1 citations · 1 across the 1 of their papers we have counts for
7 papers
Formal models of memory based on temporally-varying representations
Marc W. Howard
The idea that memory behavior relies on a gradually-changing internal state has a long history in mathematical psychology. This chapter traces this line of thought from statistical…
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales
Brandon Jacques, Zoran Tiganj, Marc W. Howard +1
Extracting temporal relationships over a range of scales is a hallmark of human perception and cognition -- and thus it is a critical feature of machine learning applied to real-wo…
Predicting the future with a scale-invariant temporal memory for the past
Wei Zhong Goh, Varun Ursekar, Marc W. Howard
In recent years it has become clear that the brain maintains a temporal memory of recent events stretching far into the past. This paper presents a neurally-inspired algorithm to u…
Cognitive computation using neural representations of time and space in the Laplace domain
Marc W. Howard, Michael E. Hasselmo
Memory for the past makes use of a record of what happened when---a function over past time. Time cells in the hippocampus and temporal context cells in the entorhinal cortex both…
Scale-dependent Relationships in Natural Language
Aakash Sarkar, Marc Howard
Natural language exhibits statistical dependencies at a wide range of scales. For instance, the mutual information between words in natural language decays like a power law with th…
Evidence accumulation in a Laplace domain decision space
Marc W. Howard, Andre Luzardo, Zoran Tiganj
Evidence accumulation models of simple decision-making have long assumed that the brain estimates a scalar decision variable corresponding to the log-likelihood ratio of the two al…