#summary Memory Prediction Research [Home] -> [SensorsResearch] -> [VisionResearch] -> [LGNBiphasicResponses] ---- *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 http://usvn.ahuman.org/svn/ahwiki/images/wiki/research/biomodel/LGN-V1.png = 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