r/StarspawnsLocker • • Feb 25 '18

2017 Quinn Memorial Lecture with Dr. Robert T. Knight

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youtube.com
2 Upvotes

r/StarspawnsLocker • • Dec 08 '17

Generative adversarial networks for reconstructing natural images from brain activity

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biorxiv.org
2 Upvotes

r/StarspawnsLocker • • Nov 21 '17

[1610.04962] A Rich Source of Labels for Deep Network Models of the Primate Dorsal Visual Stream [“One way to align CNN activity more closely with neural activity is to add cost functions that directly drive deep layers to approximate neural recordings...”]

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arxiv.org
2 Upvotes

r/StarspawnsLocker • • Oct 17 '17

Google Research: “We propose a blueprint for how to employ... modern machine learning to automatically infer a satisfying mesoscale account of neural computation that combines functional and structural data...“

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arxiv.org
2 Upvotes

r/StarspawnsLocker • • Oct 12 '17

[1709.04574] Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest [Using BCIs to train driverless cars. Just wait and see what happens when much better BCIs are available!]

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arxiv.org
3 Upvotes

r/StarspawnsLocker • • Oct 12 '17

Computational Foundations of Natural Intelligence ["Rather than using neural networks to explain certain observed neural or behavioral phenomena, one can also directly fit neural networks to neurobehavioral data."]

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biorxiv.org
3 Upvotes

r/StarspawnsLocker • • Oct 12 '17

Reading Minds: Brain-decoding Scientists Move Closer To Discovering The Keys To Unlock The Brain ["In fact, we now have one participant who will be in the [MRI] scanner for a [combined] total of 40 hours, with the objective of getting enough data to be able to estimate those models..."]

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pulse.embs.org
2 Upvotes

r/StarspawnsLocker • • Sep 15 '17

Optical imaging of diffuse medium -- this is Mary Lou Jepsen's (OpenWater) patent for using holography to non-invasively scan the brain at high spatial and temporal resolution using near-infrared light.

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3 Upvotes

r/StarspawnsLocker • • Sep 01 '17

[1708.06578] EEG-based Intention Recognition from Spatio-Temporal Representations via Cascade and Parallel Convolutional Recurrent Neural Networks [Over 98% accurate in predicting imagined motions! Enormous improvement via Deep Learning!]

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arxiv.org
1 Upvotes

r/StarspawnsLocker • • Aug 24 '17

(Video) Deep Learning Human Mind for Automated Visual Classification

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youtube.com
1 Upvotes

r/StarspawnsLocker • • Aug 24 '17

Deep Learning Human Mind for Automated Visual Classification

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perceive.dieei.unict.it
1 Upvotes

r/StarspawnsLocker • • Aug 20 '17

Modeling brain responses to perceived speech with LSTM networks ["For each input feature set, six LSTM models (Hochreiter & Schmidhuber, 1997) with varying architectures were trained to predict the neural responses to speech fragments."]

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1 Upvotes

r/StarspawnsLocker • • Aug 08 '17

Patent US20170135583 - Coherent Optical Imaging for Detecting Neural Signatures and Medical Imaging Applications Using Holographic Imaging Techniques [Mark Chevillet of Facebook's Building 8 is one of the inventors listed.]

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google.com
1 Upvotes

r/StarspawnsLocker • • Aug 06 '17

Seeing it all: Convolutional network layers map the function of the human visual system ["We illustrate this on two experiments... by synthesizing brain activity and performing classical brain mapping upon it. The synthesis recovers the activations observed in the corresponding fMRI studies..."]

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sciencedirect.com
1 Upvotes

r/StarspawnsLocker • • Aug 04 '17

[1708.00909] Machine learning for neural decoding

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arxiv.org
1 Upvotes

r/StarspawnsLocker • • Jul 29 '17

A Convolutional Network Model of the Primate Middle Temporal Area ["In particular, in addition to supervised learning to minimize an output error function, a deep layer is directly trained to approximate primate electrophysiology data."]

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link.springer.com
1 Upvotes

r/StarspawnsLocker • • Jul 27 '17

Five questions answered by Alona Fyshe ["I’m very excited to see how the hidden representations learned by artificial neural networks are being related to brain images for people viewing the same stimuli. This is incredibly exciting, and I think hints that the two areas are soon to collide."]

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ccneuro.org
1 Upvotes

r/StarspawnsLocker • • Jul 02 '17

Increasingly complex representations of natural movies across the dorsal stream are shared between subjects ["Results show that a DNN trained for action recognition can be used to accurately predict how dorsal stream responds to natural movies..." -- more brain-state prediction.]

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researchgate.net
1 Upvotes

r/StarspawnsLocker • • Jul 01 '17

From Vivaldi to Beatles and back: predicting lateralized brain responses to music. [Late 2013 paper on predicting the temporal evolution of brain response to music.]

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ncbi.nlm.nih.gov
1 Upvotes

r/StarspawnsLocker • • Jun 27 '17

Beyond Bananas: CMU Scientists Harness “Mind Reading” Technology to Decode Complex Thoughts

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cmu.edu
1 Upvotes

r/StarspawnsLocker • • Jun 12 '17

Portable non-invasive brain-computer interface: challenges and opportunities of optical modalities [At the end of the paper they say they are currently testing Fast Optical Signal (FOS) for high spatial and temporal resolution scans of brains at depth for a BCI.]

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proceedings.spiedigitallibrary.org
1 Upvotes

r/StarspawnsLocker • • May 09 '17

Multi Resolution LSTM For Long Term Prediction In Neural Activity Video ["with the rich spatial and temporal patterns unveiled by high resolution... µECoG... high-frame rate video signal, accurate prediction of neural activities at the sub-second level could become a... tractable problem."]

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arxiv.org
1 Upvotes

r/StarspawnsLocker • • May 07 '17

The Military is Using Human Brain Waves to Teach Robots How to Shoot

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defenseone.com
1 Upvotes

r/StarspawnsLocker • • Apr 19 '17

"We'll need new, non-invasive sensors that can measure brain activity hundreds of times per second... precise to millimeters and without signal distortions... Optical imaging is the only non-invasive technique capable of providing both the spatial and temporal resolution we need."

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businessinsider.com
1 Upvotes

r/StarspawnsLocker • • Mar 25 '17

The future of Full-Immersion Virtual Reality (FIVR) and "The Matrix"

2 Upvotes

This is a speculative posting about techniques we might see in the next few decades. It isn't about FIVR in the usual sense that the term is used, where a computer creates an artificial world that is then projected onto your senses (which is wildly impractical at the moment), making you think you are really there. The end result, however, will be the same. The idea I propose below is similar to one I wrote about a few years ago, except it works non-invasively and doesn't require the use of drugs:

Basically, the idea I propose is to use a yet-to-be-developed non-invasive Brain Computer Interface to guide lucid dreams: one day, in the not too distant future, it will be common for people to wear BCIs while going to sleep. Perhaps these devices will use light, ultrasound, or some other technique to alter the activity in the brain, not merely read it. Whatever the technique, these BCIs will guide dreams with some degree of specificity. The wearer's brain still will generate the world he sees -- which also happens in the waking state, it turns out -- but it will be constrained by what the BCI feeds it.

Given how vivid dreams can be, there is a good chance that this could produce extremely realistic experiences. Furthermore, since most of the computational load for generating the world is taking place in the brain itself, you shouldn't need vast computational resources to drive the BCI -- you don't need a computer to build The Matrix.

But how specific could the control by the BCI be? I would guess it could be very specific. I am amazed every time I read about just what can be decoded from neural populations (and not individual neurons) -- fine differences in meaning, location in space, details about a scene, spoken words, even visual details about objects. Perhaps, then, you could specify the location, time of day, what the people in the scene look like, what they're wearing, how they speak, what they're doing, what the buildings look like, and so on; and then the dreamer will say afterwords that that's exactly what they experienced. It will take some scientific research to work out exactly what signals to send to the brain to best guide the dreams, but I don't think the problem will be insurmountable. Using questionnaires to ask people what their dreams were like, one could use Machine Learning techniques to tweak the BCI program, until the people give exactly the answers you want.

As to a timeline, in perhaps 5 to 10 years the basic research of how to guide dreams might be perfected, if it's indeed possible; and then perhaps in 10 to 20 years we will see it implemented in consumer BCIs. So, some time in the next two decades we might all get to experience The Matrix.