TOPOLOGICAL PROPERTIES OF A NETWORK OF SPIKING NEURONS IN FACE IMAGE RECOGNITION

We introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The network performed satisfactorily given appropriate topology, i.e. the number of neurons and synaptic connections, which corresponded to the size of input images. Comparison of Synaptic Plasticity Activity Rule (SAPR) and Spike Timing Dependant Plasticity (STDP) rules, used to update connections between the neurons, indicated that the SAPR gave better results and thus was used throughout. Test results showed that the network performed better than Support Vector Machines. We also introduced a stopping criterion based on entropy, which significantly shortened the iterative process while only slightly affecting classification performance

Deep Neural Networks - A Brief History

Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure

Abstracts of the 2014 Brains, Minds, and Machines Summer School

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

Dynamic and Integrative Properties of the Primary Visual Cortex

The ability to derive meaning from complex, ambiguous sensory input requires the integration of information over both space and time, as well as cognitive mechanisms to dynamically shape that integration. We have studied these processes in the primary visual cortex (V1), where neurons have been proposed to integrate visual inputs along a geometric pattern known as the association field (AF). We first used cortical reorganization as a model to investigate the role that a specific network of V1 connections, the long-range horizontal connections, might play in temporal and spatial integration across the AF. When retinal lesions ablate sensory information from portions of the visual field, V1 undergoes a process of reorganization mediated by compensatory changes in the network of horizontal collaterals. The reorganization accompanies the brain’s amazing ability to perceptually “fill-inâ€, or “seeâ€, the lost visual input. We developed a computational model to simulate cortical reorganization and perceptual fill-in mediated by a plexus of horizontal connections that encode the AF. The model reproduces the major features of the perceptual fill-in reported by human subjects with retinal lesions, and it suggests that V1 neurons, empowered by their horizontal connections, underlie both perceptual fill-in and normal integrative mechanisms that are crucial to our visual perception. These results motivated the second prong of our work, which was to experimentally study the normal integration of information in V1. Since psychophysical and physiological studies suggest that spatial interactions in V1 may be under cognitive control, we investigated the integrative properties of V1 neurons under different cognitive states. We performed extracellular recordings from single V1 neurons in macaques that were trained to perform a delayed-match-to-sample contour detection task. We found that the ability of V1 neurons to summate visual inputs from beyond the classical receptive field (cRF) imbues them with selectivity for complex contour shapes, and that neuronal shape selectivity in V1 changed dynamically according to the shapes monkeys were cued to detect. Over the population, V1 encoded subsets of the AF, predicted by the computational model, that shifted as a function of the monkeys’ expectations. These results support the major conclusions of the theoretical work; even more, they reveal a sophisticated mode of form processing, whereby the selectivity of the whole network in V1 is reshaped by cognitive state

NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles

This paper presents an open-source aerial neuromorphic dataset that captures pedestrians and vehicles moving in an urban environment. The dataset, titled NU-AIR, features 70.75 minutes of event footage acquired with a 640 x 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment. Crowds of pedestrians, different types of vehicles, and street scenes featuring busy urban environments are captured at different elevations and illumination conditions. Manual bounding box annotations of vehicles and pedestrians contained in the recordings are provided at a frequency of 30 Hz, yielding 93,204 labels in total. Evaluation of the dataset's fidelity is performed through comprehensive ablation study for three Spiking Neural Networks (SNNs) and training ten Deep Neural Networks (DNNs) to validate the quality and reliability of both the dataset and corresponding annotations. All data and Python code to voxelize the data and subsequently train SNNs/DNNs has been open-sourced.Comment: 20 pages, 5 figure

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

PROCESSING INFORMATION ON INTERMEDIATE TIMESCALES WITHIN RECURRENT NEURAL NETWORKS

The cerebral cortex has remarkable computational abilities; it is able to solve prob- lems which remain beyond the most advanced man-made systems. The complexity arises due to the structure of the neural network which controls how the neurons interact. One surprising fact about this network is the dominance of ‘recurrent’ and ‘feedback’ connections. For example, only 5-10% of connections into the earliest stage of visual processing are ‘feedforward’, in that they carry information from the eyes (via the Lateral Geniculate Nucleus). One possible reason for these connec- tions is that they allow for information to be preserved within the network; the underlying ‘causes’ of sensory stimuli usually persist for much longer than the time scales of neural processing, and so understanding them requires continued aggrega- tion of information within the sensory cortices. In this dissertation, I investigate several models of such sensory processing via recurrent connections. I introduce the transient attractor network, which depends on recurrent plastic connectivity, and demonstrate in simulations how it might be involved in the processes of short term memory, signal de-noising, and temporal coherence analysis. I then show how a certain recurrent network structure might allow for transient associative learning to occur on the timescales of seconds using presynaptic facilitation. Finally, I consider how auditory scene analysis might occur through ‘gamma partitioning’. This process uses recurrent excitatory and inhibitory connections to preserve information within the neural network about its recent state, allowing for the separation of auditory sources into different perceptual cycles