Prior investigations have demonstrated from online recordings of hippocampal activity that information encoded during the SR and employed in the nonmatch phase is predictive of successful or unsuccessful performance based on the duration of the ensuing delay interval (Deadwyler et al 2007
, Hampson et al 2008
). These investigations have determined that the retrieval process (NR) is not as critical as the encoding (SR) phase of the task since errors are correlated more with lack of effective hippocampal cell firing during the SR (Simeral et al 2005
). DNMS trials are grouped by 5–10 s intervals of delay duration, and plotted as mean (± S.E.M.) percentage of correct trials. Animals were trained on trials with delays of 1–30 s, then subjected on a random infrequent basis to ‘probe’ trials, with delays >30 s, employed to assess accuracy of the MIMO model and closed loop manipulations. Control performance on ‘probe’ trials typically resulted in near ‘chance’ (50% correct) performance and provided a consistent basis for testing the application of the MIMO model and derived patterns of electrical stimulation. The effectiveness or ‘strength’ of the SR encoding process was evaluated with respect to whether the trial was rewarded or not as a result of the choice of the NR. ‘Strong’ SR codes are defined as MIMO derived firing patterns associated with correct responding on long delay trials, while ‘weak’ SR codes are defined as MIMO patterns that occur on error (non-rewarded) trials irrespective of duration of delay. Therefore, performance of the task on a given trial is characterized by (1) an interaction between ‘strength’ of the SR code (i.e. ‘completeness’ of the spatiotemporal firing pattern exhibited on correct long delay trials) for sample lever position and (2) duration of the ensuing delay preceding the nonmatch phase where the encoded SR information is retrieved to perform the NR ().
3.1. MIMO model measure of SR strength of encoding predicts learned behavior
The method for assessing the relationship between strength of the SR code and performance utilized a closed loop paradigm () in which CA3 activity occuring 1.5–3.0 s prior to the SR was used as input to the MIMO model to provide a prediction of the output firing pattern of simultaneously recorded CA1 cells (Hampson et al 2011
). The strength of the SR code categorized on the basis of correlations with prior performance as defined above was utilized to adjust the duration of the delay online during the same trial (). Depiction of differences in strong versus weak SR codes is shown by the mean firing rate contour maps in (lower right). Because of the extended time interval between the SR and NR due to interposed delays in the task it was possible to adjust the delay duration on the same trial as a function of the ‘strength’ of the encoded SR, determined online by the MIMO model. The closed loop procedure adjusted delay duration primarily in two ways: (1) on any trial in the session where a weak SR code was detected the delay was reduced to 10 s, and (2) on trials with detected strong SR codes, delays were extended to 40, 50, or 60 s. The graph in (lower left) shows the two extreme conditions of closed loop control of performance by (1) shortening the delay to 10 s for weak SR code trials (CL-weak code) or (2) lengthening the delay to > 30 s when strong SR codes were detected (CL-strong codes). It is clear that performance on strong SR code trials was significantly elevated (F
(1,731) = 25.84, p
< 0.001) during normal delays (≤30 s) and on extended delay (≥40 s) trials, relative to performance on non-closed loop trials of the same duration (control vs CL-strong code, ). Trials with weak SR codes adjusted to 10 s delays by the closed loop paradigm were performed nearly perfectly by all animals (CL-weak codes, ). However, also shows that performance was significantly improved on the remaining trials at all delays, (CL-weak code versus control sessions; F
(1,731) = 7.96, p
< 0.01) due to the fact that weak code trials were eliminated by the closed loop as a potential source of error on longer delay trials (weak codes, ).
Figure 2 Closed loop feedback using MIMO identification of SR encoding strength to control DNMS performance. Top: CA3 and CA1 neuronal firing (contour, left) analyzed via MIMO model (center) predicts CA1 firing patterns (contour, right). Strength of SR code in (more ...)
Verification of the MIMO model predictions is shown for trials ≤30 s in which strong or weak SR codes were detected but delays not altered by the closed loop procedure ( CL-strong code trials <30 s, weak codes shown by inverted triangles). It is clear that performance was maximal when strong codes were detected (CL-strong codes), but markedly decreased on all trials >15 s when weak codes were detected and at risk for error (weak code vs control, F
(1, 731) = 15.61, p
< 0.001) as shown in . It is clear that DNMS performance under normal conditions was a function of two factors: (1) the strength of the SR code, and (2) the coincidence of that code strength with duration of the subsequent randomly assigned delay. Since the delay between SR and NR was unpredictable, strength of SR encoding on any given trial was arbitrary and reflected possible ‘anticipation’ of the subsequent delay duration. However, as shown previously (Hampson et al 2011
), if delays were extended beyond previously trained limits (CL-strong code >30 s, ), performance on strong SR code trials also declined in a delay-dependent manner.
3.2. MIMO model stimulation reverses mismatches of SR encoding strength and delay
A unique application of the MIMO model in the closed loop paradigm described above is the ability to substitute electrical stimulation pulses in patterns that mimicked the firing of CA1 outputs predicted from the MIMO model, to facilitate memory under circumstances where there were mismatches in the online generated SR code strength for the duration of the ensuing delay. Such stimulation was used to enhance memory on trials in which the MIMO model detected weak SR codes for trials with delays >10 s (). Thus substitution of electrical stimulation pulses at the same electrode loci and in the same temporal pattern as strong SR codes reversed mismatches between spontaneously generated SR code strength and the duration of subsequent trial delay (). This stimulation procedure was capable of facilitating performance in the same manner as MIMO predicted strong SR codes when delays were extended (30–60 s) in the task (). Trials with interposed CA1 stimulation patterns derived from MIMO generated strong SR codes (stim MIMO model) were significantly increased versus trials on which no stimulation (no stim) was delivered (F(1,731) = 11.50, p < 0.001). Trials in which stimulation at the same intensity was generated from scrambled MIMO model CA1 coefficients () did not differ from no stim trials (F(1,731) = 2.12, ns.). Under such circumstances MIMO model stimulation parameters were applied to the same electrode locations but in a different spatiotemporal pattern (scrambled coefficients, ). The fact that stimulation patterns delivered with scrambled coefficients did not consistently suppress performance as might be expected relates to the fact that only some of the scrambled patterns produced the exact SR codes for the opposite lever, which as shown below () was required to drive performance below control levels. In addition, the effectiveness of the stimulation patterns was shown to decline across the same span of extended delays in a manner similar to closed loop trials with MIMO detected strong SR code firing patterns (). These results demonstrate that substitution of strong SR code patterns of electrical stimulation eliminated trials that were at risk for error when weak SR codes were detected by the MIMO model ().
Figure 3 MIMO model controlled stimulation codes. Top: prediction of ‘weak’ SR encoding in CA1 via MIMO model results in CA1 electrical stimulation with spatio-temporal patterns corresponding to strong SR code (green raster ‘substitution’, (more ...)
Figure 5 Reverse stimulation to verify specificity of closed loop MIMO model stimulation. Top: validity of CA1 ‘strong SR code’ patterns determined by delivering stimulation patterns appropriate for the opposite type of DNMS trial (‘reversed’) (more ...)
3.3. MIMO model derived stimulation replaces lost mnemonic function
The above demonstration that MIMO model stimulation could override the mismatch between anticipated duration of delay and strength of the SR code suggests that such stimulation, if synchronized and delivered at the time of occurrence of the SR, could provide a means of inducing strong SR codes. This was examined under the most severe condition by testing animals when MIMO model predications from hippocampal CA3 inputs were not available online. As in previous studies (Hampson et al 1999a
) normal operation of hippocampal circuitry was seriously impaired by chronic infusion over a two week period of the glutamatergic transmission blocking agent, MK801 (Collingridge et al 1983
, Coan et al 1987
). In animals that were trained to perform the DNMS task, infusion of MK801 unilaterally into the CA3 region (37.5 μ
; 1.5 mg ml−1
, 0.25 μ
) for 14 days produced significant disruptions in performance at all delay intervals and significantly reduced the appearance of strong SR codes detected online by the MIMO model (). However, since the MIMO model delivered successful SR stimulation patterns in the same animals under normal (nondrug) circumstances, each animal’s previously identified effective MIMO stimulation pattern was delivered at the time the animal pressed the sample lever (SR) during testing while MK801 was being chronically infused. MK801 significantly suppressed DNMS performance at all delays compared to control levels (F
(1,416) = 13.37, p
< 0.001). However, performance was significantly improved (F
(1,416) = 9.52, p
< 0.001) on trials in which CA1 stimulation was delivered with strong SR code patterns, and remained only slightly (F
(1,416) = 5.12, p
= 0.02) below control levels. shows that delivery of MIMO stimulation patterns was highly effective in reversing the detrimental effects of the drug and significantly increased performance on all trials with delays >10 s. Even though performance was not elevated back to normal (pre-MK801) levels, delivery of effective MIMO model derived stimulation patterns at the SR significantly reduced the drug-induced deficit.
Figure 4 MIMO model repair of hippocampal encoding with previously effective CA1 stimulation patterns delivered at the time of the SR. Top: intrahippocampal infusion (see , inset center right, cannula shown entering CA3 next to electrode array on right) (more ...)
3.4. Demonstration of specificity of MIMO model derived stimulation patterns
Together, the above demonstrations (–) show that detection, imposition and closed loop control based on MIMO-derived ensemble firing patterns control DNMS task behavior by enhancing encoding of the SR sufficient to counteract the effects of the intervening delay interval. It is obvious that such enhanced encoding facilitates making the NR decision, but the specificity of the MIMO model stimulation pattern can be assessed in the same experimental context. A distinct test of the specificity of the information the SR codes and their strength was ascertained by reversing the MIMO stimulation patterns such that the pattern for the left lever was delivered when the animal was presented with, and made the SR, on the right lever and vice versa. Delivery of the stimulation pattern specific for encoding the lever opposite the one presented and responded to sample, should not only eliminate enhanced encoding of the SR, but should actually impair performance below normal levels due to stimulation induced miscoding of information required in the nonmatch phase to correctly select the NR. Therefore the frequency of errors (match responses) should increase in comparison to trials in which no stimulation occurred. shows that by reversing the MIMO model SR stimulation patterns for the lever presented in the sample phase, performance was reduced below normal levels (F(1,731) = 12.53, p < 0.001) relative to control trials in which no stimulation occurred. In contrast, trials within the same sessions in which SR codes were delivered appropriate for the lever presented (normal stim) exhibited performance significantly above control levels (F(1,731) = 15.76, p < 0.001). The results shown in support the conclusion that delivery of the opposite SR stimulation pattern was capable of overriding normal encoding tendencies by significantly impairing performance on trials with deliberate mismatches between the SR stimulation pattern and lever position. It is clear that reversed strong SR code patterns had a large impact on performance. To validate the fact that the MIMO stimulation patterns were effective, also shows that performance was markedly improved if the same SR stimulation patterns were delivered when the appropriate lever was presented in the sample phase. One final control manipulation for specificity of the MIMO derived stimulation patterns was to delay delivery of the stimulation by 3–5 s after occurrence of the SR (delayed stimulation, ). The results show that if strong SR code stimulation patterns were delayed by more that 3.0 s there was no effect on performance (stim late versus no stim: F(1,731) = 3.17, ns).