10 research outputs found
Task adaption by biologically inspired stochastic comodulation
Brain representations must strike a balance between generalizability and
adaptability. Neural codes capture general statistical regularities in the
world, while dynamically adjusting to reflect current goals. One aspect of this
adaptation is stochastically co-modulating neurons' gains based on their task
relevance. These fluctuations then propagate downstream to guide
decision-making. Here, we test the computational viability of such a scheme in
the context of multi-task learning. We show that fine-tuning convolutional
networks by stochastic gain modulation improves on deterministic gain
modulation, achieving state-of-the-art results on the CelebA dataset. To better
understand the mechanisms supporting this improvement, we explore how
fine-tuning performance is affected by architecture using Cifar-100. Overall,
our results suggest that stochastic comodulation can enhance learning
efficiency and performance in multi-task learning, without additional learnable
parameters. This offers a promising new direction for developing more flexible
and robust intelligent systems
Neuromatch Academy: a 3-week, online summer school in computational neuroscience
Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
Awake hippocampal reactivations project onto orthogonal neuronal assemblies
International audienceThe chained activation of neuronal assemblies is thought to support major cognitive processes,including memory. In the hippocampus, this is observed during population bursts oftenassociated with sharp-wave ripples, in the form of an ordered reactivation of neurons. However,the organization and lifetime of these assemblies remain unknown.We used calcium imagingto map patterns of synchronous neuronal activation in the CA1 region of awakemice during runson a treadmill.The patterns were composed of the recurring activation of anatomicallyintermingled, but functionally orthogonal, assemblies.These assemblies reactivated discretetemporal segments of neuronal sequences observed during runs and could be stable acrossconsecutive days. A binding of these assemblies into longer chains revealed temporallyordered replay
Targeted V1 comodulation supports task-adaptive sensory decisions
Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing
Internal representation of hippocampal neuronal population spans a time-distance continuum
International audienceThe hippocampus plays a critical role in episodic memory: the sequential representation of visited places and experienced events. This function is mirrored by hippocampal activity that self organizes into sequences of neuronal activation that integrate spatio-temporal information. What are the underlying mechanisms of such integration is still unknown. Single cell activity was recently shown to combine time and distance information; however, it remains unknown whether a degree of tuning between space and time can be defined at the network level. Here, combining daily calcium imaging of CA1 sequence dynamics in running head-fixed mice and network modeling, we show that CA1 network activity tends to represent a specific combination of space and time at any given moment, and that the degree of tuning can shift within a continuum from one day to the next. Our computational model shows that this shift in tuning can happen under the control of the external drive power. We propose that extrinsic global inputs shape the nature of spatio-temporal integration in the hippocampus at the population level depending on the task at hand, a hypothesis which may guide future experimental studies
Internal representation of hippocampal neuronal population spans a time-distance continuum
Internal representation of hippocampal neuronal population span a time-distance continuum
Calcium imaging data in the pyramidal cell layer of hippocampal region CA1 of a mouse running on a nonmotorized uncued treadmill.<div>200s samples of 1400s imaging sessions performed on the same mouse on two consecutive days.</div><div>Resolution : 200x200px</div><div>Frequency : 10Hz</div><div>Field-of-view : 400x400µm</div