Edelman's Darwin VIII Discovers Neural Synchrony

Chris Chatham at Develintel Blog has added another post in his series on neural synchrony and oscillatory activity. The latest one deals with experiments in artificial intelligence from Gerald Edelman's lab, the complex neural net machine named Darwin VIII. Interestingly enough, Darwin VIII demonstrated synchronous activity during training, so long as the model's connections were left intact. The following is a bit technical:

The details of the implementation are as follows: the physical anatomy of Darwin VIII includes a CCD camera for vision, microphones for audition, infrared detectors for navigation, effectors for movement, and a 12 unit Beowulf cluster for the number crunching. Darwin's synthetic neuroanatomy consists of more than 53,000 units and over 1.5 million synaptic connections, with layers corresponding to V1, V2, V4, inferotemporal cortex (IT), superior colliculus, and ventral tegmental area (including a dopamine-like neuromodulatory system, based on an algorithm similar to temporal differences). For simplicity, the primary visual layer responds preferentially to green, red, horizontal, vertical and diagonal lines; all subsequent visual layers are bidirectionally connected and have increasingly large receptive fields (until IT, in which representation is non-topographic). The robot's orientation is guided by a topographic activity map in the superior colliculus layer, which also receives direct excitatory input from tones with specific amplitude and frequency picked up by the stereo microphones (this represents an a priori drive or bias for "target tones"). The dopamine system modulates synaptic efficacy between itself and IT, as well as between IT and superior colliculus, with effects that last several processing cycles. All areas contain both recurrent excitatory connections and lateral inhibition.

Neural activity was modeled via a mean firing rate model, with one small addition: a phase parameter "provides temporal specificity without incurring the computational costs associated with modeling of the spiking activity of individual neurons in real-time." All synaptic connections were modeled as phase-dependent, such that new phases are chosen at random unless the a unit's presynaptic input phases surpass a threshold, after which phase changes are first sent through a nonlinear "squashing" function and then scaled by a phase learning rate. This implementation causes postsynaptic phase to be influenced in the direction of the most active presynaptic units' phases. Synaptic efficacy is modified both with traditional firing-rate dependent credit/blame assignment, as well as with phase-dependent credit/blame assignment, in which units with tightly coupled phases are subject to potentiation, and those with uncoupled phases are subject to depression.

The experiment was divided into training and testing phases; during training, Darwin autonomously explored an environment consisting of one target item and three distractor items which share multiple attributes with the target item. For example, if a red diamond was the target, red squares and green diamonds would be distractor items. At the beginning of each training phase (which was repeated for three different Darwin "subjects"), all weights were randomized. Throughout the training phases, sounds were emitted from speakers which caused Darwin to orient towards the target. In the testing phase, these speakers are turned off and Darwin is allowed to explore its environment for another 15,000 cycles.

The authors measured Darwin's ability to locate the targets in its environment: each simulated subject was able to do so over 80% of the time. As the authors point out, " It should be noted that successful performance on this task is not trivial. Targets and distracters appeared in the visual field at many different scales and at many different positions as Darwin VIII explored its environment. Moreover, because of shared properties, targets cannot be reliably distinguished from distracters on the basis of color or shape alone."
At each timestep in the experiment, the researchers took a "snapshot" of the activity in every unit, and the weight of every connection. Results showed self-synchronization among neurons with recurrent connections within only 15 cycles; when reentrant connections were lesioned, no synchrony occurred within 10,000 cycles. Most importantly, multiple simultaneous synchronous firing patterns were observed within the active units of both IT, superior colliculus, and the value system (dopamine) layer, with both more synchrony and higher firing rates in circuits corresponding to targets or target features; in their own words, "the simultaneous viewing of two objects clearly evoked two distinct sets of circuits that were distributed throughout the simulated nervous system and distinguished by differences in the relative timing of their activity." In other words, multiple polyphase patterns of synchronous firing can self-organize inside a network with the proper architecture and environment.

You can find more information from Edelman's group here.

Thanks to Chris for alerting us to this remarkable finding. Neural net models apparently show emergent behaviour similar to that of actual neuron groupings in brains. Will the scientists find more similarity the more faithfully their models copy biological brains? Quite possibly. Then what? Then you might find more neuroscientists using neural net models to test their theories of brain function. When the information flows in both directions, the potential for important breakthroughs increases.