Difference between revisions of "LGNBiphasicResponses"
From aHuman Wiki
(Automated page entry using MWPush.pl) |
(Automated page entry using MWPush.pl) |
||
(3 intermediate revisions by the same user not shown) | |||
Line 1: | Line 1: | ||
− | |||
<pre style="color: green">Memory Prediction Research</pre> | <pre style="color: green">Memory Prediction Research</pre> | ||
[[Home]] -> [[SensorsResearch]] -> [[VisionResearch]] -> [[LGNBiphasicResponses]] | [[Home]] -> [[SensorsResearch]] -> [[VisionResearch]] -> [[LGNBiphasicResponses]] | ||
− | + | __TOC__ | |
---- | ---- | ||
− | + | '''Article:''' | |
* Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus, 2009 - Janneke F. M. Jehee, Dana H. Ballard | * Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus, 2009 - Janneke F. M. Jehee, Dana H. Ballard | ||
= Abstract = | = Abstract = | ||
− | * | + | * '''biphasic neural response''' - best response when quick switching between two opposite patterns - detected in LGN, V1, MT |
− | * article describes: | + | * article describes: '''hierarchical model of predictive coding''' and simulations that capture these temporal variations in neuronal response properties |
− | * focus on the | + | * focus on the '''LGN-V1 circuit''' |
* after training on natural images the model exhibits the brain’s LGN-V1 connectivity structure: | * after training on natural images the model exhibits the brain’s LGN-V1 connectivity structure: | ||
− | + | ** structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN | |
− | + | ** spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN | |
− | + | ** model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback | |
* predictive feedback is a general coding strategy in the brain | * predictive feedback is a general coding strategy in the brain | ||
Line 25: | Line 24: | ||
* LGN cells have center-surround organization | * LGN cells have center-surround organization | ||
* LGN regions switch between bright- to dark-excitatory in 20 ms | * LGN regions switch between bright- to dark-excitatory in 20 ms | ||
− | + | ** what computational reason can change preferred simulus | |
− | + | ** biphasic dynamics follow from neural mechanisms of '''predictive coding''' | |
* to be efficient - early-level visual processing removes correlations in the input, resulting in a more sparse and statistically independent output | * to be efficient - early-level visual processing removes correlations in the input, resulting in a more sparse and statistically independent output | ||
− | + | ** early visual areas remove correlations by removing the predictable components in their input | |
− | + | ** center-surround structure of LGN receptive fields can be explained using predictive coding mechanisms - center pixel intensity value can be replaced with the difference between the center value and a prediction from a linear weighted sum of its surrounding values | |
* works for interaction of lower-order and higher-order visual areas | * works for interaction of lower-order and higher-order visual areas | ||
− | + | ** low-order and high-order visual areas are reciprocally connected | |
− | + | ** higher-level receptive fields represent the predictions of the visual world | |
− | + | ** lower-level areas signal the error between predictions and actual visual input | |
− | + | ** explains end-stopping | |
− | http://ahuman. | + | http://usvn.ahuman.org/svn/ahwiki/images/wiki/research/biomodel/LGN-V1.png |
= Results = | = Results = | ||
Line 44: | Line 43: | ||
* two layers - LGN and V1 | * two layers - LGN and V1 | ||
* Steps | * Steps | ||
− | + | ** 1. V1 receives input from LGN | |
− | + | ** 2. V1 neuron with receptive field that best matched the input feeds its prediction back to LGN | |
− | + | ** 3. LGN neurons compute error between prediction and actual input | |
− | + | ** 4. LGN sends error forward to correct prediction | |
− | + | ** 5. process is repeated, single feedforward-feedback cycle takes around 20 milliseconds | |
* connection weights of the model are adapted to the input by minimizing the description length or entropy of the joint distribution of inputs and neural responses | * connection weights of the model are adapted to the input by minimizing the description length or entropy of the joint distribution of inputs and neural responses | ||
− | + | ** minimizes the model’s prediction errors | |
− | + | ** improves the sparseness of the neural code | |
− | + | ** model converges to a set of connection weights that is optimal for predicting that input | |
== LGN-V1 connectivity structure after training == | == LGN-V1 connectivity structure after training == | ||
* feedforward connection weights from on-center type and off-center type LGN cells coding for the same spatial location are summed for each of the model’s 128 V1 cells | * feedforward connection weights from on-center type and off-center type LGN cells coding for the same spatial location are summed for each of the model’s 128 V1 cells | ||
− | + | ** V1 responses in the model are linear across their on and off inputs | |
− | + | ** after training, the receptive fields show orientation tuning as found for simple cells in V1 | |
* relation between the learned receptive fields in model V1 and the properties of LGN units: | * relation between the learned receptive fields in model V1 and the properties of LGN units: | ||
− | + | ** connections are initially random and are adjusted as a consequence of the model’s learning rule together with exposure to natural images | |
− | + | ** after training, on- and off-center units are spatially aligned with the on- and off-zones of the model V1 receptive field | |
== Reversal of polarity due to predictive feedback == | == Reversal of polarity due to predictive feedback == | ||
− | * first consider a | + | * first consider a '''model with non-biphasic inputs''' |
− | + | ** spatio-temporal response of model on-center type geniculate cells is calculated using a reverse correlation algorithm | |
− | + | ** as in cat LGN, model on-center type receptive fields are arranged in center and surround, and the bright-excitatory phase is followed by a dark-excitatory phase | |
− | + | ** removing feedback in the model causes the previously biphasic responses to disappear | |
− | * then model is modified to | + | * then model is modified to '''simulate biphasic retinal inputs''' |
− | + | ** temporal response profile of model on-center type cells is obtained using reverse correlation | |
− | + | ** predictive feedback interactions cause reversals of polarity in LGN to be more pronounced than the retinal input | |
− | * why | + | * why '''biphasic responses appear in the mapped model LGN receptive fields''' |
− | + | ** reverse correlation leads to visual changes occurring much faster than most natural input the system would encode | |
− | + | ** consider stimulus consisting primarily of bright regions | |
− | + | ** on-center type LGN cells will respond to the onset of this stimulus | |
− | + | ** on zones in the LGN are linked to on zones of receptive fields in V1, which soon start to increase activation and make predictions | |
− | + | ** by the time that predictions of the first stimulus arrive in lower-level areas, areas, the initial representation of the bright stimulus has been replaced by a second white noise stimulus, and the prediction is compared against a new and unexpected stimulus representation | |
− | + | ** in reverse correlation, predictive processing shows up as a comparison against this running average whitenoise stimulation | |
− | + | ** predicted bright region is of higher luminance than the average second stimulus, causing off-center type cells to respond to the offset of the bright reference stimulus | |
* reversals in polarity of model LGN cells are most profound in a small time window after presentation of the reference stimulus but disappear gradually later on | * reversals in polarity of model LGN cells are most profound in a small time window after presentation of the reference stimulus but disappear gradually later on | ||
− | + | ** initial prediction is dynamically updated to include predictions of stimuli presented after the reference stimulus | |
− | + | ** new predictions are closer to the average white-noise stimulation | |
− | + | ** reversals in polarity will appear as long as predictions deviate from the average white-noise stimulation | |
− | + | ** precise amount of overlap between prediction and stimulus is not critical | |
* simple cell off-zones mediate inhibitory influences to off-center LGN cells and excitatory influences to on-center LGN cells | * simple cell off-zones mediate inhibitory influences to off-center LGN cells and excitatory influences to on-center LGN cells | ||
* for all model on or off-center LGN receptive fields that are aligned over a V1 receptive field region of the same polarity, firing rates decrease due to feedback | * for all model on or off-center LGN receptive fields that are aligned over a V1 receptive field region of the same polarity, firing rates decrease due to feedback | ||
Line 93: | Line 92: | ||
* model that encodes an image using predictive feedforward-feedback cycles: | * model that encodes an image using predictive feedforward-feedback cycles: | ||
− | + | ** can learn the brain’s LGNV1 connectivity structure | |
− | + | ** structure of V1 receptive fields is linked to the spatial alignment and properties of centersurround cells in the LGN | |
− | + | ** captures reversals in polarity of neuronal responses in LGN | |
− | + | ** captures phase-reversed pattern of influence from V1 simple cells on LGN cells | |
* confirms idea that visual system uses predictive feedforward-feedback interactions to efficiently encode natural input | * confirms idea that visual system uses predictive feedforward-feedback interactions to efficiently encode natural input | ||
* natural visual world is dominated by low temporal frequencies | * natural visual world is dominated by low temporal frequencies | ||
− | + | ** retinal image to be relatively stable over the periods of time considered in the model | |
− | + | ** under certain conditions visual inputs do change rapidly—more rapidly than most natural inputs the system would encode | |
* geniculate cells receive many more feedback connections (~30%) than feedforward connections (~10%) | * geniculate cells receive many more feedback connections (~30%) than feedforward connections (~10%) | ||
− | + | ** feedback signals from V1 affect the response properties of LGN cells | |
− | + | ** feedback from V1 seems to affect the strength of center-surround interactions in LGN | |
− | + | ** LGN cells respond strongly to bars that are roughly the same size as the center of their receptive field | |
− | + | ** responses are attenuated or eliminated when the bar extends beyond the receptive field center (end-stopping) | |
− | + | ** this property has been found to depend on feedback signals from V1 | |
* previous model | * previous model | ||
− | + | ** captured endstopping and some other modulations | |
− | + | ** predictive feedback model was trained on natural images, in which lines are usually longer rather than shorter | |
− | + | ** higher-level receptive fields optimized for representing longer bars | |
− | + | ** when presented with shorter bars, the model’s higher-level units could not predict their lower-level input, error responses in the lower-level neurons could not be suppressed | |
* in new model the predictive feedback framework also includes rebound effects in LGN | * in new model the predictive feedback framework also includes rebound effects in LGN | ||
− | + | ** biphasic responses are stronger in geniculate neurons than in the retinal neurons | |
− | + | ** result from predictive feedback interactions, similar to endstopping and some other inhibitory effects | |
− | + | ** reversals in polarity have also been described for several cortical areas that do not receive direct input from biphasic retinal cells | |
* other explanations of stronger biphasic responses in the LGN | * other explanations of stronger biphasic responses in the LGN | ||
− | + | ** higher LGN thresholds | |
− | + | ** inhibitory feedback from the perigeniculate nucleus | |
− | + | ** feedforward inhibition | |
* framework features: | * framework features: | ||
− | + | ** captures biphasic responses and orientation selectivity | |
− | + | ** captures phase-reversed influence of cortical feedback to LGN | |
− | + | ** explains end-stopping and some other modulations due to surround inhibition in V1 and LGN | |
− | + | ** explains reversals in polarity for many areas in cortex | |
* computationally advantageous to implement predictive operations through feedback projections | * computationally advantageous to implement predictive operations through feedback projections | ||
− | + | ** allow the system to remove redundancy and decorrelate visual responses between areas | |
− | + | ** higher-level cortical receptive fields are larger and encode more complex stimuli | |
− | + | ** allows predictions of higher complexity and larger regions in the visual field | |
* biphasic responses are attenuated in the LGN, or absent in cortex, without cortical feedback | * biphasic responses are attenuated in the LGN, or absent in cortex, without cortical feedback | ||
* model uses subtractive feedback to compare higher-level predictions with actual lower-level input | * model uses subtractive feedback to compare higher-level predictions with actual lower-level input | ||
− | + | ** could be mediated by, for example, local inhibitory neurons in the same-level area together with long-range excitatory connections from the next higher-level area | |
* model could easily be extended to include more cortical areas | * model could easily be extended to include more cortical areas | ||
− | + | ** each level would have both coding units and difference detecting units | |
− | + | ** coding units predict their lower-level input | |
− | + | ** coding units convey the current estimate to the error detectors of the same-level area | |
− | + | ** error detectors then signal the difference between their input and its prediction to the next higher level | |
− | + | ** finally one prediction becomes dominant in the entire system | |
* more accurate higher-level predictions (or equivalently greater overlap between the visual input and higher-level receptive fields) results in reduced activity of lower-level difference detectors | * more accurate higher-level predictions (or equivalently greater overlap between the visual input and higher-level receptive fields) results in reduced activity of lower-level difference detectors | ||
− | + | ** when top-down predictions in the model are off, lower-level difference detectors enhance their responses | |
− | + | ** higher-level coding neurons enhance their activity when stimuli are presented that match their receptive field properties | |
− | + | ** subsequent feedforward-feedback passes refine the initial predictions, until finally the entire system settles on the mostly likely interpretation | |
* recurrent cycles of processing are less costly in time when the system forms a hierarchy | * recurrent cycles of processing are less costly in time when the system forms a hierarchy | ||
− | + | ** most likely predictions are computed first and sent on to higher-level processing areas, which do not have to wait to begin their own computations, enabling initial rapid gist-of-the-scene processing and subsequent feedforward-feedback cycles to fill in the missing details | |
− | + | ** some global aspects of a stimulus can be detected very rapidly while detailed aspects are reported later in time | |
* top-down signals serve many computational functions | * top-down signals serve many computational functions | ||
− | + | ** sparsifying mechanism | |
− | + | ** effect of top-down signals in general is not best described as either inhibitory or excitatory | |
− | + | ** higher-level areas feed anticipatory signals back to earlier areas, enhancing neural responses to a stimulus that would otherwise fall below threshold | |
− | + | ** excitatory interaction between higher-level anticipation and the incoming lower-level signal | |
− | + | ** feedback could also act as a bayesian style prior, and adapt early level signals according to different sensory or behavioral conditions | |
− | + | ** mechanism presented here should be regarded as a relatively lowlevel mechanism that automatically creates sparser solutions | |
* rebound effects are a common feature in reverse correlation mapping and have been described in several visual areas | * rebound effects are a common feature in reverse correlation mapping and have been described in several visual areas | ||
− | + | ** biphasic responses have been found for neurons in LGN and V1 | |
− | + | ** reversals in selectivity in the motion domain have also been found for neurons in MT |
Latest revision as of 19:07, 28 November 2018
Memory Prediction Research
Home -> SensorsResearch -> VisionResearch -> LGNBiphasicResponses
Contents
Article:
- Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus, 2009 - Janneke F. M. Jehee, Dana H. Ballard
Abstract
- biphasic neural response - best response when quick switching between two opposite patterns - detected in LGN, V1, MT
- article describes: hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties
- focus on the LGN-V1 circuit
- after training on natural images the model exhibits the brain’s LGN-V1 connectivity structure:
- structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN
- spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN
- model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback
- predictive feedback is a general coding strategy in the brain
Introduction
- layout of V1-to-LGN feedback connections follows the structure of LGN-to-V1 feedforward connections
- LGN cells have center-surround organization
- LGN regions switch between bright- to dark-excitatory in 20 ms
- what computational reason can change preferred simulus
- biphasic dynamics follow from neural mechanisms of predictive coding
- to be efficient - early-level visual processing removes correlations in the input, resulting in a more sparse and statistically independent output
- early visual areas remove correlations by removing the predictable components in their input
- center-surround structure of LGN receptive fields can be explained using predictive coding mechanisms - center pixel intensity value can be replaced with the difference between the center value and a prediction from a linear weighted sum of its surrounding values
- works for interaction of lower-order and higher-order visual areas
- low-order and high-order visual areas are reciprocally connected
- higher-level receptive fields represent the predictions of the visual world
- lower-level areas signal the error between predictions and actual visual input
- explains end-stopping
Results
Hierarchical model of predictive coding
- two layers - LGN and V1
- Steps
- 1. V1 receives input from LGN
- 2. V1 neuron with receptive field that best matched the input feeds its prediction back to LGN
- 3. LGN neurons compute error between prediction and actual input
- 4. LGN sends error forward to correct prediction
- 5. process is repeated, single feedforward-feedback cycle takes around 20 milliseconds
- connection weights of the model are adapted to the input by minimizing the description length or entropy of the joint distribution of inputs and neural responses
- minimizes the model’s prediction errors
- improves the sparseness of the neural code
- model converges to a set of connection weights that is optimal for predicting that input
LGN-V1 connectivity structure after training
- feedforward connection weights from on-center type and off-center type LGN cells coding for the same spatial location are summed for each of the model’s 128 V1 cells
- V1 responses in the model are linear across their on and off inputs
- after training, the receptive fields show orientation tuning as found for simple cells in V1
- relation between the learned receptive fields in model V1 and the properties of LGN units:
- connections are initially random and are adjusted as a consequence of the model’s learning rule together with exposure to natural images
- after training, on- and off-center units are spatially aligned with the on- and off-zones of the model V1 receptive field
Reversal of polarity due to predictive feedback
- first consider a model with non-biphasic inputs
- spatio-temporal response of model on-center type geniculate cells is calculated using a reverse correlation algorithm
- as in cat LGN, model on-center type receptive fields are arranged in center and surround, and the bright-excitatory phase is followed by a dark-excitatory phase
- removing feedback in the model causes the previously biphasic responses to disappear
- then model is modified to simulate biphasic retinal inputs
- temporal response profile of model on-center type cells is obtained using reverse correlation
- predictive feedback interactions cause reversals of polarity in LGN to be more pronounced than the retinal input
- why biphasic responses appear in the mapped model LGN receptive fields
- reverse correlation leads to visual changes occurring much faster than most natural input the system would encode
- consider stimulus consisting primarily of bright regions
- on-center type LGN cells will respond to the onset of this stimulus
- on zones in the LGN are linked to on zones of receptive fields in V1, which soon start to increase activation and make predictions
- by the time that predictions of the first stimulus arrive in lower-level areas, areas, the initial representation of the bright stimulus has been replaced by a second white noise stimulus, and the prediction is compared against a new and unexpected stimulus representation
- in reverse correlation, predictive processing shows up as a comparison against this running average whitenoise stimulation
- predicted bright region is of higher luminance than the average second stimulus, causing off-center type cells to respond to the offset of the bright reference stimulus
- reversals in polarity of model LGN cells are most profound in a small time window after presentation of the reference stimulus but disappear gradually later on
- initial prediction is dynamically updated to include predictions of stimuli presented after the reference stimulus
- new predictions are closer to the average white-noise stimulation
- reversals in polarity will appear as long as predictions deviate from the average white-noise stimulation
- precise amount of overlap between prediction and stimulus is not critical
- simple cell off-zones mediate inhibitory influences to off-center LGN cells and excitatory influences to on-center LGN cells
- for all model on or off-center LGN receptive fields that are aligned over a V1 receptive field region of the same polarity, firing rates decrease due to feedback
- where the overlapping fields are of reversed polarity, there is an increase in firing rate
- neurophysiology: influence of V1 simple cells on LGN on- and off-cells is phase-reversed
Discussion
- model that encodes an image using predictive feedforward-feedback cycles:
- can learn the brain’s LGNV1 connectivity structure
- structure of V1 receptive fields is linked to the spatial alignment and properties of centersurround cells in the LGN
- captures reversals in polarity of neuronal responses in LGN
- captures phase-reversed pattern of influence from V1 simple cells on LGN cells
- confirms idea that visual system uses predictive feedforward-feedback interactions to efficiently encode natural input
- natural visual world is dominated by low temporal frequencies
- retinal image to be relatively stable over the periods of time considered in the model
- under certain conditions visual inputs do change rapidly—more rapidly than most natural inputs the system would encode
- geniculate cells receive many more feedback connections (~30%) than feedforward connections (~10%)
- feedback signals from V1 affect the response properties of LGN cells
- feedback from V1 seems to affect the strength of center-surround interactions in LGN
- LGN cells respond strongly to bars that are roughly the same size as the center of their receptive field
- responses are attenuated or eliminated when the bar extends beyond the receptive field center (end-stopping)
- this property has been found to depend on feedback signals from V1
- previous model
- captured endstopping and some other modulations
- predictive feedback model was trained on natural images, in which lines are usually longer rather than shorter
- higher-level receptive fields optimized for representing longer bars
- when presented with shorter bars, the model’s higher-level units could not predict their lower-level input, error responses in the lower-level neurons could not be suppressed
- in new model the predictive feedback framework also includes rebound effects in LGN
- biphasic responses are stronger in geniculate neurons than in the retinal neurons
- result from predictive feedback interactions, similar to endstopping and some other inhibitory effects
- reversals in polarity have also been described for several cortical areas that do not receive direct input from biphasic retinal cells
- other explanations of stronger biphasic responses in the LGN
- higher LGN thresholds
- inhibitory feedback from the perigeniculate nucleus
- feedforward inhibition
- framework features:
- captures biphasic responses and orientation selectivity
- captures phase-reversed influence of cortical feedback to LGN
- explains end-stopping and some other modulations due to surround inhibition in V1 and LGN
- explains reversals in polarity for many areas in cortex
- computationally advantageous to implement predictive operations through feedback projections
- allow the system to remove redundancy and decorrelate visual responses between areas
- higher-level cortical receptive fields are larger and encode more complex stimuli
- allows predictions of higher complexity and larger regions in the visual field
- biphasic responses are attenuated in the LGN, or absent in cortex, without cortical feedback
- model uses subtractive feedback to compare higher-level predictions with actual lower-level input
- could be mediated by, for example, local inhibitory neurons in the same-level area together with long-range excitatory connections from the next higher-level area
- model could easily be extended to include more cortical areas
- each level would have both coding units and difference detecting units
- coding units predict their lower-level input
- coding units convey the current estimate to the error detectors of the same-level area
- error detectors then signal the difference between their input and its prediction to the next higher level
- finally one prediction becomes dominant in the entire system
- more accurate higher-level predictions (or equivalently greater overlap between the visual input and higher-level receptive fields) results in reduced activity of lower-level difference detectors
- when top-down predictions in the model are off, lower-level difference detectors enhance their responses
- higher-level coding neurons enhance their activity when stimuli are presented that match their receptive field properties
- subsequent feedforward-feedback passes refine the initial predictions, until finally the entire system settles on the mostly likely interpretation
- recurrent cycles of processing are less costly in time when the system forms a hierarchy
- most likely predictions are computed first and sent on to higher-level processing areas, which do not have to wait to begin their own computations, enabling initial rapid gist-of-the-scene processing and subsequent feedforward-feedback cycles to fill in the missing details
- some global aspects of a stimulus can be detected very rapidly while detailed aspects are reported later in time
- top-down signals serve many computational functions
- sparsifying mechanism
- effect of top-down signals in general is not best described as either inhibitory or excitatory
- higher-level areas feed anticipatory signals back to earlier areas, enhancing neural responses to a stimulus that would otherwise fall below threshold
- excitatory interaction between higher-level anticipation and the incoming lower-level signal
- feedback could also act as a bayesian style prior, and adapt early level signals according to different sensory or behavioral conditions
- mechanism presented here should be regarded as a relatively lowlevel mechanism that automatically creates sparser solutions
- rebound effects are a common feature in reverse correlation mapping and have been described in several visual areas
- biphasic responses have been found for neurons in LGN and V1
- reversals in selectivity in the motion domain have also been found for neurons in MT