César Rennó-Costa

The Mechanism of Rate Remapping in the Dentate Gyrus

Rate remapping is a recently revealed neural code in which sensory information modulates the firing rate of hippocampal place cells. The mechanism underlying rate remapping is unknown. Its characteristic modulation, however, must arise from the interaction of the two major inputs to the hippocampus, the medial entorhinal cortex (MEC), in which grid cells represent the spatial position of the rat, and the lateral entorhinal cortex (LEC), in which cells represent the sensory properties of the environment. We have used computational methods to elucidate the mechanism by which this interaction produces rate remapping. We show that the convergence of LEC and MEC inputs, in conjunction with a competitive network process mediated by feedback inhibition, can account quantitatively for this phenomenon. The same principle accounts for why different place fields of the same cell vary independently as sensory information is altered. Our results show that rate remapping can be explained in terms of known mechanisms.

Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function

Much has been learned about the hippocampal/entorhinal system, but an overview of how its parts work in an integrated way is lacking. One question regards the function of entorhinal grid cells. We propose here that their fundamental function is to provide a coordinate system for producing mind-travel in the hippocampus, a process that accesses associations with upcoming positions. We further propose that mind-travel occurs during the second half of each theta cycle. By contrast, the first half of each theta cycle is devoted to computing current position using sensory information from the lateral entorhinal cortex (LEC) and path integration information from the medial entorhinal cortex (MEC). This model explains why MEC lesions can abolish hippocampal phase precession but not place fields.

A Signature of Attractor Dynamics in the CA3 Region of the Hippocampus

The notion of attractor networks is the leading hypothesis for how associative memories are stored and recalled. A defining anatomical feature of such networks is excitatory recurrent connections. These “attract” the firing pattern of the network to a stored pattern, even when the external input is incomplete (pattern completion). The CA3 region of the hippocampus has been postulated to be such an attractor network; however, the experimental evidence has been ambiguous, leading to the suggestion that CA3 is not an attractor network. In order to resolve this controversy and to better understand how CA3 functions, we simulated CA3 and its input structures. In our simulation, we could reproduce critical experimental results and establish the criteria for identifying attractor properties. Notably, under conditions in which there is continuous input, the output should be “attracted” to a stored pattern. However, contrary to previous expectations, as a pattern is gradually “morphed” from one stored pattern to another, a sharp transition between output patterns is not expected. The observed firing patterns of CA3 meet these criteria and can be quantitatively accounted for by our model. Notably, as morphing proceeds, the activity pattern in the dentate gyrus changes; in contrast, the activity pattern in the downstream CA3 network is attracted to a stored pattern and thus undergoes little change. We furthermore show that other aspects of the observed firing patterns can be explained by learning that occurs during behavioral testing. The CA3 thus displays both the learning and recall signatures of an attractor network. These observations, taken together with existing anatomical and behavioral evidence, make the strong case that CA3 constructs associative memories based on attractor dynamics.

Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.

Distributed Adaptive Control: A Proposal on the Neuronal Organization of Adaptive Goal Oriented Behavior

In behavioral motor coordination and interaction it is a fundamental challenge how an agent can learn to perceive and act in unknown and dynamic environments. At present, it is not clear how an agent can – without any explicitly predefined knowledge – acquire internal representations of the world while interacting with the environment. To meet this challenge, we propose a biologically based cognitive architecture called Distributed Adaptive Control (DAC). DAC is organized in three different, tightly coupled, layers of control: reactive, adaptive and contextual. DAC based systems are self-contained and fully grounded, meaning that they autonomously generate representations of their primary sensory inputs, hence bootstrapping their behavior form simple to advance interactions. Following this approach, we have previously identified a novel environmentally mediated feedback loop in the organization of perception and behavior, i.e. behavioral feedback. Additionally, we could demonstrated that the dynamics of the memory structure of DAC, acquired during a foraging task, are equivalent to a Bayesian description of foraging. In this chapter we present DAC in a concise form and show how it is allowing us to extend the different subsystems to more biophysical detailed models. These further developments of the DAC architecture, not only allow to better understand the biological systems, but moreover advance DACs behavioral capabilities and generality.

The encoding of complex visual stimuli by a canonical model of the primary visual cortex: Temporal population code for face recognition on the iCub robot

The connectivity of the cerebral cortex is characterized by dense local and sparse long-range connectivity. It has been proposed that this connection topology provides a rapid and robust transformation of spatial stimulus information into a temporal population code (TPC). TPC is a canonical model of cortical computation whose topological requirements are independent of the properties of the input stimuli and, therefore, can be generalized to the processing requirements of all cortical areas. Here we propose a real time implementation of TPC for classifying faces, a complex natural stimuli that mammals are constantly confronted with. The model consists of a network comprising a primary visual cortex V1 network of laterally connected integrate-and-fire neurons implemented in the humanoid robot platform iCub. The experiment was performed using human faces presented to the robot under different angles and position of light incidence. We show that the TPC-based model can recognize faces with a correct ratio of 97 % without any face-specific strategy. Additionally, the speed of encoding is coherent with the mammalian visual system suggesting that the representation of natural static visual stimulus is generated based on the combined temporal dynamics of multiple neuron populations. Our results provides that, without any input dependent wiring, TPC can be efficiently used for encoding local features in a high complexity task such as face recognition.

A wavelet based neural model to optimize and read out a temporal population code

It has been proposed that the dense excitatory local connectivity of the neo-cortex plays a specific role in the transformation of spatial stimulus information into a temporal representation or a temporal population code (TPC). TPC provides for a rapid, robust, and high-capacity encoding of salient stimulus features with respect to position, rotation, and distortion. The TPC hypothesis gives a functional interpretation to a core feature of the cortical anatomy: its dense local and sparse long-range connectivity. Thus far, the question of how the TPC encoding can be decoded in downstream areas has not been addressed. Here, we present a neural circuit that decodes the spectral properties of the TPC using a biologically plausible implementation of a Haar transform. We perform a systematic investigation of our model in a recognition task using a standardized stimulus set. We consider alternative implementations using either regular spiking or bursting neurons and a range of spectral bands. Our results show that our wavelet readout circuit provides for the robust decoding of the TPC and further compresses the code without loosing speed or quality of decoding. We show that in the TPC signal the relevant stimulus information is present in the frequencies around 100 Hz. Our results show that the TPC is constructed around a small number of coding components that can be well decoded by wavelet coefficients in a neuronal implementation. The solution to the TPC decoding problem proposed here suggests that cortical processing streams might well consist of sequential operations where spatio-temporal transformations at lower levels forming a compact stimulus encoding using TPC that are subsequently decoded back to a spatial representation using wavelet transforms. In addition, the results presented here show that different properties of the stimulus might be transmitted to further processing stages using different frequency components that are captured by appropriately tuned wavelet-based decoders.

Place and grid cells in a loop: implications for memory function and spatial coding

Place cells in the hippocampus and grid cells in the medial entorhinal cortex have different codes for space. However, how one code relates to the other is ill understood. Based on the anatomy of the entorhinal-hippocampal circuitry, we constructed a model of place and grid cells organized in a loop to investigate their mutual influence in the establishment of their codes for space. Using computer simulations, we first replicated experiments in rats that measured place and grid cell activity in different environments, and then assessed which features of the model account for different phenomena observed in neurophysiological data, such as pattern completion and pattern separation, global and rate remapping of place cells, and realignment of grid cells. We found that (1) the interaction between grid and place cells converges quickly; (2) the spatial code of place cells does not require, but is altered by, grid cell input; (3) plasticity in sensory inputs to place cells is key for pattern completion but not pattern separation; (4) grid realignment can be explained in terms of place cell remapping as opposed to the other way around; (5) the switch between global and rate remapping is self-organized; and (6) grid cell input to place cells helps stabilize their code under noisy and/or inconsistent sensory input. We conclude that the hippocampus-entorhinal circuit uses the mutual interaction of place and grid cells to encode the surrounding environment and propose a theory on how such interdependence underlies the formation and use of the cognitive map. SIGNIFICANCE STATEMENT The mammalian brain implements a positional system with two key pieces: place and grid cells. To gain insight into the dynamics of place and grid cell interaction, we built a computational model with the two cell types organized in a loop. The proposed model accounts for differences in how place and grid cells represent different environments and provides a new interpretation in which place and grid cells mutually interact to form a coupled code for space.

Integrating neuroscience-based models towards an autonomous biomimetic Synthetic Forager

Foraging can be described as goal-oriented exploration for resources. It exemplifies how animals coordinate complex sensory and effector systems under varying environmental conditions. To emulate the foraging capabilities of natural systems is a major goal for robotics. Therefore, foraging is an excellent paradigm to benchmark novel autonomous control strategies. Here we describe the biomimetic control architecture of the Synthetic Forager (SF), an effort to integrate multiple biologically constrained models of specific perceptual and cognitive processes pertaining to foraging into one general autonomous robot controller. This proposal is built upon the well-established Distributed Adaptive Control (DAC) framework and brings together neuroscience-based models of decision-making, multi-modal sensory processing, localization and mapping and allostatic behavioral control. To show the potential of the SF model we used it to control a high-mobility wheeled robotic platform in three behavioral tasks similar to experimental protocols applied to rodents. We show that the robot can reliably perform cue detection, rule learning and goal-oriented navigation in open environments. We propose that this approach to robotics allows both the study of embodied neuroscience models and the transfer of brain based principles to robotic systems.

A Framework for Mobile Robot Navigation Using a Temporal Population Code

Recently, we have proposed that the dense local and sparse long-range connectivity of the visual cortex accounts for the rapid and robust transformation of visual stimulus information into a temporal population code, or TPC. In this paper, we combine the canonical cortical computational principle of the TPC model with two other systems: an attention system and a hippocampus model. We evaluate whether the TPC encoding strategy can be efficiently used to generate a spatial representation of the environment. We benchmark our architecture using stimulus input from a real-world environment. We show that the mean correlation of the TPC representation in two different positions of the environment has a direct relationship with the distance between these locations. Furthermore, we show that this representation can lead to the formation of place cells. Our results suggest that TPC can be efficiently used in a high complexity task such as robot navigation.