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Information flow through a model of the C. elegans klinotaxis circuit
Understanding how information about external stimuli is transformed into
behavior is one of the central goals of neuroscience. Here we characterize the
information flow through a complete sensorimotor circuit: from stimulus, to
sensory neurons, to interneurons, to motor neurons, to muscles, to motion.
Specifically, we apply a recently developed framework for quantifying
information flow to a previously published ensemble of models of salt
klinotaxis in the nematode worm C. elegans. The models are grounded in the
neuroanatomy and currently known neurophysiology of the worm. The unknown model
parameters were optimized to reproduce the worm's behavior. Information flow
analysis reveals several key principles underlying how the models operate: (1)
Interneuron class AIY is responsible for integrating information about positive
and negative changes in concentration, and exhibits a strong left/right
information asymmetry. (2) Gap junctions play a crucial role in the transfer of
information responsible for the information symmetry observed in interneuron
class AIZ. (3) Neck motor neuron class SMB implements an information gating
mechanism that underlies the circuit's state-dependent response. (4) The neck
carries non-uniform distribution about changes in concentration. Thus, not all
directions of movement are equally informative. Each of these findings
corresponds to an experimental prediction that could be tested in the worm to
greatly refine our understanding of the neural circuit underlying klinotaxis.
Information flow analysis also allows us to explore how information flow
relates to underlying electrophysiology. Despite large variations in the neural
parameters of individual circuits, the overall information flow architecture
circuit is remarkably consistent across the ensemble, suggesting that
information flow analysis captures general principles of operation for the
klinotaxis circuit
Neural coding strategies and mechanisms of competition
A long running debate has concerned the question of whether neural
representations are encoded using a distributed or a local coding scheme. In
both schemes individual neurons respond to certain specific patterns of
pre-synaptic activity. Hence, rather than being dichotomous, both coding
schemes are based on the same representational mechanism. We argue that a
population of neurons needs to be capable of learning both local and distributed
representations, as appropriate to the task, and should be capable of generating
both local and distributed codes in response to different stimuli. Many neural
network algorithms, which are often employed as models of cognitive processes,
fail to meet all these requirements. In contrast, we present a neural network
architecture which enables a single algorithm to efficiently learn, and respond
using, both types of coding scheme
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