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Asopa, A.

Publications and source records attributed to Asopa, A..

2 recordsLinked to original sources
bioRxiv2026

Selection navigates a degenerate circuit space: behavioral individuation without structural differentiation in constrained neuroevolution

Neural degeneracy, the capacity of structurally distinct circuits to perform the same function, is typically studied as an emergent property of biological neural systems. Here we impose it by construction, engineering the conditions that make degeneracy the expected outcome of evolutionary search, and ask what individual-target selection achieves within that degenerate space. We use constrained neuroevolution to evolve 14-neuron recurrent circuits (Dale's Law, sparse connectivity, quantized weights) replicating the natural navigation behavior of 9 individual mice across 54 independent evolutionary runs. Architectural constraints impose a structural floor: no aggregate circuit statistic differs across mice (0/18 features, all pFDR > 0.47), and this uniformity extends to the topology axis itself: topology distance predicts behavioral distance at no scale, in evolved or random constrained agents alike, and no structural axis carries significant information about behavioral identity (maximum NMI = 0.2173). Yet behavioral individuation is robust: own-mouse fitness error is 33.4% lower than cross-mouse error, and this specialization persists on held-out data. We show that what individual-target selection shapes is not circuit structure but the strength of functional sensitivity commitment: specialists develop roughly 6.4-6.7x higher sensitivity variance than generalists trained on all mice simultaneously (9/9 mice, mouse-level p = 0.002), despite exploring statistically indistinguishable topological diversity. The particular pathways a circuit commits to, carry no shared mouse-specific signature, so behavioral individuation is expressed as a magnitude of functional commitment rather than a structural or pathway fingerprint - degeneracy that operates not just at the structural level but at the level of the computational strategies that implement individual behavior.

neuroscience↗
bioRxiv2024

Short-term plasticity of EI balance at single neurons can detect pattern transitions

Sensory input and internal context converge onto the hippocampus as spatio-temporal activity patterns. Transitions in these input patterns are frequently salient. We demonstrate that short-term potentiation (STP) mediates escape from EI balance to implement mismatch detection in spatiotemporally patterned activity sequences. We characterized STP in the mouse hippocampus CA3-CA1 network using optogenetic patterned stimuli in CA3 while recording from CA1 pyramidal neurons. STP modulates EI summation across patterns, first amplifying, then reducing responses. We parameterized a multiscale model of network projections onto hundreds of E and I boutons on a CA1 neuron, each including stochastic signaling to mediate STP. The model detected mismatches in trains of input patterns, which we experimentally confirmed. Mismatch selectivity depends on stimulus overlap, network weights, and connectivity. It is robust over a wide range of model parameters and assumptions about input spike timing jitter, postsynaptic spiking and stochasticity. Finally, we predict that optimal mismatch selectivity can be tuned over low to high gamma frequencies by modulating network parameters, and show that there is strong mismatch detection for gamma-frequency bursts between theta cycles, consistent with theta-tuned snapshots of novel input.

neuroscience↗