The SR algorithm described in Stachenfeld et al., 2017 first discretizes the environment explored by an animal (whether a physical or abstract space) into a set of n states that the animal transitions through over time (Figure 1A). The animal’s behavior can then be thought of as a Markov chain with a corresponding transition probability matrix TN×N (Figure 1B). T gives the probability that the animal transitions to a state s′ from the state s in one time step: Tj⁢i=P⁢(s′=i|s=j). The SR matrix is defined as(1)M=∑t=0∞γtTt=(I−γT)−1

Here, γ∈(0,1) is a temporal discount factor. Mj⁢i can be seen as a measure of the occcupancy of state i over time if the animal starts at state j, with γ controlling how much to discount time steps in the future (Figure 1C). The SR of state j is the jth row of M and represents the states that an animal is likely to transition to from state j. Stachenfeld et al., 2017 demonstrate that, if one assumes each state drives a single neuron, the SR of j resembles the population activity of hippocampal neurons when the animal is at state j (Figure 1D). They also show that the ith column of M resembles the place field (activity as a function of state) of a hippocampal neuron representing state i (Figure 1E). In addition, the ith column of M shows which states are likely to lead to state i.

We begin by drawing connections between the SR algorithm (Stachenfeld et al., 2017) and an analogous neural network architecture. The input to the network encodes the current state of the animal and is represented by a layer of input neurons (Figure 1FG). These neurons feed into the rest of the network that computes the SR (Figure 1FG). The SR is then read out by a layer of output neurons so that downstream systems receive a prediction of the upcoming states (Figure 1FG). We will first model the inputs ϕ as one-hot encodings of the current state of the animal (Figure 1FG). That is, each input neuron represents a unique state, and input neurons are one-to-one connected to the hidden neurons.

We first consider an architecture in which a recurrent neural network (RNN) is used to compute the SR (Figure 1F). Let us assume that the T matrix is encoded in the synaptic weights of the RNN. In this case, the steady state activity of the network in response to input ϕ retrieves a row of the SR matrix, M⊺ϕ (Figure 1F, subsection 4.14). Intuitively, this is because each recurrent iteration of the RNN progresses the prediction by one transition. In other words, the tth recurrent iteration raises T to the tth power as in Equation 1. To formally derive this result, we first start by defining the dynamics of our RNN with classical rate network equations (Amarimber, 1972). At time t, the firing rate x(t) of N neurons given each neurons’ input ϕ(t) follows the discrete-time dynamics (assuming a step size Δ⁢t=1)(2)Δx=−x(t)+γJf(x(t))+ϕ(t)

Here, γ scales the recurrent activity and is a constant factor for all neurons. The synaptic weight matrix J∈ℛN×N is defined such that Ji⁢j is the synaptic weight from neuron j to neuron i. Notably, this notation is transposed from what is used in RL literature, where conventions have the first index as the starting state. Generally, f is some nonlinear function in Equation 2. For now, we will consider f to be the identity function, rendering this equation linear. Under this assumption, we can solve for the steady state activity xss as(3)xss=(I−γJ)−1ϕ

Equivalence between Equation 1 and Equation 3 is clearly reached when J=T⊺ (Russek et al., 2017; Vértes and Sahani, 2019). Thus, if the network can learn T in its synaptic weight matrix, it will exactly compute the SR.

Here, the factor γ represents the gain of the neurons in the network, which is factored out of the synaptic strengths characterized by J. Thus, γ is an independently adjustable factor that can flexibly control the strength of the recurrent dynamics (see Sompolinsky et al., 1988). A benefit of this flexibility is that the system can retrieve successor representations of varying predictive strengths by modulating the gain factor γ. In this way, the predictive horizon can be dynamically controlled without any additional learning required. We will refer to the γ used during learning of the SR as the baseline γ, or γB.

We next consider what is needed in a learning rule such that J approximates T⊺. In order to learn a transition probability matrix, a learning rule must associate states that occur sequentially and normalize the synaptic weights into a valid probability distribution. We derive a learning rule that addresses both requirements (Figure 1H, Appendix 2),(4)ΔJij=ηxi(t)xj(t−1)−ηxj(t−1)∑kJikxk(t−1),

where η is the learning rate. The first term in Equation 4 is a temporally asymmetric potentiation term that counts states that occur in sequence. This is similar to spike-timing dependent plasticity, or STDP (Bi and Poo, 1998; Skaggs and McNaughton, 1996; Abbott and Blum, 1996).

The second term in Equation 4 is a form of synaptic depotentiation. Depotentiation has been hypothesized to be broadly useful for stabilizing patterns and sequence learning (Földiák, 1990; Fiete et al., 2010), and similar inhibitory effects are known to be elements of hippocampal learning (Kullmann and Lamsa, 2007; Lamsa et al., 2007). In our model, the depotentiation term in Equation 4 imposes local anti-Hebbian learning at each neuron– that is, each column of J is normalized independently. This normalizes the observed transitions from each state by the number of visits to that state, such that transition statistics are correctly captured. We note, however, that other ways of column-normalizing the synaptic weight matrix may give similar representations (Appendix 7).

Crucially, the update rule (Equation 4) uses information local to each neuron (Figure 1H), an important aspect of biologically plausible learning rules. We show that, in the asymptotic limit, the update rule extracts information about the inputs ϕ and learns T exactly despite having access only to neural activity x (Appendix 3). We will refer to an RNN using Equation 4 as the RNN-Successor, or RNN-S. Combined with recurrent dynamics (Equation 3), RNN-S computes the SR exactly (Figure 1H).

As an alternative to the RNN-S model, we consider the conditions necessary for a feedforward neural network to compute the SR. Under this architecture, the M matrix must be encoded in the weights from the input neurons to the hidden layer neurons (Figure 1G). This can be achieved by updating the synaptic weights with a temporal difference (TD) learning rule, the standard update used to learn the SR in the usual algorithm. Although the TD update learns the SR, it requires information about multiple input layer neurons to make updates for the synapse from input neuron j to output neuron i (Figure 1I). Thus, it is useful to explore other possible mechanisms that are simpler to compute locally. We refer to the model described in Figure 1I as the feedforward-TD (FF-TD) model. The FF-TD model implements the canonical SR algorithm.

To evaluate the effectiveness of the RNN-S learning rule, we tested its accuracy in learning the SR matrix for random walks. Specifically, we simulated random walks with different transition biases in a 1D circular track environment (Figure 2A). The RNN-S can learn the SR for these random walks (Figure 2B).

Because equivalence is only reached in the asymptotic limit of learning (i.e. Δ⁢J→0), our RNN-S model learns the SR slowly. In contrast, animals are thought to be able to learn the structure of an environment quickly (Zhang et al., 2021), and neural representations in an environment can also develop quickly (Monaco et al., 2014; Sheffield and Dombeck, 2015; Bittner et al., 2015). To remedy this, we introduce a dynamic learning rate that allows for faster normalization of the synaptic weight matrix, similar to the formula for calculating a moving average (Appendix 4). For each neuron, suppose that a trace n of its recent activity is maintained with some time constant λ∈(0,1),, (5)n(t)=∑t′<tλ(t−t′)x(t′)

If the learning rate of the outgoing synapses from each neuron j is inversely proportional to nj(η=1nj(t)), the update equation quickly normalizes the synapses to maintain a valid transition probability matrix (Appendix 4). Modulating synaptic learning rates as a function of neural activity is consistent with experimental observations of metaplasticity (Abraham and Bear, 1996; Abraham, 2008; Hulme et al., 2014). We refer to this as an adaptive learning rate and contrast it with the previous static learning rate. We consider the setting where λ=1, so the learning rate monotonically decreases over time (Figure 2C). In general, however, the learning rate could increase or decrease over time if λ<1 (Figure 2C), and n could be reset, allowing for rapid learning. Our learning rule with the adaptive learning rate is the same as in Equation 4, with the exception that η=min⁢(1nj⁢(t),1.0) for synapses J*j. This learning rule still relies only on information local to the neuron as in Figure 1H.

The RNN-S with an adaptive learning rate normalizes the synapses more quickly than a network with a static learning rate (Figure 2D, Figure 2—figure supplement 1) and learns T faster (Figure 2E, Figure 2—figure supplement 1). The RNN-S with a static learning rate exhibits more of a tradeoff between normalizing synapses quickly (Figure 2D, Figure 2—figure supplement 1A) and learning M accurately (Figure 2E, Figure 2—figure supplement 1). However, both versions of the RNN-S estimate M more quickly than the FF-TD model (Figure 2F, Figure 2—figure supplement 1).

Place fields can form quickly, but over time the place fields may skew if transition statistics are consistently biased (Stachenfeld et al., 2017; Monaco et al., 2014; Sheffield and Dombeck, 2015; Bittner et al., 2015). The adaptive learning rate recapitulates both of these effects, which are thought to be caused by slow and fast learning processes, respectively. A low learning rate can capture the biasing of place fields, which develops over many repeated experiences. This is seen in the RNN-S with a static learning rate (Figure 2G). However, a high learning rate is needed for hippocampal place cells to develop sizeable place fields in one-shot. Both these effects of slow and fast learning can be seen in the neural activity of an example RNN-S neuron with an adaptive learning rate (Figure 2H). After the first lap, a sizeable field is induced in a one-shot manner, centered at the cell’s preferred location. In subsequent laps, the place field slowly distorts to reflect the bias of the transition statistics (Figure 2H). The model is able to capture these learning effects because the adaptive learning rate transitions between high and low learning rates, unlike the static version (Figure 2I).

Thus far, we have assumed that the RNN-S learning rule uses pre→post activity over two neighboring time steps (Equation 4). A more realistic framing is that a convolution with a plasticity kernel determines the weight change at any synapse. We tested how this affects our model and what range of plasticity kernels best supports the estimation of the SR. We do this by replacing the pre→post potentiation term in Equation 4 with a convolution:(6)ΔJij=xi(t)∑t′=−∞tK+(t−t′)xj(t′)+xj(t)∑t′=−∞tK−(t−t′)xi(t′)−ηxj(t−1)∑kJikxk(t−1)

In the above equation, the full kernel K is split into a pre→post kernel (K+) and a post→pre kernel (K-). K+ and K- are parameterized as independent exponential functions, A⁢e-t/τ.

To systematically explore the space of plasticity kernels that can be used to learn the SR, we performed a grid search over the sign and the time constants of the pre→post and post→pre sides of the plasticity kernels. For each fixed sign and time constant, we used an evolutionary algorithm to learn the remaining parameters that determine the plasticity kernel. We find that plasticity kernels that are STDP-like are more effective than others, although plasticity kernels with slight post→pre potentiation work as well (Figure 2J). The network is sensitive to the time constant and tends to find solutions for time constants around a few hundred milliseconds (Figure 2JK). Our robustness analysis indicates the timescale of a plasticity rule in such a circuit may be longer than expected by standard STDP, but within the timescale of changes in behavioral states. We note that this also contrasts with behavioral timescale plasticity (Bittner et al., 2015), which integrates over a window that is several seconds long. Finally, we see that even plasticity kernels with slightly different time constants may give results with minimal error from the SR matrix, even if they do not estimate the SR exactly (Figure 2J). This suggests that, although other plasticity rules could be used to model long-horizon predictions, the SR is a reasonable –although not strictly unique– model to describe this class of predictive representations.

We next investigate how robust the RNN-S model is to the value of γ. Typically, for purposes of fitting neural data or for RL simulations, γ will take on values as high as 0.9 (Stachenfeld et al., 2017; Barreto et al., 2017). However, previous work that used RNN models reported that recurrent dynamics become unstable if the gain γ exceeds a critical value (Sompolinsky et al., 1988; Zhang et al., 2021). This could be problematic as we show analytically that the RNN-S update rule is effective only when the network dynamics are stable and do not have non-normal amplification (Appendix 2). If these conditions are not satisfied during learning, the update rule no longer optimizes for fitting the SR and the learned weight matrix will be incorrect.

We first test how the value of γB, the gain of the network during learning, affects the RNN-S dynamics. The dynamics become unstable when γB exceeds 0.6 (Figure 3—figure supplement 1). Specifically, the eigenvalues of the synaptic weight matrix exceed the critical threshold for learning when γB>0.6 (Figure 3A, ‘Linear’). As expected from our analytical results, the stability of the network is tied to the network’s ability to estimate M. RNN-S cannot estimate M well when γB>0.6 (Figure 3B, ‘Linear’). We explored two strategies to enable RNN-S to learn at high γ.

One way to tame this instability is to add a saturating nonlinearity into the dynamics of the network. This is a feature of biological neurons that is often incorporated in models to prevent unbounded activity (Dayan and Abbott, 2001) Specifically, instead of assuming the network dynamics are linear (f is the identity function in Equation 2), we add a hyperbolic tangent into the dynamics equation. This extends the stable regime of the network– the eigenvalues do not exceed the critical threshold until γB>0.8 (Figure 3A). Similar to the linear case, the network with nonlinear dynamics fits M well until the critical threshold for stability (Figure 3b). These differences are clear visually as well. While the linear network does not estimate M well for γB=0.8 (Figure 3B), the estimate of the nonlinear network (Figure 3c) is a closer match to the true M (Figure 3D). However, there is a tradeoff between the stabilizing effect of the nonlinearity and the potential loss of accuracy in calculating M with a nonlinearity (Figure 3—figure supplement 1).

We explore an alternative strategy for computing M with arbitrarily high γ in the range 0≤γ<1. We have thus far pushed the limits of the model in learning the SR for different γB. However, an advantage of our recurrent architecture is that γ is a global gain modulated independently of the synaptic weights. Thus, an alternative strategy for computing M with high γ is to consider two distinct modes that the network can operate under. First, there is a learning phase in which the plasticity mechanism actively learns the structure of the environment and the model is in a stable regime (i.e. γB is small). Separately, there is a retrieval phase during which the gain γR of the network can be flexibly modulated. By changing the gain, the network can compute the SR with arbitrary prediction horizons, without any changes to the synaptic weights. We show the effectiveness of separate network phases by simulating a 1D walk where the learning phase uses a small γB (Figure 3E). Halfway through the walk, the animal enters a retrieval mode and accurately computes the SR with higher γR (Figure 3E).

Under this scheme, the model can compute the SR for any γ<1 (Figure 3—figure supplement 1). The separation of learning and retrieval phases stabilizes neural dynamics and allows flexible tuning of predictive power depending on task context.

We wondered how RNN-S performs given more biologically realistic inputs. We have so far assumed that an external process has discretized the environment into uncorrelated states so that each possible state is represented by a unique input neuron. In other words, the inputs ϕ are one-hot vectors. However, inputs into the hippocampus are expected to be continuous and heterogeneous, with states encoded by overlapping sets of neurons (Hardcastle et al., 2017). When inputs are not one-hot, there is not always a canonical ground-truth T matrix to fit and the predictive representations are referred to as successor features (Barreto et al., 2017; Kulkarni et al., 2016). In this setting, the performance of a model estimating successor features is evaluated by the temporal difference (TD) loss function.

Using the RNN-S model and update rule (Equation 4), we explore more realistic inputs ϕ and refer to ϕ as ‘input features’ for consistency with the successor feature literature. We vary the sparsity and spatial correlation of the input features (Figure 4A). As before (Figure 3H), the network will operate in separate learning and retrieval modes, where γB is below the critical value for stability. Under these conditions, the update rule will learn(7)J=Rϕ⁢ϕ⁢(-1)⁢Rϕ⁢ϕ⁢(0)-1

at steady state, where Rϕ⁢ϕ⁢(τ) is the correlation matrix of ϕ with time lag τ (Appendix 3). Thus, the RNN-S update rule has the effect of normalizing the input feature via a decorrelative factor (Rϕ⁢ϕ⁢(0)-1) and mapping the normalized input to the feature expected at the next time step in a STDP-like manner (Rϕ⁢ϕ⁢(-1)). This interpretation generalizes the result that J=T⊺ in the one-hot encoding case (Appendix 3).

We wanted to further explore the function of the normalization term. In the one-hot case, it operates over each synapse independently and makes a probability distribution. With more realistic inputs, it operates over a set of synapses and has a decorrelative effect. We first ask how the decorrelative term changes over learning of realistic inputs. We compare the mean value of the STDP term of the update (xi⁢(t)⁢xj⁢(t-1)) to the normalization term of the update (xj⁢(t-1)⁢∑kJi⁢k⁢xk⁢(t-1)) during a sample walk (Figure 4B). The RNN-S learning rule has stronger potentiating effects in the beginning of the walk. As the model learns more of the environment and converges on the correct transition structure, the strength of the normalization term balances out the potentiation term. It may be that the normalization term is particularly important in maintaining this balance as inputs become more densely encoded. We test this hypothesis by using a normalization term that operates on each synapse independently (similar to Oja’s Rule, Oja, 1982, Appendix 5). We see that the equilibrium between potentiating and depressing effects is not achieved by this type of independent normalization (Figure 4B, Appendix 6).

We wondered whether the decorrelative normalization term is necessary for the RNN-S to develop accurate representations. By replacing the decorrelative term with an independent normalization, features from non-adjacent states begin to be associated together and the model activity becomes spatially non-specific over time (Figure 4C, top). In contrast, using the decorrelative term, the RNN-S population activity is more localized (Figure 4C, bottom).

Interestingly, we noticed an additional feature of place maps as we transitioned from one-hot feature encodings to more complex feature encodings. We compared the representations learned by the RNN-S in a circular track walk with one-hot features versus more densely encoded features. For both input distributions, the RNN-S displayed the same skewing in place fields seen in Figure 2 (Figure 4—figure supplement 1). However, the place field peaks of the RNN-S model additionally shifted backwards in space for the more complex feature encodings (Figure 4D). This was not seen for the one-hot encodings (Figure 4D). The shifting in the RNN-S model is consistent with the observations made in Mehta et al., 2000 and demonstrates the utility of considering more complex input conditions. A similar observation was made in Stachenfeld et al., 2017 with noisy state inputs. In both cases, field shifts could be caused by neurons receiving external inputs at more than one state, particularly at states leading up to its original field location.

We ask whether RNN-S can accurately estimate successor features, particularly under conditions of natural behavior. Specifically, we used the dataset from Payne et al., 2021, gathered from foraging Tufted Titmice in a 2D arena (Figure 5A). We discretize the arena into a set of states and encode each state as in Section 2.5. Using position-tracking data from Payne et al., 2021, we simulate the behavioral trajectory of the animal as transitions through the discrete state space. The inputs into the successor feature model are the features associated with the states in the behavioral trajectory.

We first wanted to test whether the RNN-S model was robust across a range of different types of input features. We calculate the TD loss of the model as a function of the spatial correlation across inputs ϕ (Figure 5B). We find that the model performs well across a range of inputs but loss is higher when inputs are spatially uncorrelated. This is consistent with the observation that behavioral transitions are spatially local, such that correlations across spatially adjacent features aid in the predictive power of the model. We next examine the model performance as a function of the sparsity of inputs ϕ (Figure 5C). We find the model also performs well across a range of feature sparsity, with lowest loss when features are sparse.

To understand the interacting effects of spatial correlation and feature sparsity in more detail, we performed a parameter sweep over both of these parameters (Figure 5D, Figure 5—figure supplement 1A-F). We generated random patterns according to the desired sparsity and smoothness with a spatial filter to generate correlations. This means that the entire parameter space is not covered in our sweep (e.g. the top-left area with high correlation and high sparsity is not explored). Note that since we generate ϕ by randomly drawing patterns, the special case of one-hot encoding is also not included in the parameter sweep (one-hot encoding is already explored in Figure 2). The RNN-S seems to perform well across a wide range, with highest loss in regions of low spatial correlation and low sparsity.

We want to compare the TD loss of RNN-S to that of a non-biological model designed to minimize TD loss. We repeat the same parameter sweep over input features with the FF-TD model (Figure 5E, Figure 5—figure supplement 1G). The FF-TD model performs similarly to the RNN-S model, with lower TD loss in regions with low sparsity or higher correlation. We also tested how the performance of both models is affected by the strength of γR (Figure 5F). Both models show a similar increase in TD loss as γR increases, although the RNN-S has a slightly lower TD loss at high γ than the FF-TD model. Both models perform substantially better than a random network with weights of comparable magnitude (Figure 5—figure supplement 1D).

Unlike in the one-hot case, there is no ground-truth T matrix for non-one-hot inputs, so representations generated by RNN-S and FF-TD may look different, even at the same TD loss. Therefore, to compare the two models, it is important to compare representations to neural data.

Finally, we tested whether the neural representations learned by the models with behavioral trajectories from Figure 5 match hippocampal firing patterns. We performed new analysis on neural data from Payne et al., 2021 to establish a dataset for comparison. The neural data from Payne et al., 2021 was collected from electrophysiological recordings in titmouse hippocampus during freely foraging behavior (Figure 6A). Payne et al., 2021 discovered the presence of place cells in this area. We analyzed statistics of place cells recorded in the anterior region of the hippocampus, where homology with rodent dorsal hippocampus is hypothesized (Tosches et al., 2018). We calculated the distribution of place field size measured relative to the arena size (Figure 6B), as well as the distribution of the number of place fields per place cell (Figure 6C). Interestingly, with similar analysis methods, Henriksen et al., 2010 see similar statistics in the proximal region of dorsal CA1 in rats, indicating that our analyses could be applicable across organisms.

In order to test how spatial representations in the RNN-S are impacted by input features, we performed parameter sweeps over input statistics. As in Payne et al., 2021, we define place cells in the model as cells with at least one statistically significant place field under permutation tests. Under most of the parameter range, all RNN-S neurons would be identified as a place cell (Figure 6D). However, under conditions of high spatial correlation and low sparsity, a portion of neurons (12%) do not have any fields in the environment. These cells are excluded from further analysis. We measured how the size of place fields varies across the parameter range (Figure 6E). The size of the fields increases as a function of the spatial correlation of the inputs, but is relatively insensitive to sparsity. This effect can be explained as the spatial correlation of the inputs introducing an additional spatial spread in the neural activity. Similarly, we measured how the number of place fields per cell varies across the parameter range (Figure 6F). The number of fields is maximal for conditions in which input features are densely encoded and spatial correlation is low. These are conditions in which each neuron receives inputs from multiple, spatially distant states.

Finally, we wanted to identify regions of parameter space that were similar to the data of Payne et al., 2021. We measured the KL divergence between our model’s place field statistics (Figure 6DE) and the statistics measured in Payne et al., 2021 (Figure 6BC). We combined the KL divergence of both these distributions to find the parameter range in which the RNN-S best fits neural data (Figure 6G). This optimal parameter range occurs when inputs have a spatial correlation of σ≈8.75 cm and sparsity ≈0.15. We note that the split-half noise floor of the dataset of Payne et al., 2021 is a KL divergence of 0.12 bits (Figure 6—figure supplement 1E). We can visually confirm that the model fits the data well by plotting the place fields of RNN-S neurons (Figure 6H).

We wondered whether the predictive gain (γR) of the representations affects the ability of the RNN-S to fit data. The KL divergence changes only slightly as a function of γR. Mainly, the KL-divergence of the place field size increases as γR increases (Figure 6I), but little effect is seen in the distribution of the number of place fields per neuron (Figure 6J).

We next tested whether the neural data was better fit by representations generated by RNN-S or the FF-TD model. Across all parameters of the input features, despite having similar TD loss (Figure 5DE), the FF-TD model has much higher divergence from neural data (Figure 6GI, Figure 6—figure supplement 1), similar to a random feedforward network (Figure 6—figure supplement 1E).

Overall, our RNN-S model seems to strike a balance between performance in estimating successor features, similarity to data, and biological plausibility. Furthermore, our analyses provide a prediction of the input structure into the hippocampus that is otherwise not evident in an algorithmic description or in a model that only considers one-hot feature encodings.

Code is posted on Github: https://github.com/chingf/sr-project; Fang, 2022.

We simulated random walks in 1D (circular track) and 2D (square) arenas. In 1D simulations, we varied the probability of staying in the current state and transitioning forwards or backwards to test different types of biases on top of a purely random walk. In 2D simulations, the probabilities of each possible action were equal. In our simulations, one timestep corresponds to 1/3 second and spatial bins are assumed to be 5 cm apart. This speed of movement (15 cm/s) was chosen to be consistent with previous experiments. In theory, one can imagine different choices of timestep size to access different time horizons of prediction– that is, the choice of timestep interacts with the choice of γ in determining the prediction horizon.

This section provides details and pseudocode of the RNN-S simulation. Below are explanations of the most relevant variables:

The RNN-S algorithm is as follows:

We introduce additional kernel-related variables to the RNN-S model above that are optimized by an evolutionary algorithm (see following methods subsection for more details):

We also define the variable tk=20, which is the length of the temporal support for the plasticity kernel. The value of t_k was chosen such that e-tk/τ was negligibly small for the range of τ we were interested in. The update algorithm is the same as in Algorithm 1, except lines 15-16 are replaced with the following:

To learn parameters of the RNN-S model, we use covariance matrix adaptation evolution strategy (CMA-ES) to learn the parameters of the plasticity rule. The training data provided are walks simulated from a random distribution of 1D walks. Walks varied in the number of states, the transition statistics, and the number of timesteps simulated. The loss function was the mean-squared error (MSE) loss between the RNN J matrix and the ideal estimated T matrix at the end of the walk.

For the RNN-S model with tm⁢a⁢x recurrent steps, lines 10 and 13 in Algorithm 1 is replaced with M⊺←∑t=0tm⁢a⁢xγt⁢Jt.

For RNN-S with nonlinear dynamics, there is no closed form solution. So, we select a value for tm⁢a⁢x and replace lines 10 and 13 in Algorithm 1 with an iterative update for tm⁢a⁢x steps: Δx=−x+γJtanh(x′)+ϕ. We choose tm⁢a⁢x such that γmaxt<10−4.

We use a tanh nonlinearity as described above. For simplicity, we set γB=0.

As in Algorithm 1, but with the following in place of line 16(8)ΔJij←xi(t)xj(t−1)−Jijxj(t−1)2

In all simulations of the FF-TD model, we use the temporal difference update. We perform a small grid search over the learning rate η to minimize error (for SR, this is the MSE between the true M and estimated M; for successor features, this is the temporal difference error). In the one-hot SR case, the temporal difference update given an observed transition from state s to state s′ is:(9)ΔMji={γMs′i−Msiif s=j≠i1+γMs′i−Msiif s=j=i0otherwise

for all synapses j→i. Given arbitrarily structured inputs (as in the successor feature case), the temporal difference update is:(10)ΔM⊺=η(ϕ+γMϕ′−Mϕ)ϕ⊺

or, equivalently,(11)ΔMji=η(ϕi+γ∑kMkiϕk′−∑kMkiϕk)ϕj

For a walk with N states, we created N-dimensional feature vectors for each state. We choose an initial sparsity probability p and create feature vectors as random binary vectors with probability p of being ‘on’. The feature vectors were then blurred by a 2D Gaussian filter with variance σ with 1 standard deviation of support. The blurred features were then min-subtracted and max-normalized. The sparsity of each feature vector was calculated as the L1 norm divided by N. The sparsity s of the dataset then was the median of all the sparsity values computed from the feature vectors. To vary the spatial correlation of the dataset we need only vary σ. To vary the sparsity s of the dataset we need to vary p, then measure the final s after blurring with σ. Note that, at large σ, the lowest sparsity values in our parameter sweep were not possible to achieve.

We use the standard TD loss function (Equation 18). To measure TD loss, at the end of the walk we take a random sample of observed transition pairs (ϕ,ϕ′). We use these transitions as the dataset to evaluate the loss function.

We use the open source dataset from Payne et al., 2021. We select for excitatory cells in the anterior tip of the hippocampus. We then select for place cells using standard measures (significantly place-modulated and stable over the course of the experiment).

We determined place field boundaries with a permutation test as in Payne et al., 2021. We then calculated the number of fields per neuron and the field size as in Henriksen et al., 2010. The same analyses were conducted for simulated neural data from the RNN-S and FF-TD models.

We use behavioral tracking data from Payne et al., 2021. For each simulation, we randomly select an experiment and randomly sample a 28-min window from that experiment. If the arena coverage is less than 85% during the window, we redo the sampling until the coverage requirement is satisfied. We then downsample the behavioral data so that the frame rate is the same as our simulation (3 FPS). Then, we divide the arena into a 14×14 grid. We discretize the continuous X/Y location data into these states. This sequence of states makes up the behavioral transitions that the model simulates.

From the models, we get the activity of each model neuron over time. We make firing field plots with the same smoothing parameters as Payne et al., 2021.

Systemic discriminatory practices have been identified in neuroscience citations, and a ‘citation diversity statement’ has been proposed as an intervention (Dworkin et al., 2020; Zurn et al., 2020). There is evidence that quantifying discriminatory practices can lead to systemic improvements in academic settings (Hopkins, 2002). Many forms of discrimination could lead to a paper being under-cited, for example authors being less widely known or less respected due to discrimination related to gender, race, sexuality, disability status, or socioeconomic background. We manually estimated the number of male and female first and last authors that we cited, acknowledging that this quantification ignores many known forms of discrimination, and fails to account for nonbinary/intersex/trans folks. In our citations, first-last author pairs were 62% male-male, 19% female-male, 9% male-female, and 10% female-female, somewhat similar to base rates in our field (biaswatchneuro.com). To familiarize ourselves with the literature, we used databases intended to counteract discrimination (blackinneuro.com, anneslist.net, connectedpapers.com). The process of making this statement improved our paper, and encouraged us to adopt less biased practices in selecting what papers to read and cite in the future. We were somewhat surprised and disappointed at how low the number of female authors were, despite being a female-female team ourselves. Citation practices alone are not enough to correct the power imbalances endemic in academic practice National Academies of Sciences, 2018 — this requires corrections to how concrete power and resources are distributed.