SIRENA: A CAD environment for behavioural modelling and simulation of VLSI cellular neural network chips

This paper presents SIRENA, a CAD environment for the simulation and modelling of mixed-signal VLSI parallel processing chips based on cellular neural networks. SIRENA includes capabilities for: (a) the description of nominal and non-ideal operation of CNN analogue circuitry at the behavioural level; (b) performing realistic simulations of the transient evolution of physical CNNs including deviations due to second-order effects of the hardware; and, (c) evaluating sensitivity figures, and realize noise and Monte Carlo simulations in the time domain. These capabilities portray SIRENA as better suited for CNN chip development than algorithmic simulation packages (such as OpenSimulator, Sesame) or conventional neural networks simulators (RCS, GENESIS, SFINX), which are not oriented to the evaluation of hardware non-idealities. As compared to conventional electrical simulators (such as HSPICE or ELDO-FAS), SIRENA provides easier modelling of the hardware parasitics, a significant reduction in computation time, and similar accuracy levels. Consequently, iteration during the design procedure becomes possible, supporting decision making regarding design strategies and dimensioning. SIRENA has been developed using object-oriented programming techniques in C, and currently runs under the UNIX operating system and X-Windows framework. It employs a dedicated high-level hardware description language: DECEL, fitted to the description of non-idealities arising in CNN hardware. This language has been developed aiming generality, in the sense of making no restrictions on the network models that can be implemented. SIRENA is highly modular and composed of independent tools. This simplifies future expansions and improvements.Comisión Interministerial de Ciencia y Tecnología TIC96-1392-C02-0

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting

Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration

Distributed Hypothesis Testing, Attention Shifts and Transmitter Dynatmics During the Self-Organization of Brain Recognition Codes

BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088

Altered brainstem responses to modafinil in schizophrenia: implications for adjunctive treatment of cognition.

Candidate pro-cognitive drugs for schizophrenia targeting several neurochemical systems have consistently failed to demonstrate robust efficacy. It remains untested whether concurrent antipsychotic medications exert pharmacodynamic interactions that mitigate pro-cognitive action in patients. We used functional MRI (fMRI) in a randomized, double-blind, placebo-controlled within-subject crossover test of single-dose modafinil effects in 27 medicated schizophrenia patients, interrogating brainstem regions where catecholamine systems arise to innervate the cortex, to link cellular and systems-level models of cognitive control. Modafinil effects were evaluated both within this patient group and compared to a healthy subject group. Modafinil modulated activity in the locus coeruleus (LC) and ventral tegmental area (VTA) in the patient group. However, compared to the healthy comparison group, these effects were altered as a function of task demands: the control-independent drug effect on deactivation was relatively attenuated (shallower) in the LC and exaggerated (deeper) in the VTA; in contrast, again compared to the comparison group, the control-related drug effects on positive activation were attenuated in LC, VTA and the cortical cognitive control network. These altered effects in the LC and VTA were significantly and specifically associated with the degree of antagonism of alpha-2 adrenergic and dopamine-2 receptors, respectively, by concurrently prescribed antipsychotics. These sources of evidence suggest interacting effects on catecholamine neurons of chronic antipsychotic treatment, which respectively increase and decrease sustained neuronal activity in LC and VTA. This is the first direct evidence in a clinical population to suggest that antipsychotic medications alter catecholamine neuronal activity to mitigate pro-cognitive drug action on cortical circuits

1st INCF Workshop on Genetic Animal Models for Brain Diseases

The INCF Secretariat organized a workshop to focus on the “role of neuroinformatics in the processes of building, evaluating, and using genetic animal models for brain diseases” in Stockholm, December 13–14, 2009. Eight scientists specialized in the fields of neuroinformatics, database, ontologies, and brain disease participated together with two representatives of the National Institutes of Health and the European Union, as well as three observers of the national INCF nodes of Norway, Poland, and the United Kingdom

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented