approach to cortical function helps elucidate key features of cortical organization as well as cognitive aspects of mammalian life history strategies

Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenario

Oscillatory Network Activity in Brain Functions and Dysfunctions

Recent experimental studies point to the notion that the brain is a complex dynamical system whose behaviors relating to brain functions and dysfunctions can be described by the physics of network phenomena. The brain consists of anatomical axonal connections among neurons and neuronal populations in various spatial scales. Neuronal interactions and synchrony of neuronal oscillations are central to normal brain functions. Breakdowns in interactions and modifications in synchronization behaviors are usual hallmarks of brain dysfunctions. Here, in this dissertation for PhD degree in physics, we report discoveries of brain oscillatory network activity from two separate studies. These studies investigated the large-scale brain activity during tactile perceptual decision-making and epileptic seizures. In the perceptual decision-making study, using scalp electroencephalography (EEG) recordings of brain potentials, we investigated how oscillatory activity functionally organizes different neocortical regions as a network during a tactile discrimination task. While undergoing EEG recordings, blindfolded healthy participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. Based on the current dipole modeling in the brain, we found that the source-level peak activity appeared in the left primary somatosensory cortex (SI), right lateral occipital complex (LOC), right posterior intraparietal sulcus (pIPS) and finally left dorsolateral prefrontal cortex (dlPFC) at 45, 130, 160 and 175 ms respectively. Spectral interdependency analysis showed that fine tactile discrimination is mediated by distinct but overlapping ~15 Hz beta and ~80 Hz gamma band large-scale oscillatory networks. The beta-network that included all four nodes was dominantly feedforward, similar to the propagation of peak cortical activity, implying its role in accumulating and maintaining relevant sensory information and mapping to action. The gamma-network activity, occurring in a recurrent loop linked SI, pIPS and dlPFC, likely carrying out attentional selection of task-relevant sensory signals. Behavioral measure of task performance was correlated with the network activity in both bands. In the study of epileptic seizures, we investigated high-frequency (\u3e 50 Hz) oscillatory network activity from intracranial EEG (IEEG) recordings of patients who were the candidates for epilepsy surgery. The traditional approach of identifying brain regions for epilepsy surgery usually referred as seizure onset zones (SOZs) has not always produced clarity on SOZs. Here, we investigated directed network activity in the frequency domain and found that the high frequency (\u3e80 Hz) network activities occur before the onset of any visible ictal activity, andcausal relationships involve the recording electrodes where clinically identifiable seizures later develop. These findings suggest that high-frequency network activities and their causal relationships can assist in precise delineation of SOZs for surgical resection

Normal And Epilepsy-Associated Pathologic Function Of The Dentate Gyrus

The dentate gyrus plays critical roles both in cognitive processing and in regulating propagation of pathological, synchronous activity through the limbic system. The cellular and circuit mechanisms underlying these diverse functions overlap extensively. At the cellular level, the intrinsic properties of dentate granule cells combine to make these neurons fundamentally reluctant to activate, one of their hallmark traits. At the circuit level, the dentate gyrus is one of the more heavily inhibited regions of the brain, with powerful feedforward and feedback GABAergic inhibition dominating responses to afferent activation. In pathologic states such as epilepsy, disease-associated alterations within the dentate gyrus combine to compromise this circuit’s regulatory properties, culminating in a collapse of its normal function. Through the use of dynamic circuit imaging and electrophysiological brain slice recordings, pharmacology, immunohistochemistry, and a pilocarpine model of epilepsy, I characterize the emergence of dentate granule cell firing properties during brain development and then examine how the circuit’s normal activation properties become corrupted as epilepsy develops. I find that, in the perinatal brain, dentate granule cells activate in large numbers. As animals mature, these cells become less excitable and activate in extremely sparse populations in a precise, repeatable, frequency-dependent manner. This sparse activation is mediated by local circuit inhibition and not by alterations in afferent innervation of granule cells. Later, in a pilocarpine model of epilepsy, I demonstrate that normally sparse granule cell activation is massively enhanced during both epilepsy development and expression. This augmentation in excitability is mediated primarily by local disinhibition, and the mechanistic cause of this compromised inhibitory function varies over time following epileptogenic injury. My results implicate a reduction in chloride ion extrusion as a mechanism compromising inhibitory function and contributing to granule cell hyperactivation specifically during early epilepsy development. In contrast, we demonstrate that sparse dentate granule cell activation in chronically epileptic mice is rescued by glutamine application, implicating compromised GABA synthesis as a mechanism of disinhibition in chronic epilepsy. We conclude that compromised feedforward inhibition within the local circuit is the predominant mediator of the massive dentate gyrus circuit hyperactivation evident in animals during and following epilepsy development

A network that performs brute-force conversion of a temporal sequence to a spatial pattern: relevance to odor recognition

A classic problem in neuroscience is how temporal sequences (TSs) can be recognized. This problem is exemplified in the olfactory system, where an odor is defined by the TS of olfactory bulb (OB) output that occurs during a sniff. This sequence is discrete because the output is subdivided by gamma frequency oscillations. Here we propose a new class of "brute-force" solutions to recognition of discrete sequences. We demonstrate a network architecture in which there are a small number of modules, each of which provides a persistent snapshot of what occurs in a different gamma cycle. The collection of these snapshots forms a spatial pattern (SP) that can be recognized by standard attractor-based network mechanisms. We will discuss the implications of this strategy for recognizing odor-specific sequences generated by the OB

Evolution of Memory in Reactive Artificial Neural Networks

In the neuronal circuits of natural and artificial agents, memory is usually implemented with recurrent connections, since recurrence allows past agent state to affect the present, on-going behavior. Here, an interesting question arises in the context of evolution: how reactive agents could have evolved into cognitive ones with internalized memory? This study strives to find an answer to the question by simulating neuroevolution on artificial neural networks, with the hypothesis that internalization of external material interaction can be a plausible evolutionary path leading to a fully internalized memory system. A series of computational experiments were performed to gradually verify the above hypothesis. The first experiment demonstrated the possibility that external materials can be used as memory-aids for a memoryless reactive artificial agents in a simple 1-dimensional environment. Here, the reactive artificial agents used environmental markers as memory references to be successful in the ball-catching task that requires memory. Motivated by the result of the first experiment, an extended experiment was conducted to tackle a more complex memory problem using the same principle of external material interaction. This time, the reactive artificial agents are tasked to remember the locations of food items and the nest in a 2-dimensional environment. Such path-following behavior is a trivial foraging strategy of various lower animals such as ants and fish. The final experiment was designed to show the evolution of internal recurrence. In this experiment, I showed the evolutionary advantage of external material interaction by comparing the results from neural network topology evolution algorithms with and without the material interaction mechanism. The result confirmed that the agents with external material interaction learned to solve the memory task faster and more accurately. The results of the experiments provide insights on the possible evolutionary route to an internalized memory. The use of external material interaction can help reactive artificial agents to go beyond the functionality restricted by their simple network structure. Moreover, it allows much faster convergence with higher accuracy than the topological evolution of the artificial agents. These results suggest one plausible evolutionary path from reactive, through external material interaction, to recurrent structure

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference

Steep, Spatially Graded Recruitment of Feedback Inhibition by Sparse Dentate Granule Cell Activity

The dentate gyrus of the hippocampus is thought to subserve important physiological functions, such as 'pattern separation'. In chronic temporal lobe epilepsy, the dentate gyrus constitutes a strong inhibitory gate for the propagation of seizure activity into the hippocampus proper. Both examples are thought to depend critically on a steep recruitment of feedback inhibition by active dentate granule cells. Here, I used two complementary experimental approaches to quantitatively investigate the recruitment of feedback inhibition in the dentate gyrus. I showed that the activity of approximately 4% of granule cells suffices to recruit maximal feedback inhibition within the local circuit. Furthermore, the inhibition elicited by a local population of granule cells is distributed non-uniformly over the extent of the granule cell layer. Locally and remotely activated inhibition differ in several key aspects, namely their amplitude, recruitment, latency and kinetic properties. Finally, I show that net feedback inhibition facilitates during repetitive stimulation. Taken together, these data provide the first quantitative functional description of a canonical feedback inhibitory microcircuit motif. They establish that sparse granule cell activity, within the range observed in-vivo, steeply recruits spatially and temporally graded feedback inhibition