Biology subjects

Zachary Kilpatrick

Publications and source records attributed to Zachary Kilpatrick.

3 recordsLinked to original sources

bioRxiv2016

Evidence accumulation and change rate inference in dynamic environments

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is updating the posterior probability of all possible changepoint counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation based plasticity rule. We thus show how optimal observers accumulates evidence in changing environments, and map this computation to reduced models which perform inference using plausible neural mechanisms.

Neuroscience↗

bioRxiv2016

Extending Levelt’s Propositions to perceptual multistability involving interocular grouping

Levelts Propositions have been a touchstone for experimental and modeling studies of perceptual multistability. We asked whether Levelts Propositions extend to perceptual multistability involving interocular grouping. To address this question we used split-grating stimuli withcomplementary halves of the same color. As in previous studies, subjects reported four percepts in alternation: the two stimuli presented to each eye (single-eye percepts), as well as two interocularly grouped, single color percepts (grouped percepts). Most subjects responded to increased color saturation by more frequently reporting a single color image, thus increasingthe predominance of grouped percepts (Levelts Proposition I). In these subjects increased predominance was due to a decrease in the average dominance duration of single-eye percepts, while that of grouped percepts remained largely unaffected. This is in accordance with generalized Levelts Proposition II which posits that the average dominance duration of the stronger (in this case single-eye) percept is primarily affectedbychanges in stimulus strength. In accordance with Proposition III, thealternation rate increased as the difference in the strength of the percepts decreased. To explain the mechanism behind these observations, we introduce a hierarchical model consisting of low-level neural populations, eachresponding to input at a visual hemifield, and higher-level populations representing the percepts. The model exhibits the changes in dominance durationobserved in the data, and conforms to all of Levelts Propositions.

Neuroscience↗

bioRxiv2014

Networks that learn the precise timing of event sequences

Neuronal circuits can learn and replay firing patterns evoked by sequences of sensory stimuli. After training, a brief cue can trigger a spatiotemporal pattern of neural activity similar to that evoked by a learned stimulus sequence. Network models show that such sequence learning can occur through the shaping of feedforward excitatory connectivity via long term plasticity. Previous models describe how event order can be learned, but they typically do not explain how precise timing can be recalled. We propose a mechanism for learning both the order and precise timing of event sequences. In our recurrent network model, long term plasticity leads to the learning of the sequence, while short term facilitation enables temporally precise replay of events. Learned synaptic weights between populations determine the time necessary for one population to activate another. Long term plasticity adjusts these weights so that the trained event times are matched during playback. While we chose short term facilitation as a time-tracking process, we also demonstrate that other mechanisms, such as spike rate adaptation, can fulfill this role. We also analyze the impact of trial-to-trial variability, showing how observational errors as well as neuronal noise result in variability in learned event times. The dynamics of the playback process determine how stochasticity is inherited in learned sequence timings. Future experiments that characterize such variability can therefore shed light on the neural mechanisms of sequence learning.

Neuroscience↗