A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Review: Object vision in a structured world

In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Top-down effects on early visual processing in humans: a predictive coding framework

An increasing number of human electroencephalography (EEG) studies examining the earliest component of the visual evoked potential, the so-called C1, have cast doubts on the previously prevalent notion that this component is impermeable to top-down effects. This article reviews the original studies that (i) described the C1, (ii) linked it to primary visual cortex (V1) activity, and (iii) suggested that its electrophysiological characteristics are exclusively determined by low-level stimulus attributes, particularly the spatial position of the stimulus within the visual field. We then describe conflicting evidence from animal studies and human neuroimaging experiments and provide an overview of recent EEG and magnetoencephalography (MEG) work showing that initial V1 activity in humans may be strongly modulated by higher-level cognitive factors. Finally, we formulate a theoretical framework for understanding top-down effects on early visual processing in terms of predictive coding

TEST: A Tropic, Embodied, and Situated Theory of Cognition

TEST is a novel taxonomy of knowledge representations based on three distinct hierarchically organized representational features: Tropism, Embodiment, and Situatedness. Tropic representational features reflect constraints of the physical world on the agent’s ability to form, reactivate, and enrich embodied (i.e., resulting from the agent’s bodily constraints) conceptual representations embedded in situated contexts. The proposed hierarchy entails that representations can, in principle, have tropic features without necessarily having situated and/or embodied features. On the other hand, representations that are situated and/or embodied are likely to be simultaneously tropic. Hence while we propose tropism as the most general term, the hierarchical relationship between embodiment and situatedness is more on a par, such that the dominance of one component over the other relies on the distinction between offline storage vs. online generation as well as on representation-specific properties