Biology subjects

Hawkins, J.

Publications and source records attributed to Hawkins, J..

6 recordsLinked to original sources

bioRxiv2018

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.

neuroscience↗

bioRxiv2018

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 objects 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.

neuroscience↗

bioRxiv2018

The Effect of Previous Life Cycle Phase on the Growth Kinetics, Morphology and Antibiotic Resistance of Salmonella Typhimurium DT104 in Brain Heart Infusion and Ground Chicken Extract

Growth models are predominately used in the food industry to estimate the potential growth of select microorganisms under environmental conditions. The growth kinetics, cellular morphology and antibiotic resistance were studied throughout the life cycle of Salmonella Typhimurium. The effect of the previous life cycle phase (late log phase [LLP], early stationary phase [ESP], late stationary phase [LSP] and early death phase [EDP]) of Salmonella after reinoculation in brain heart infusion broth (BHI), ground chicken extract (GCE) and BHI at pH 5, 7 and 9 and salt concentrations 2, 3 and 4% was investigated. The growth media and previous life cycle phase had significant effects on the lag time ({lambda}), specific growth rate (max) and maximum population density (Ymax). At 2% and 4% salt concentration the LLP had the significantly (P<0.05) fastest max (1.07 and 0.69 log CFU/mL/h, respectively). As the cells transitioned from the late log phase (LLP) to the early death phase (EDP), the {lambda} significantly (P<0.05) increased. At pH 5 and 9 the EDP had a significantly (P<0.05) lower Ymax than the LLP, ESP and LSP. As the cells transitioned from a rod shape to a coccoid shape in the EDP, the cells were more susceptible to antibiotics. The cells regained their resistance as they transitioned back to a rod shape from the EDP to the log and stationary phase. Our results revealed that growth kinetics, cells length, shape and antibiotic resistance were significantly affected by the previous life cycle phase.

microbiology↗

bioRxiv2017

Untangling Sequences: Behavior vs. External Causes

There are two fundamental reasons why sensory inputs to the brain change over time. Sensory inputs can change due to external factors or they can change due to our own behavior. Interpreting behavior-generated changes requires knowledge of how the body is moving, whereas interpreting externally-generated changes relies solely on the temporal sequence of input patterns. The sensory signals entering the neocortex change due to a mixture of both behavior and external factors. The neocortex must disentangle them but the mechanisms are unknown. In this paper, we show that a single neural mechanism can learn and recognize both types of sequences. In the model, cells are driven by feedforward sensory input and are modulated by contextual input. If the contextual input includes information derived from efference motor copies, the cells learn sensorimotor sequences. If the contextual input consists of nearby cellular activity, the cells learn temporal sequences. Through simulation we show that a network containing both types of contextual input automatically separates and learns both types of input patterns. We review experimental data that suggests the upper layers of cortical regions contain the anatomical structure required to support this mechanism.

neuroscience↗

bioRxiv2017

Why Does the Neocortex Have Layers and Columns, A Theory of Learning the 3D 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.

neuroscience↗

bioRxiv2016

The HTM Spatial Pooler: a neocortical algorithm for online sparse distributed coding

1.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 spatial pooler, 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 spatial pooler outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the spatial pooler 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.

neuroscience↗