212,830 research outputs found
Building machines that adapt and compute like brains
Building machines that learn and think like humans is essential not only for
cognitive science, but also for computational neuroscience, whose ultimate goal
is to understand how cognition is implemented in biological brains. A new
cognitive computational neuroscience should build cognitive-level and neural-
level models, understand their relationships, and test both types of models
with both brain and behavioral data.Comment: Commentary on: Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. (2017)
Building machines that learn and think like people. Behavioral and Brain
Sciences, 4
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and
sufficient mechanism for inducing the stochasticity observed in cortex. Here,
we introduce Synaptic Sampling Machines, a class of neural network models that
uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised
learning. Similar to the original formulation of Boltzmann machines, these
models can be viewed as a stochastic counterpart of Hopfield networks, but
where stochasticity is induced by a random mask over the connections. Synaptic
stochasticity plays the dual role of an efficient mechanism for sampling, and a
regularizer during learning akin to DropConnect. A local synaptic plasticity
rule implementing an event-driven form of contrastive divergence enables the
learning of generative models in an on-line fashion. Synaptic sampling machines
perform equally well using discrete-timed artificial units (as in Hopfield
networks) or continuous-timed leaky integrate & fire neurons. The learned
representations are remarkably sparse and robust to reductions in bit precision
and synapse pruning: removal of more than 75% of the weakest connections
followed by cursory re-learning causes a negligible performance loss on
benchmark classification tasks. The spiking neuron-based synaptic sampling
machines outperform existing spike-based unsupervised learners, while
potentially offering substantial advantages in terms of power and complexity,
and are thus promising models for on-line learning in brain-inspired hardware
How to build a brain
Your cells are magnificent little things, every single one is full of complex microsystems all working together to keep you going. They’re more intricate and advanced than any machines we can make, but sometimes… they need a little help to get going. Stem cells are like tiny teenagers, they’re full of potential but they need a kick in the pants to get going, and that’s where I come in. After a stroke, patients are left with chunks of damaged brain tissue. Now, instead of trying to rebuild the incredibly complex human brain from scratch, I’d much give cells the support and encouragement they need to rebuild it themselves. My research goal is to rebuild damaged brain tissue, but in truth, stem cells will be doing all the actual building, I’m just making materials that tell them how to build a brain
Brain machines: New interfaces
A pesar del creciente número de descubrimientos en la neurociencia, los
enormes logros tecnológicos y algún nuevo impulso teórico, aún sigue firme entre los
cientÃficos –asà como en el sentido común– un contraste claro y marcado entre la mente y el
cuerpo. En la actualidad, esta imagen del ser humano, heredada de una cultura platónicocartesiana
consolidada, comienza a aparecer en peligro no solo por algunas nuevas
perspectivas teóricas, sino también por la propagación de dinámicas socio-culturales
originales que se producen principalmente en la esfera de las prácticas diarias, del fitness y
de la gestión del tiempo libre. Este ensayo focalizará su atención en el análisis del uso de
algunos de los dispositivos tecnológicos más utilizados en el mercado del fitness;
reflexionará acerca de la imagen del cuerpo y de las transformaciones de la identidad que
tienen lugar en nuestra sociedad, tratando de examinar las últimas posibles aportaciones
de la neurociencia a los paradigmas tradicionales de las ciencias socialesDespite the large number of discoveries in Neuroscience, great technological
achievements and some renewed theories, the idea of a clear and stark contrast between
mind and body is still widespread amongst scientists and common people. Nowadays, this
interpretation of the human being (a clear legacy of a deeply consolidated Platonic-
Cartesian culture) is affected both by the new theoretical perspectives and by the spreading
of some socio-cultural dynamics that manifest themselves daily in body and free time
management. Inspired by the analysis of one of the most common technological devices for
fitness, the aim of this work is to propose some reflections on identity and body image
transformations in our society. Moreover, the establishment of a relationship between the
latest emerging contributions in Neuroscience and the traditional paradigms of
contemporary social sciences will be pursued by the autho
Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals
The extent to which humans can interact with machines significantly enhanced through inclusion of speech, gestures, and eye movements. However, these communication channels depend on a functional motor system. As many people suffer from severe damage of the motor system resulting in paralysis and inability to communicate, the development of brain-machine interfaces (BMI) that translate electric or metabolic brain activity into control signals of external devices promises to overcome this dependence. People with complete paralysis can learn to use their brain waves to control prosthetic devices or exoskeletons. However, information transfer rates of currently available noninvasive BMI systems are still very limited and do not allow versatile control and interaction with assistive machines. Thus, using brain waves in combination with other biosignals might significantly enhance the ability of people with a compromised motor system to interact with assistive machines. Here, we give an overview of the current state of assistive, noninvasive BMI research and propose to integrate brain waves and other biosignals for improved control and applicability of assistive machines in paralysis. Beside introducing an example of such a system, potential future developments are being discussed
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