Add to ORKGThis study investigates a population decoding paradigm, in which the estimation of stimulus in the previous step is used as prior knowledge for consecutive decoding. We analyze the decoding accu-racy of such a Bayesian decoder (Maximum a Posteriori Estimate), and show that it can be implemented by a biologically plausible recurrent network, where the prior knowledge of stimulus is con-veyed by the change in recurrent interactions as a result of Hebbian learning.
Two theoretical ideas have emerged recently with the ambition to provide a unifying functional expla...
International audienceIn the context of sensory or higher-level cognitive processing, we present a r...
There is a wealth of approaches to understanding the ways that populations of neurons encode static,...
Embodied agents, be they animals or robots, acquire information about the world through their senses...
Behavioral experiments on humans and animals suggest that the brain performs probabilistic inference...
Bayesian inference has emerged as a general framework that captures how organisms make decisions und...
SummaryWhen making a decision, one must first accumulate evidence, often over time, and then select ...
zemelOu.arizona.edu We study the problem of statistically correct inference in networks whose basic ...
When making a decision, one must first accumulate evidence, often over time, and then select the app...
In the vertebrate nervous system, sensory stimuli are typically encoded through the concerted activi...
This study uses a neural field model to investigate computational aspects of population coding and d...
A fundamental task for both biological perception systems and human-engineered agents is to infer un...
We propose a theoretical framework for efficient representation of time-varying sensory information ...
The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs ...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Two theoretical ideas have emerged recently with the ambition to provide a unifying functional expla...
International audienceIn the context of sensory or higher-level cognitive processing, we present a r...
There is a wealth of approaches to understanding the ways that populations of neurons encode static,...
Embodied agents, be they animals or robots, acquire information about the world through their senses...
Behavioral experiments on humans and animals suggest that the brain performs probabilistic inference...
Bayesian inference has emerged as a general framework that captures how organisms make decisions und...
SummaryWhen making a decision, one must first accumulate evidence, often over time, and then select ...
zemelOu.arizona.edu We study the problem of statistically correct inference in networks whose basic ...
When making a decision, one must first accumulate evidence, often over time, and then select the app...
In the vertebrate nervous system, sensory stimuli are typically encoded through the concerted activi...
This study uses a neural field model to investigate computational aspects of population coding and d...
A fundamental task for both biological perception systems and human-engineered agents is to infer un...
We propose a theoretical framework for efficient representation of time-varying sensory information ...
The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs ...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Two theoretical ideas have emerged recently with the ambition to provide a unifying functional expla...
International audienceIn the context of sensory or higher-level cognitive processing, we present a r...
There is a wealth of approaches to understanding the ways that populations of neurons encode static,...