New York Tech Journal
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Automatically scalable #Python & #Neuroscience as it relates to #MachineLearning

Posted on June 28th, 2016

#NYAI: New York Artificial Intelligence

06/28/2016 @Rise, 43 West 23rd Street, NY, 2nd floor

IMG_20160628_192420[1] IMG_20160628_200539[1] IMG_20160628_201905[1]

Braxton McKee (@braxtonmckee ) @Ufora first spoke about the challenges of creating a version of Python (#Pyfora) that naturally scales to take advantage of the hardware to handle parallelism as the problem grows.

Braxton presented an example in which we compute the minimum distance from target points a larger universe of points base on their Cartesian coordinates. This is easily written for small problems, but the computation needs to be optimized when computing this value across many cpu’s.

However, the allocation across cpu’s depends on the number of targets relative to the size of the point universe. Instead of trying to solve this analytically, they use a #Dynamicrebalancing strategy that splits the task and adds resources to the subtasks creating bottlenecks.

This approach solves many resource allocation problems, but still faces challenges

  1. nested parallelism. They look for parallelism within the code and look for bottlenecks at the top level of parallelism and split the task into subtasks at that level, …
  2. the data do not fit in memory. They break tasks into smaller tasks. They also have each task know which other caches hold data, so they can be accessed directly without going to slower main memory
  3. different types of architectures (such as gpu’s) require different types of optimization
  4. the optimizer cannot look inside python packages, so cannot optimize a bottleneck within a package.


  1. is a just-in-time compiler that moves stack frames from machine-to-machine and senses how to take advantage of parallelism
  2. tracks what data a thread is using
  3. dynamically schedules threads and data
  4. takes advantage of mutability which allows the compiler to assume that functions do no change over time so the compiler can look inside the function when optimizing execution
  5. is written on top of another language which allows for the possibility of porting the method to other languages

In the second presentation, Jeremy Freeman spoke about the relationship between neuroscience research and machine learning models. He first talking about the early works on understanding the function of the visual cortex.

Findings by Hubel & Wiesel in1959 have set the foundation for visual processing models for the past 40 years. They found that Individual neurons in the V1 area of the visual cortex responded to the orientation of lines in the visual field. These inputs fed neurons that detect more complex features, such as edges, moving lines, etc.

Others also considered systems which have higher level recognition and how to train a system. These include

Perceptrons by Rosenblatt, 1957

Neocognitrons by Fukushima, 1980

Hierarchical learning machines, Lecun, 1985

Back propagation by Rumelhart, 1986

His doctoral research looked at the activity of neurons in V2 area. They found they could generate high order patterns that some neurons discriminate among.

But in 2012, there was a jump in performance of neural nets – U. of Toronto

By 2014, some of the neural network algos perform better than humans and primates, especially in the area of image processing. This has lead to many advances such as Google deepdream which combines images and texture to create an artistic hybrid image.

Recent scientific research allows one to look at thousands of neurons simultaneously. He also talked about some of his current research which uses “tactile virtual reality” to examine the neural activity as a mouse explores a maze (the mouse walks on a ball that senses it’s steps as it learns the maze).

Jeremy also spoke about Model-free episodic control for complex sequential tasks requiring memory and learning. ML research has created models such as LSTM and Neural Turing Nets which retain state representations. Graham Taylor has looked at neural feedback modulation using gates.

He also notes that there are similar functionalities between the V1 area in the visual cortex, the A1 auditory area, and the S1, tactile area.

To find out more, he suggested visiting his github site: Freeman-lab and looking the web site

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