Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences
Abstract
The majority of distinct sensory and motor events occur as temporally ordered sequences with rich probabilistic structure. Sequences can be characterized by the probability of transitioning from the current state to upcoming states (forward probability), as well as the probability of having transitioned to the current state from previous states (backward probability). Despite the prevalence of probabilistic sequencing of both sensory and motor events, the Hebbian mechanisms that mold synapses to reflect the statistics of experienced probabilistic sequences are not well understood. Here, we show through analytic calculations and numerical simulations that Hebbian plasticity (correlation, covariance, and STDP) with pre-synaptic competition can develop synaptic weights equal to the conditional forward transition probabilities present in the input sequence. In contrast, post-synaptic competition can develop synaptic weights proportional to the conditional backward probabilities of the same input sequence. We demonstrate that to stably reflect the conditional probability of a neuron’s inputs and outputs, local Hebbian plasticity requires balance between competitive learning forces that promote synaptic differentiation and homogenizing learning forces that promote synaptic stabilization. The balance between these forces dictates a prior over the distribution of learned synaptic weights, strongly influencing both the rate at which structure emerges and the entropy ofmore »
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences and Computational Research Divisions
- Stanford Univ., CA (United States). Dept. of Applied Physics
- Univ. of California, San Francisco, CA (United States). Dept. of Physiology. Center for Integrative Neuroscience; Howard Hughes Medical Inst., Chevy Chase, MD (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1628198
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Computational Neuroscience
- Additional Journal Information:
- Journal Volume: 9; Journal ID: ISSN 1662-5188
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; Mathematical & Computational Biology; Neurosciences & Neurology; Hebbian plasticity; pre/post-synaptic; probability; sequences; birdsong
Citation Formats
Bouchard, Kristofer E., Ganguli, Surya, and Brainard, Michael S. Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences. United States: N. p., 2015.
Web. doi:10.3389/fncom.2015.00092.
Bouchard, Kristofer E., Ganguli, Surya, & Brainard, Michael S. Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences. United States. https://doi.org/10.3389/fncom.2015.00092
Bouchard, Kristofer E., Ganguli, Surya, and Brainard, Michael S. Tue .
"Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences". United States. https://doi.org/10.3389/fncom.2015.00092. https://www.osti.gov/servlets/purl/1628198.
@article{osti_1628198,
title = {Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences},
author = {Bouchard, Kristofer E. and Ganguli, Surya and Brainard, Michael S.},
abstractNote = {The majority of distinct sensory and motor events occur as temporally ordered sequences with rich probabilistic structure. Sequences can be characterized by the probability of transitioning from the current state to upcoming states (forward probability), as well as the probability of having transitioned to the current state from previous states (backward probability). Despite the prevalence of probabilistic sequencing of both sensory and motor events, the Hebbian mechanisms that mold synapses to reflect the statistics of experienced probabilistic sequences are not well understood. Here, we show through analytic calculations and numerical simulations that Hebbian plasticity (correlation, covariance, and STDP) with pre-synaptic competition can develop synaptic weights equal to the conditional forward transition probabilities present in the input sequence. In contrast, post-synaptic competition can develop synaptic weights proportional to the conditional backward probabilities of the same input sequence. We demonstrate that to stably reflect the conditional probability of a neuron’s inputs and outputs, local Hebbian plasticity requires balance between competitive learning forces that promote synaptic differentiation and homogenizing learning forces that promote synaptic stabilization. The balance between these forces dictates a prior over the distribution of learned synaptic weights, strongly influencing both the rate at which structure emerges and the entropy of the final distribution of synaptic weights. Together, these results demonstrate a simple correspondence between the biophysical organization of neurons, the site of synaptic competition, and the temporal flow of information encoded in synaptic weights by Hebbian plasticity while highlighting the utility of balancing learning forces to accurately encode probability distributions, and prior expectations over such probability distributions.},
doi = {10.3389/fncom.2015.00092},
journal = {Frontiers in Computational Neuroscience},
number = ,
volume = 9,
place = {United States},
year = {Tue Jul 21 00:00:00 EDT 2015},
month = {Tue Jul 21 00:00:00 EDT 2015}
}
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FIGURE 1: Production and learning of sequences and their probabilistic characterizations. (A) (top) Example spectrogram (power at frequency vs. time) of the song from one Bengalese finch. Songs are composed of categorical vocalizations (syllables), organized into probabilistic sequences. (bottom) Forward probability transition diagram for the song displayed above. Here, eachmore »
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Works referencing / citing this record:
Timing during transitions in Bengalese finch song: implications for motor sequencing
journal, September 2017
- Troyer, Todd W.; Brainard, Michael S.; Bouchard, Kristofer E.
- Journal of Neurophysiology, Vol. 118, Issue 3
Advantages of comparative studies in songbirds to understand the neural basis of sensorimotor integration
journal, August 2017
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Auditory-induced neural dynamics in sensory-motor circuitry predict learned temporal and sequential statistics of birdsong
journal, August 2016
- Bouchard, Kristofer E.; Brainard, Michael S.
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