Hippocampus as unitary coherent particle filter

We present a mapping of the hippocampal formation onto a Temporal Restricted Boltzmann Machine [1] based architecture, running a deterministic version of Gibbs sampling, and extended with a lostness detection and recovery circuit modelled on subiculum and septal acetylcholine (ACh). The mapping approximates Bayesian filtering, which infers both auto-associative de-noised percepts and temporal sequences, the latter including sequences of places during navigation. Inference may be viewed as a neurally implemented particle filter with a single particle-as suggested previously [2] as a purely behavioural animal model

Machines Learning - Towards a New Synthetic Autobiographical Memory

Autobiographical memory is the organisation of episodes and contextual information from an individual’s experiences into a coherent narrative, which is key to our sense of self. Formation and recall of autobiographical memories is essential for effective, adaptive behaviour in the world, providing contextual information necessary for planning actions and memory functions such as event reconstruction. A synthetic autobiographical memory system would endow intelligent robotic agents with many essential components of cognition through active compression and storage of historical sensorimotor data in an easily addressable manner. Current approaches neither fulfil these functional requirements, nor build upon recent understanding of predictive coding, deep learning, nor the neurobiology of memory. This position paper highlights desiderata for a modern implementation of synthetic autobiographical memory based on human episodic memory, and proposes that a recently developed model of hippocampal memory could be extended as a generalised model of autobiographical memory. Initial implementation will be targeted at social interaction, where current synthetic autobiographical memory systems have had success

Extending a Hippocampal Model for Navigation Around a Maze Generated from Real-World Data

An essential component in the formation of understanding is the ability to use past experience to comprehend the here and now, and to aid selection of future action. Past experience is stored as memories which are then available for recall at very short notice, allowing for understanding of short and long term action. Autobiographical memory (ABM) is a form of temporally organised memory and is the organisation of episodes and contextual information from an individual’s experience into a coherent narrative, which is key to a sense of self. Formation and recall of memories is essential for effective and adaptive behaviour in the world, providing contextual information necessary for planning actions and memory functions, such as event reconstruction. Here we tested and developed a previously defined computational memory model, based on hippocampal structure and function, as a first step towards developing a synthetic model of human ABM (SAM). The hippocampal model chosen has functions analogous to that of human ABM. We trained the model on real-world sensory data and demonstrate successful, biologically plausible memory formation and recall, in a navigational task. The hippocampal model will later be extended for application in a biologically inspired system for human-robot interaction

Scaling up a Boltzmann machine model of hippocampus with visual features for mobile robots

Previous papers [4], [5] have described a detailed mapping between biological hippocampal navigation and a temporal restricted Boltzmann machine [20] with unitary coherent particle filtering. These models have focused on the biological structures and used simplified microworlds in implemented examples. As a first step in scaling the model up towards practical bio-inspired robotic navigation, we present new results with the model applied to real world visual data, though still limited by a discretized configuration space. To extract useful features from visual input we apply the SURF transform followed by a new lamellae-based winner-take-all Dentate Gyrus. This new visual processing stream allows the navigation system to function without the need for a simplifying data assumption of the previous models, and brings the hippocampal model closer to being a practical robotic navigation system

Scaling a hippocampus model with GPU parallelisation and test-driven refactoring

The hippocampus is the brain area used for localisation, mapping and episodic memory. Humans and animals can outperform robotic systems in these tasks, so functional models of hippocampus may be useful to improve robotic navigation, such as for self-driving cars. Previous work developed a biologically plausible model of hippocampus based on Unitary Coherent Particle Filter (UCPF) and Temporal Restricted Boltzmann Machine, which was able to learn to navigate around small test environments. However it was implemented in serial software, which becomes very slow as the environments and numbers of neurons scale up. Modern GPUs can parallelize execution of neural networks. The present Neural Software Engineering study develops a GPU accelerated version of the UCPF hippocampus software, using the formal Software Engineering techniques of profiling, optimisation and test-driven refactoring. Results show that the model can greatly benefit from parallel execution, which may enable it to scale from toy environments and applications to real-world ones such as self-driving car navigation. The refactored parallel code is released to the community as open source software as part of this publication

The robot vibrissal system: Understanding mammalian sensorimotor co-ordination through biomimetics

Chapter 10 The Robot Vibrissal System: Understanding Mammalian Sensorimotor Co-ordination Through Biomimetics Tony J. Prescott, Ben Mitchinson, Nathan F. Lepora, Stuart P. Wilson, Sean R. Anderson, John Porrill, Paul Dean, Charles ..

Why are probabilistic laws governing quantum mechanics and neurobiology?

We address the question: Why are dynamical laws governing in quantum mechanics and in neuroscience of probabilistic nature instead of being deterministic? We discuss some ideas showing that the probabilistic option offers advantages over the deterministic one.Comment: 40 pages, 8 fig

Simultaneous localisation and mapping on a multi-degree of freedom biomimetic whiskered robot

A biomimetic mobile robot called “Shrewbot” has been built as part of a neuroethological study of the mammalian facial whisker sensory system. This platform has been used to further evaluate the problem space of whisker based tactile Simultaneous Localisation And Mapping (tSLAM). Shrewbot uses a biomorphic 3-dimensional array of active whiskers and a model of action selection based on tactile sensory attention to explore a circular walled arena sparsely populated with simple geometric shapes. Datasets taken during this exploration have been used to parameterise an approach to localisation and mapping based on probabilistic occupancy grids. We present the results of this work and conclude that simultaneous localisation and mapping is possible given only noisy odometry and tactile information from a 3-dimensional array of active biomimetic whiskers and no prior information of features in the environment

Presynaptic GABAB Receptors Functionally Uncouple Somatostatin Interneurons from the Active Hippocampal Network

Information processing in cortical neuronal networks relies on properly balanced excitatory and inhibitory neurotransmission. A ubiquitous motif for maintaining this balance is the somatostatin interneuron (SOM-IN) feedback microcircuit. Here, we investigated the modulation of this microcircuit by presynaptic GABAB receptors (GABABRs) in the rodent hippocampus. Whole-cell recordings from SOM-INs revealed that both excitatory and inhibitory synaptic inputs are strongly inhibited by GABABRs, while optogenetic activation of the interneurons shows that their inhibitory output is also strongly suppressed. Electron microscopic analysis of immunogold-labelled freeze-fracture replicas confirms that GABABRs are highly expressed presynaptically at both input and output synapses of SOM-INs. Activation of GABABRs selectively suppresses the recruitment of SOM-INs during gamma oscillations induced in vitro. Thus, axonal GABABRs are positioned to efficiently control the input and output synapses of SOM-INs and can functionally uncouple them from local network with implications for rhythmogenesis and the balance of entorhinal versus intrahippocampal afferents