COSYNE 2017 Computational and Systems Neuroscience Meeting
|When||Thu, Feb 23, 2017 — Tue, Feb 28, 2017|
Salt Lake City, UT USA
|Topic||Robust object learning with cross-cortical column connections|
|Poster Presentation||Jeff Hawkins, Yuwei Cui, Subutai Ahmad, and Marcus Lewis|
Neocortical pyramidal neurons receive a large fraction of their excitatory inputs from neurons in other cortical columns. These inputs innervate basal dendritic segments that are capable of initiating active dendritic spikes. The function of these lateral connections remains unclear. In this study, we propose that cross-column connections implement an important neural mechanism that contributes to robust pattern recognition. We consider the problem of object recognition with a set of cortical columns, where each cortical column contains thousands of neurons. An object consists of a set of component features. Each feature corresponds to sensory input at a particular location on the object, which are represented by sparse distributed representations (SDR) in an upstream neural population. A cortical column learns an object by forming feedforward connections from its component feature SDRs to a sparse set of neurons. After learning, sensation of a sequence of object features leads to activations of the corresponding neural population. Since features can be shared among multiple objects, information received by a single cortical column can be ambiguous. It takes many sensations before the network converges to the correct representation. We find that the performance can be dramatically improved by simultaneously considering a set of cortical columns with lateral connections, where each column learns feedforward connections independently and learns cross-column lateral connections according to Hebbian rules. The lateral inputs target onto distal dendritic segments and act as contextual signals. Although they are not strong enough to directly activate a neuron, neurons with both lateral inputs and feedforward inputs will fire faster and prevent other neurons from responding. We show that objects can be recognized faster with multiple columns because the lateral connections help to disambiguate the inputs, and that each cortical column can store more objects without being confused with cross-cortical column connections.
Subutai Ahmad, VP Research, will deliver a workshop on Feb. 28 titled “Why Do Neurons Have Thousands of Synapses? A Theory of Sequence Learning in Neocortex." This workshop will be based on the content in our peer-reviewed paper by the same name.
The annual Cosyne meeting provides an inclusive forum for the exchange of experimental and theoretical/computational approaches to problems in systems neuroscience.
To encourage interdisciplinary interactions, the main meeting is arranged in a single track. A set of invited talks are selected by the Executive Committee and Organizing Committee, and additional talks and posters are selected by the Program Committee, based on submitted abstracts.
Cosyne topics include (but are not limited to): neural coding, natural scene statistics, dendritic computation, neural basis of persistent activity, nonlinear receptive field mapping, representations of time and sequence, reward systems, decision-making, synaptic plasticity, map formation and plasticity, population coding, attention, and computation with spiking networks. Participants include pure experimentalists, pure theorists, and everything in between.