GoKawiila, DT population activity maintains persistent, lateralized history signals across diverse visual stimulus conditions. Activity corresponds to the left-DT − right-DT. Magenta, stimulus on the left side, green, stimulus on the right side. b, Generalization to escape behaviour in a “threatening agent” assay. b1: Schematic of the closed-loop paradigm where a dark disc actively pursues the fish, engaging it from the left or right side. b2,b3: Quantification of serial dependence in escape manoeuvres; avoidance distance increases on the second escape following a recent threat encounter, compared with the first escape, for both left- and right-obstacle trials. Two-sided paired Student’s t-tests: left-obstacle trials, P = 0.034 (n = 168 trials); right-obstacle trials, P = 0.042 (n = 145 trials). Data pooled from 10 fish. c, Model extension. To extend memory beyond a two-trial context, Layer 3 (L3) was partitioned into K independent NMDA-recurrent subgroups (here, K = 7). Within each subgroup, recurrent dynamics support either low or high stable NMDA activation levels (see Supplementary Note 1: ‘Analysis of multistable persistent activity in layer 3’). By lowering the NMDA-channel opening probability, entry into the high-NMDA state becomes stochastic rather than deterministic. Subgroups were uncoupled (no inter-subgroup recurrence), and the net L3 output was defined as the linear sum across subgroups. Consequently, consecutive “stay” trials cumulatively increase the probability of high NMDA state recruitment across the population, yielding a graded code that represents longer histories. Conversely, a “switch” trial acts as a global reset, reliably driving the network into the low-activity state and erasing the accumulated sequence information. d,e, Graded history encoding over multiple trials. Top, L3 population output traces for different trial sequences, grouped by the stimulus identity three trials back (d, t − 3) or four trials back (e, t − 4) while holding more recent trials matched. Bottom, L3 output measured in the late-interval window immediately before the next-trial onset. The increased separation across deeper-history conditions demonstrates that stochastic recruitment enables the network to integrate evidence over longer timescales. In d, three-trials-back sequences are shown (RRR, n = 61; LRR, n = 45; RLR, n = 51; LLR, n = 46; RRL, n = 48; LRL, n = 52; RLL, n = 48; LLL, n = 39). In e, four-trials-back sequences are shown (RRRR, n = 37; LRRR, n = 24; RLRR, n = 20; LLRR, n = 22; RRLR, n = 25; LRLR, n = 25; RLLR, n = 23; LLLR, n = 23; RRRL, n = 22; LRRL, n = 21; RLRL, n = 31; LLRL, n = 21; RRLL, n = 22; LRLL, n = 25; RLLL, n = 23; LLLL, n = 16). Data are shown as mean ± s.e.m.
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