Earlier this month, I attended the annual
Computational and Systems Neuroscience meeting (Cosyne)
in Salt Lake City. Cosyne is a peer reviewed scientific
conference that brings experimental and theoretical neuroscientists together to
exchange data and ideas. Why does a machine intelligence company attend a
neuroscience meeting? Numenta’s approach to machine intelligence starts with a
deep understanding of how the neocortex learns. We use the brain as a blueprint.
The HTM theory is not only inspired by neuroscience concepts, but also
constrained by detailed neuroscience findings. Neuroscientists, using many new
tools, have made tremendous advancements in understanding the physiology and
connectivity of the brain. We would like to see whether the latest experimental
evidences could fit into the HTM theory. If not, how should we revise the theory
to be consistent with the experimental observations?
From neuroscience findings to machine intelligence
This year we presented a poster on “a theory of sequence memory in the
neocortex.” The ability to recognize and memorize regular temporal patterns from
sensory input streams is critical for almost all cortical functions. The topic
of neural representations of time and sequence in the cortex was very popular at
Cosyne this year, as you can see in the
Our work is unique, as it is not only built on concrete experimental findings
from neuroscience and makes a number of experimentally testable predictions, but
also achieves compelling performance on real-world sequence learning tasks.
Neurons in the neocortex receive thousands of inputs on their highly elaborated
dendritic trees. Unlike most artificial neural network models individual
dendritic branches act as active pattern detectors: co-activation of a number of
synapses leads to a dendritic spike that can depolarize the cell body for
hundreds of milliseconds. This phenomenon of active dendrites has been known for
a long time among neuroscientists. A number of presentations at Cosyne modeled
the biophysical mechanism underlying dendritic spikes. Nevertheless, the
function of dendritic spikes remains unclear and it is not incorporated in most
neural network models.
Left: A pyramidal neuron in the cortex (Spruston 2008, Nat Rev
Neurosci). Right: Researchers stimulated individual synapses optically
and measured voltage responses at the soma. Simultaneous stimulation of enough
synapses (8 in this case) caused a large and sustained depolarization at the
cell body (Major et al., 2013, Annu Rev Neurosci).
The HTM sequence memory model utilizes the active dendrites of cortical neurons
to learn sequences from data streams. Temporal sequences are learned via growth
of new synapses and are represented with
sparse distributed representations.
Predictions of future inputs are made through the generation of dendritic
spikes. The resulting model gives rise to a powerful sequence memory, which not
only achieves comparable performance to state-of-the-art machine learning
algorithms, but also exhibits many desirable attributes for real-world sequence
learning with streaming data.
HTM sequence memory model makes accurate 2.5 hour ahead-predictions of taxi
demand in the New York City.
Interactions with other neuroscientists
Our work attracted wide interest among
neuroscientists. Several experimental neuroscientists were very excited to learn
about the important functional role of the long observed phenomena of dendritic
spikes. Some even expressed interest in running more specific experiments to
test the learning mechanisms used in HTM. We also benefited by discussing with
other neuroscientists. For example, by talking with researchers that build
detailed biophysical models of active dendrites, we now have a better idea of
how HTM would work on a detailed biophysical level.
I found quite a few other presentations at Cosyne that were related to HTM.
Prof. Michael Berry’s group from Princeton University recorded a large
population of neurons in the primary visual cortex during the presentation of
image sequences. The observed behavior of the real neural population matches
many aspects of the HTM sequence memory model. Prof. Jose Carmena from UC
Berkeley presented a novel paradigm for brain-machine interface where subjects
continuously learn to control a small set of neurons. Interestingly, the
performance over time looks quite similar to that of the HTM model on a
continuous learning task. These studies, and many others, give us valuable
insights on the development of future HTM algorithms. We would like to keep
collaborating with the neuroscience community. We believe doing so would
tremendously speed up our progress on machine intelligence.
I encourage you to take a look at our poster and the
accompanying paper. Please also check out
this recently published Frontiers Neural Circuit paper
to learn about the HTM theory. Let me know what you think of it by contacting
firstname.lastname@example.org and join the discussion of HTM in
the NuPIC community.
Numenta Cosyne Poster. Click to enlarge.