Behavioral imprinting is a distinct form of learning that has a lifelong impact on social interactions and affectional behaviors1-4. Unlike other forms of memory, imprinting does not require conspicuous association of stimuli; exposure per se appears sufficient to induce memories that neither undergo extinction nor are altered by experience later in life. The site of storage of imprinted memory and the mechanisms that control its formation and permanence are unknown. Here we uncover a molecular mechanism that controls olfactory imprinting, which underlies behaviors including kin and nest recognition, maternal attachment, and homing5-10. We show that odor exposure during the perinatal period converts an innately aversive odor into a homing signal. The behavioral change is associated with odor-induced changes in the projection patterns of olfactory sensory neuron (OSN) expressing the cognate receptors for the exposed odor. We show that the Wnt signaling receptor Frizzled1 (Fzd1) acts as a master regulator of the critical period of OSN development and is responsible for closing the critical period to prevent further changes in the neural circuit. In Fzd1 knockout mice axon projection patterns are continually modified by sensory experience. As Fzd1 knockout abolishes the developmental critical period, it also abolishes odor imprinting. Specific knockout of Fzd1 in the OSNs have the same effect. Mechanistically, Fzd1 controls the critical period through an autoregulated shutdown and by controlling an activity-driven regulon in the OSNs. The transient expression and the subsequent downregulation of Fzd1 leads to the irreversible closure of the critical period to lock in circuits established during the critical period. The evidence suggests that imprinted odor memory is stored in the patterns of connectivity at the first synapse in the olfactory bulb. Early odor experience induces changes in the OSN projection to alter connectivity with innate circuits to establish a life-long memory.
Our recognition of an object is consistent across conditions, unaffected by motion, perspective, rotation, and corruption. This robustness is thought to be enabled by invariant object representations, but how the brain achieves it remains unknown. In artificial neural networks, learning to represent objects is simulated as an optimization process. The system reduces discrepancies between actual and desired outputs by updating specific connections through mechanisms such as error backpropagation. These operations are biologically implausible primarily because they require individual connections at all levels to be sensitive to errors found at the late stages of the network. On the other hand, learning in the nervous system occurs locally, and synaptic changes depend only on pre- and post-synaptic activities. It is unclear how local updates translate into coordinated changes across large populations of neurons and lead to sophisticated cognitive functions. Here we demonstrate that it is possible to achieve robust and invariant object representations in naturally observed network architectures using only biologically realistic local learning rules. Adopting operations fundamentally different from current ANN models, unsupervised recurrent networks can learn to represent and categorize objects through sensory experiences without propagating or detecting errors. This white box, fully interpretable networks can extract clean images from their corrupted forms and produce representations prospectively robust against unfamiliar perturbations. Continuous learning does not cause catastrophic forgetting commonly observed in ANNs. Without explicit instructions, the networks can classify objects and represent the identity of 3D objects regardless of perspective, size, or position. These findings have substantial implications for understanding how biological brains achieve invariant object representation and for developing biologically realistic intelligent networks that are efficient and robust.
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