18 citations · 43 across the 9 of their papers we have counts for
13 papers
Learning temporal relationships between symbols with Laplace Neural Manifolds
Marc W. Howard, Zahra G. Esfahani, Bao Le +1
Firing across populations of neurons in many regions of the mammalian brain maintains a temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that p…
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…
A deep convolutional neural network that is invariant to time rescaling
Brandon G. Jacques, Zoran Tiganj, Aakash Sarkar +2
Human learners can readily understand speech, or a melody, when it is presented slower or faster than usual. Although deep convolutional neural networks (CNNs) are extremely powerf…
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…
Estimating scale-invariant future in continuous time
Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg +1
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous tim…
Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world
Tyler A. Spears, Brandon G. Jacques, Marc W. Howard +1
In both the human brain and any general artificial intelligence (AI), a representation of the past is necessary to predict the future. However, perfect storage of all experiences i…
Neural scaling laws for an uncertain world
Marc W. Howard, Karthik H. Shankar
Autonomous neural systems must efficiently process information in a wide range of novel environments, which may have very different statistical properties. We consider the problem…
Neural mechanism to simulate a scale-invariant future timeline
Karthik H. Shankar, Inder Singh, Marc W. Howard
Predicting future events, and their order, is important for efficient planning. We propose a neural mechanism to non-destructively translate the current state of memory into the fu…
Optimally fuzzy temporal memory
Karthik H. Shankar, Marc W. Howard
Any learner with the ability to predict the future of a structured time-varying signal must maintain a memory of the recent past. If the signal has a characteristic timescale relev…