Linking Visual Cortical Development to Visual Perception

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

Cognitive computing: algorithm design in the intersection of cognitive science and emerging computer architectures

For the first time in decades computers are evolving into a fundamentally new class of machine. Transistors are still getting smaller, more economical, and more power-efficient, but operating frequencies leveled off in the mid-2000's. Today, improving performance requires placing a larger number of slower processing cores on each of many chips. Software written for such machines must scale out over many cores rather than scaling up with a faster single core. Biological computation is an extreme manifestation of such a many-slow-core architecture and therefore offers a potential source of ideas for leveraging new hardware. This dissertation addresses several problems in the intersection of emerging computer architectures and biological computation, termed Cognitive Computing: What mechanisms are necessary to maintain stable representations in a large distributed learning system? How should complex biologically-inspired algorithms be tested? How do visual sensing limitations like occlusion influence performance of classification algorithms? Neurons have a limited dynamic output range, but must process real-world signals over a wide dynamic range without saturating or succumbing to endogenous noise. Many existing neural network models leverage spatial competition to address this issue, but require hand-tuning of several parameters for a specific, fixed distribution of inputs. Integrating spatial competition with a stabilizing learning process produces a neural network model capable of autonomously adapting to a non-stationary distribution of inputs. Human-engineered complex systems typically include a number of architectural features to curtail complexity and simplify testing. Biological systems do not obey these constraints. Biologically-inspired algorithms are thus dramatically more difficult to engineer. Augmenting standard tools from the software engineering community with features targeted towards biologically-inspired systems is an effective mitigation. Natural visual environments contain objects that are occluded by other objects. Such occlusions are under-represented in the standard benchmark datasets for testing classification algorithms. This bias masks the negative effect of occlusion on performance. Correcting the bias with a new dataset demonstrates that occlusion is a dominant variable in classification performance. Modifying a state-of-the-art algorithm with mechanisms for occlusion resistance doubles classification performance in high-occlusion cases without penalty for unoccluded objects

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells

On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex

In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices. All files used for the simulations are made available through the journal web site1

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been or may be effective for the analysis of 3D CT images of plant roots. We describe and comment on different approaches to aspects of the analysis of plant roots based on images, namely, (1) root segmentation, i.e., the isolation of root from non-root matter; (2) root-system reconstruction; and (3) extraction of higher-level phenotypes. As many of these approaches are novel and have yet to be applied to this context, we limit ourselves to brief descriptions of the methodologies. With the rapid development and growing use of X-ray CT scanning technologies to generate large volumes of data relevant to root structure, it is timely to review existing and potential quantitative and computational approaches to the analysis of such data. Summaries of several computational tools are included in the Appendix

OPTIMIZATION OF TIME-RESPONSE AND AMPLIFICATION FEATURES OF EGOTs FOR NEUROPHYSIOLOGICAL APPLICATIONS

In device engineering, basic neuron-to-neuron communication has recently inspired the development of increasingly structured and efficient brain-mimicking setups in which the information flow can be processed with strategies resembling physiological ones. This is possible thanks to the use of organic neuromorphic devices, which can share the same electrolytic medium and adjust reciprocal connection weights according to temporal features of the input signals. In a parallel - although conceptually deeply interconnected - fashion, device engineers are directing their efforts towards novel tools to interface the brain and to decipher its signalling strategies. This led to several technological advances which allow scientists to transduce brain activity and, piece by piece, to create a detailed map of its functions. This effort extends over a wide spectrum of length-scales, zooming out from neuron-to-neuron communication up to global activity of neural populations. Both these scientific endeavours, namely mimicking neural communication and transducing brain activity, can benefit from the technology of Electrolyte-Gated Organic Transistors (EGOTs). Electrolyte-Gated Organic Transistors (EGOTs) are low-power electronic devices that functionally integrate the electrolytic environment through the exploitation of organic mixed ionic-electronic conductors. This enables the conversion of ionic signals into electronic ones, making such architectures ideal building blocks for neuroelectronics. This has driven extensive scientific and technological investigation on EGOTs. Such devices have been successfully demonstrated both as transducers and amplifiers of electrophysiological activity and as neuromorphic units. These promising results arise from the fact that EGOTs are active devices, which widely extend their applicability window over the capabilities of passive electronics (i.e. electrodes) but pose major integration hurdles. Being transistors, EGOTs need two driving voltages to be operated. If, on the one hand, the presence of two voltages becomes an advantage for the modulation of the device response (e.g. for devising EGOT-based neuromorphic circuitry), on the other hand it can become detrimental in brain interfaces, since it may result in a non-null bias directly applied on the brain. If such voltage exceeds the electrochemical stability window of water, undesired faradic reactions may lead to critical tissue and/or device damage. This work addresses EGOTs applications in neuroelectronics from the above-described dual perspective, spanning from neuromorphic device engineering to in vivo brain-device interfaces implementation. The advantages of using three-terminal architectures for neuromorphic devices, achieving reversible fine-tuning of their response plasticity, are highlighted. Jointly, the possibility of obtaining a multilevel memory unit by acting on the gate potential is discussed. Additionally, a novel mode of operation for EGOTs is introduced, enabling full retention of amplification capability while, at the same time, avoiding the application of a bias in the brain. Starting on these premises, a novel set of ultra-conformable active micro-epicortical arrays is presented, which fully integrate in situ fabricated EGOT recording sites onto medical-grade polyimide substrates. Finally, a whole organic circuitry for signal processing is presented, exploiting ad-hoc designed organic passive components coupled with EGOT devices. This unprecedented approach provides the possibility to sort complex signals into their constitutive frequency components in real time, thereby delineating innovative strategies to devise organic-based functional building-blocks for brain-machine interfaces.Nell’ingegneria elettronica, la comunicazione di base tra neuroni ha recentemente ispirato lo sviluppo di configurazioni sempre più articolate ed efficienti che imitano il cervello, in cui il flusso di informazioni può essere elaborato con strategie simili a quelle fisiologiche. Ciò è reso possibile grazie all'uso di dispositivi neuromorfici organici, che possono condividere lo stesso mezzo elettrolitico e regolare i pesi delle connessioni reciproche in base alle caratteristiche temporali dei segnali in ingresso. In modo parallelo, gli ingegneri elettronici stanno dirigendo i loro sforzi verso nuovi strumenti per interfacciare il cervello e decifrare le sue strategie di comunicazione. Si è giunti così a diversi progressi tecnologici che consentono agli scienziati di trasdurre l'attività cerebrale e, pezzo per pezzo, di creare una mappa dettagliata delle sue funzioni. Entrambi questi ambiti scientifici, ovvero imitare la comunicazione neurale e trasdurre l'attività cerebrale, possono trarre vantaggio dalla tecnologia dei transistor organici a base elettrolitica (EGOT). I transistor organici a base elettrolitica (EGOT) sono dispositivi elettronici a bassa potenza che integrano funzionalmente l'ambiente elettrolitico attraverso lo sfruttamento di conduttori organici misti ionici-elettronici, i quali consentono di convertire i segnali ionici in segnali elettronici, rendendo tali dispositivi ideali per la neuroelettronica. Gli EGOT sono stati dimostrati con successo sia come trasduttori e amplificatori dell'attività elettrofisiologica e sia come unità neuromorfiche. Tali risultati derivano dal fatto che gli EGOT sono dispositivi attivi, al contrario dell'elettronica passiva (ad esempio gli elettrodi), ma pongono comunque qualche ostacolo alla loro integrazione in ambiente biologico. In quanto transistor, gli EGOT necessitano l'applicazione di due tensioni tra i suoi terminali. Se, da un lato, la presenza di due tensioni diventa un vantaggio per la modulazione della risposta del dispositivo (ad esempio, per l'ideazione di circuiti neuromorfici basati su EGOT), dall'altro può diventare dannosa quando gli EGOT vengono adoperati come sito di registrazione nelle interfacce cerebrali, poiché una tensione non nulla può essere applicata direttamente al cervello. Se tale tensione supera la finestra di stabilità elettrochimica dell'acqua, reazioni faradiche indesiderate possono manifestarsi, le quali potrebbero danneggiare i tessuti e/o il dispositivo. Questo lavoro affronta le applicazioni degli EGOT nella neuroelettronica dalla duplice prospettiva sopra descritta: ingegnerizzazione neuromorfica ed implementazione come interfacce neurali in applicazioni in vivo. Vengono evidenziati i vantaggi dell'utilizzo di architetture a tre terminali per i dispositivi neuromorfici, ottenendo una regolazione reversibile della loro plasticità di risposta. Si discute inoltre la possibilità di ottenere un'unità di memoria multilivello agendo sul potenziale di gate. Viene introdotta una nuova modalità di funzionamento per gli EGOT, che consente di mantenere la capacità di amplificazione e, allo stesso tempo, di evitare l'applicazione di una tensione all’interfaccia cervello-dispositivo. Partendo da queste premesse, viene presentata una nuova serie di array micro-epicorticali ultra-conformabili, che integrano completamente i siti di registrazione EGOT fabbricati in situ su substrati di poliimmide. Infine, viene proposto un circuito organico per l'elaborazione del segnale, sfruttando componenti passivi organici progettati ad hoc e accoppiati a dispositivi EGOT. Questo approccio senza precedenti offre la possibilità di filtrare e scomporre segnali complessi nelle loro componenti di frequenza costitutive in tempo reale, delineando così strategie innovative per concepire blocchi funzionali a base organica per le interfacce cervello-macchina

Attractors, memory and perception

In this Thesis, the first three introductory chapters are devoted to the review of literature on contextual perception, its neural basis and network modeling of memory. In chapter 4, the first two sections give the definition of our model; and the next two sections, 4.3 and 4.4, report the original work of mine on retrieval properties of different network structures and network dynamics underlying the response to ambiguous patterns, respectively. The reported work in chapter 5 has been done in collaboration with Prof Bharathi Jagadeesh in University of Washington, and is already published in the journal \u201dCerebral Cortex\u201d. In this collaboration, Yan Liu, from the group in Seattle, carried out the recording experiments and I did the data analysis and network simulations. Chapter 6, which represents a network model for \u201dpriming\u201d and \u201dadaptation aftereffect\u201d is done by me. The works reported in 4.3, 4.5, and the whole chapter 6 are in preparation for publication