PMC

MIMO stimulation on DNMS trials. (a) Prediction of strong SR encoding in CA1 via MIMO model used to derive spatio-temporal patterns of electrical stimulation delivered to CA1 electrode sites (right). Stimulation consisted of biphasic electrical pulses (0.5–2.0 V, 10–50 µV, 0.5 s duration, minimum interpulse interval=50 ms), delivered on trials with delay durations of 15–50 s. (b) DNMS delay curves for animals (n = 9) tested under normal (no stimulation) conditions, and when 35% of trials received CA1 stimulation commencing 3.0 s prior to the SR. Stimulation sessions consisted of (a) CA1 patterns derived from the MIMO model (green), (b) reversal of patterns to deliver Right trial stimulation on Left trials, and vice versa (red), or (c) the same stimulation “power,” but with randomization of MIMO coefficients to disrupt CA1 spatiotemporal firing patterns. Mean (±S.E.M.) DNMS performance is shown averaged over five sessions (i.e., 500 trials) per condition. Asterisks (*p < 0.01, **p < 0.001) indicate significant difference in DNMS performance compared to Control (No Stim.) trials. Inset: Weighted CA1 firing plot derived from strong-code DNMS trials when the MIMO coefficients were “scrambled” by randomizing the spatiotemporal sequence of coefficients (by neuron and time), but not the actual CA1 firing. (c) Same data as in B sorted according to Left versus Right DNMS trials. Bars indicate mean (±S.E.M.) DNMS performance averaged over all delays, dark shading=Left DNMS trials, light shading=Right DNMS trails. Asterisks (**p < 0.001) indicate significant difference in DNMS performance compared to Control (No Stim.) trials.

Cumulative effects of MIMO generated strong SR code CA1 stimulation. (a) Cumulative increase (left) in overall performance on nonstimulated trials (mean%correct ± SEM) in animals (n = 8) receiving 25–30 stimulation trials (Stim Trials) per session over successive sessions. Individualized MIMO stimulation patterns produced a more rapid increase in performance after 240 trials than Generic stimulation patterns that took an additional 160 trials (400 trials total) to produce equivalent performance levels (shown at breakpoint in axis). The right half of the plot (after the break point) shows the decay (Decline) of facilitated performance over an additional 600 trials with the same delays when stimulation was no longer delivered on any trials during the sessions. Dashed line=performance of equivalently trained animals (n = 20) that never received stimulation. (b) DNMS delay curves for phases of cumulative facilitation and decline in performance following 180 trials (Trl 180) and 240 trials (Trl 240) of MIMO (left) and Generic (right) stimulation and after cessation of stimulation for an additional 400 trials (Trl 800), corresponding to the periods shown in A. Distribution of SR code strengths for all trials within the same sessions are shown in the graphs to the right of the corresponding delay curves. Code Strength computed from overall ensemble firing rate multiplied by the MIMO coefficients for each trial (see Section II). Dashed vertical line indicates median code strength of 2.0 obtained prior to cumulative stimulation procedure. Asterisks (*p < 0.01, **p < 0.001) indicate significant increase over control (no stimulation, Trl 0); hash marks (# p < 0.01, ##p < 0.001) indicate significant difference in code distribution from control (Trl 0).

DNMS task and associated hippocampal ensemble activity. (a) DNMS trial diagram of SR, Delay, and NR for water reward (WR) Timeline below indicates sequence of phases in the task: intertrial interval (IT); Sample Presentation (SP); Sample response (SR); Delay interval (Delay); last nosepoke (NP) during Delay (LNP); Nonmatch response (NR); Reinforcement (water reward) (Reinf.). (b) DNMS performance for 15 animals. Blue trace indicates mean ± S.E.M. % correct Nonmatch Responses on DNMS trials sorted according to length of delay (in 5 s increments). Red Trace indicates latency to perform the Nonmatch Response on the same trails. (c)MIMO nonlinear model analysis of DNMS generated hippocampal ensemble activity. Left: Color contours depict ensemble firing from 16 CA3 neurons (8 per hemisphere) recorded up to 3.0 s prior to the SR on Left (upper) or Right (lower) DNMS trials. Center: Schematic of MIMO model. CA3 input spike trains X₁ − X_n predict CA1 output spike trains Y₁ − Y_n at right. The input–output relationship between CA3 and CA1 is modeled b parallel MISO nonlinear equations: w = u(k, x) + a(h, y) + n(σ), where k indicates the Volterra kernels, σ is a noise term, and H is a feedback term. The MIMO model is constructed of parallel MISO computations expanded with corresponding definitions. Near Right: MIMO predicted CA1 output for Left and Right Sample derived via MIMO model from the CA3 firing input at left. Far Right: Actual CA1 firing corresponding to the MIMO model output. Neuron firing rates spike probability indicated by color scale.

DNMS performance level corresponds to strength of SR encoding. (a) Contour Plots illustrate derivation of SR encoding using the MIMO model. Top: CA1 firing in Sample phase of correct Left trials (Actual Firing) constitutes a Strong Code. CA3 firing for the same trials, input to the MIMO model, produces the corresponding Strong Code output prediction of CA1 firing. Weighted CA1 firing map (Model: Strong Code) is derived from product of CA1 firing with model coefficients as x_Left ● w_Left, where x_Left = Left SR ensemble firing (by neuron and time), and w_Left = the MIMO weighting coefficients for correct Left trials. Bottom: CA1 firing corresponding to opposite trial (Right Sample) demonstrates lack of correlation between CA1 firing (Actual Firing) and model coefficients which produces low code strength (Model: Weak Code) for the Left Sample. Center: Graph shows DNMS performance on trials with varying strength of SR encoding. Code strength computed as the correlation between CA1 actual firing on DNMS trials and MIMO-predicted Left Correct SR firing (normalized with mean = 0, standard deviation = 1). Colored-coded curves show DNMS performance on trials at different delay durations associated with the respective weighted CA1 firing (dashed arrows); for example, Strong Codes are associated with high level (correct) DNMS performance at all delays (black trace), while Weak Codes are predominantly associated with errors at delays > 10 s (red line). Green dashed trace illustrates “Normal” DNMS performance equivalent to control trace in B: Intermediate Codes: “Mixed,” “Normal,” and “Sparse” codes result from combination of both correct and incorrect trial firing input to the MIMO model. Each MIMO model output is shown with actual firing representative from single trials with performance consistent with the behavioral graph for the respective code strength at left. Errors are indicated for delays at each code level. The “Normal” code reflects the MIMO model for trials associated with the behavioral performance associated with 1.5 code strength (Green dashed line) that occurred on normal trials with no strong or weak codes present. Insets: Schematic illustrates correspondence between model coefficients (red curve) and CA1 firing (raster) for each of the SR code conditions.

Neural contributions to MIMO model. (a) Mean firing rate contour maps of hippocampal ensemble activity averaged across 20 animals from neurons recorded at each respective CA1 electrode location in arrays in both hemispheres. Mean of MIMO derived strong SR codes calculated 3.0 s prior to SR occurrence (−3.0 to 0.0 s, X- axis) for left and right lever positions appropriate to the two types of DNMS trial. (b) Mean firing rate contour maps averaged across the same 20 animals as in A for MIMO derived weak SR codes. High density of firing in strong SR codes. (A) contrasts sharply with lack of firing density in weak SR codes. (B) averaged across the same animals and DNMS sessions. (c) Cross-correlation of each of the 20, single animal, Strong SR Code patterns with the mean pattern in A averaged over the remaining 19 animals. Distribution of high correlations (R > 0.38, p < 0.001) reveals consistent pattern of SR firing across animals, leading to identification of “Generic” MIMO strong SR codes for Left and Right lever trials. (d) Examples of FCTs recorded in hippocampal ensembles, shown as single trial raster displays (dots = neural spikes, row of dots = one DNMS trial,) and perievent histograms for ±1.5 s relative to SR and NR responses designated as 0.0 s. Position cells fire only to behavioral responses on one lever position (Left Position cell shown) in either phase of DNMS task. Phase cells fire only during Sample or Nonmatch phase, irrespective of position of lever response (Nonmatch phase cell shown). Conjunctive cells fire only to a particular combinations of position and phase (i.e., Left Nonmatch cell shown). Trial-type cells combine conjunctive cell attributes and fire in both Sample and Nonmatch phases of task but only on a single type of trial and do not fire on the opposite type of trial (Right Sample/ Left Nonmatch Trial-type cell shown). (e) Contribution of FCTs to mean strong SR code in A. Individual ensembles exhibiting strong SR code patterns for 20 animals were analyzed, and neurons with a significant tendency to fire in this pattern (i.e., significant variance contributions: > 50% coefficient weighting) were classified as to FCT and ranked in terms of percentage of total cells contributing to the strong SR code pattern. Graphs indicate frequency distribution of different FCTs contributing to Left and Right strong SR codes in A. Asterisks (*p < 0.01, **p < 0.001) indicate significantly higher than chance contribution of a given FCT (, p < 0.001); hash marks (#p < 0.01) indicate significantly lower contribution than expected by chance (, p < 0.01).